The mission control of a fixed-wings UAV is slightly more complex compared to other models like quad-rotors or unicycles since the dynamic of a fixed-wing aircraft does not allow rapid direction changes or stop-rotate-go type of movements. Those restriction needs to be taken into consideration when building a mission control system, moreover, the problem increases in complexity when the goal is to fly in formation with several UAVs, indeed during its development, we have to handle also the scalability of the communication link and the robustness of the formation during harsh weather conditions and disturbances. In this research we propose a solution based on a vector field This vector field is computed based on the leader attitude and the followers positions. From the vector field, the follower can calculate the heading and the speed that it has to keep in order to reach the formation. To reach this solution, we first defined a desired heading and ground speed, then, by defining the goals of the UAV and the dynamic of the UAV we found the commanded heading and the commanded ground speed for each follower. The stability of the result is checked by dividing the convergence problem into two smaller problems and applying the Lyapunov stability theory. The solution is fully implemented using state of the art technology in embedded and autopilots for UAV. The system was physically simulated with ROS in Gazebo. At the end, the performances are compared to another state of the art solution, in which VFF outperformed in most of the cases

UnmannedAerial Vehicles(UAVs) have multi-domain applications and fixed-wing UAVs are a widely used class. There is ongoing research on topics in view to optimize the control and guidance of UAVs. This work explores the design, implementation and Software-in-the-Loop validation of an autopilot using adaptive guidance laws with emphasis on formation control of multiple fixed-wing UAVs. The work is done on Raspberry Pis in C++ which can be interfaced to standard autopilots as companion computers. The work splits a mission given by the user into primitive missions and uses an adaptive vector field approach for following it. For formation control, the work implements a discretized version of the Model Reference Adaptive Control synchronisation laws for multi-agent systems. Simulations are done in a distributed setting with a server program designed for the purpose. The server program handles the user inputs and configurations of the UAVs.

The work presented in this report focuses on longitudinal control of heterogeneous platoon. Firstly, merging maneuver is looked upon, considering heterogeneity in the vehicles i.e. considering different dynamics for the vehicles involved. Then heterogeneity in platoon, arising from the difference in potential limits of the vehicles is taken into account, which the current literature fails to address. The uncertain/heterogeneous dynamics are addressed through the difference in the engine driveline constant, which differs for different vehicles and keeps on changing during operation. The heterogeneity is tackled by using Adaptive control, which adapts to the changing dynamics and estimates the uncertainty to act accordingly. Adaptive strategies for formation keeping in platoons of automated vehicles have been recently proposed; while these strategies are able to cope with uncertain vehicle parameters, they have the drawback of handling only acyclic graphs (e.g. look-ahead topology). This prevents from enhancing formation keeping protocols with more complex platooning maneuvers such as synchronized merging/splitting. This work proposes an adaptive strategy for performing synchronized merging maneuvers in the presence of uncertain vehicle parameters: during these maneuvers, a cyclic communication graph is instantiated, which must be handled in a suitable way. The strategy is framed as a synchronization protocol with a set of adaptive control laws, designed via Lyapunov stability theory. A benchmark scenario in which two platoons formed in different lanes are required to merge, (e.g. due to a lane closure because of roadworks) is presented to show the effectiveness of the proposed strategy. The approach is proven to be scalable to platoons with arbitrary number of vehicles. In later part of the work, a method is proposed which takes the limits of the vehicles into consideration, to make the platooning realistic. An adaptive platooning strategy is used so as the vehicles can adaptively synchronize to desired reference dynamics. In order to handle engine limits, a mechanism is proposed based on making the reference dynamics `not too demanding', by properly saturating their control action. Such saturation action will allow all vehicles in the platoon to remain in their engine potential limits throughout. Another problem that affects the stability of the platoon is that, any perturbations or disturbance in the platoon leaves the vehicle scattered if the vehicles are already running at their potential limits. This work proposes an adaptive platooning method with bi-directional interaction and a mechanism coping with engine constraints. The bi-directional interaction allows the vehicle to not only look at the error in spacing from the preceding vehicle (vehicle in front) but also the succeeding vehicle, this makes the vehicle's control action (acceleration) depend on both front and back errors, the vehicle can thus slow down for the following vehicle if it lags behind. The bi-directional strategy developed is then proved string stable, which the literature struggles to establish with the already developed bi-directional strategies. Simulations are then conducted to validate the theoretical analysis and show the effectiveness of the method in retaining cohesiveness of the platoon. The simulations include a merging maneuver to check the effectiveness of merging with synchronized cyclic communication. For the engine potential limits, the firstly state of the art algorithm's simulation is run which is then compared to the performance of the proposed algorithm. Finally, to check the effectiveness of bi-directional CACC strategy simulations for a specific case is run for both uni-directional and bi-directional strategies proposed in the thesis and are then compared, showing the advantage of using bi-directional CACC strategy over uni-directional.

Traditional centralized power plants have limited ability to adapt to the varying power demands caused due to the increasing deployment of renewable energy sources. For power grids, willing to increase the use of renewable energy and thereby decrease the energy bills, demand side energy management could act as an effective solution. Demand side energy management of the power grid refers to the process of regulating the power demands of the devices it serves. A large fraction of this power demand on the grid lines is caused due to Thermostatically Controlled Loads such as residential refrigerators, electric water heaters, air conditioners, industrial heaters, ovens, etc. Traditionally, the energy management of these devices is achieved using model predictive control and linear quadratic regulation. To better handle the system heterogeneity and computation costs, model-free adaptive control algorithms are explored.

This thesis provides a theoretical framework for the arise of stop-and-go waves in platoons of Human-driven Vehicles (HVs) on a ring road topology. Possibilities and limitations for traffic control and suppression of such waves via a single Autonomous Vehicle (AV) are considered. Being HVs prone to show string instability, i.e. a type of instability caused by a disturbance acting on one of the vehicles and which is amplified throughout the platoon, the analysis starts from the observation that the standard notion of string stability on a ring road is too demanding for a mixed (human and automated) traffic scenario. Thus, after providing a new definition of string stability on a ring roadway, two directions are pursued. First, only HVs on a ring roadway are considered and their stability/instability analyzed. Second, a mixed-platoon with a single AV and the remaining HVs is placed on the ring and the AV is designed in order to ensure equilibrium stability. By means of this investigation, possibilities and limitations of traffic control via a single AV are emphasized.

Formation flying is a phenomenon observed very often in the natural world, e.g. birds flying in a flock. The past decade has given a lot of emphasis on the research for the control of autonomous Unmanned Aerial Vehicles (UAVs) of the fixed-wing kind, in an effort to emulate the behavior of natural flocks. Emulating this behavior requires the construction of path following and formation control laws with the capability of adapting to changing situations, in a similar way as natural flocks can do. This thesis is devoted to studying Adaptive Vector Field Guidance laws and Adaptive Formation laws for fixed-wing UAVs. Formation control relies on an adaptive hierarchical formation control method for uncertain heterogeneous nonlinear agents with Euler-Lagrange (EL) dynamics. It is shown that various formations (T-V-Y formations) can be established using this method, tested using a Matlab/Simulink environment. Additionally, a distinguishing feature of this thesis is the development of a 3D-Simulation platform to perform a hardware in the loop (HITL) simulations (i.e. using the control hardware on board of an actual UAV): a Raspberry Pi is used to run the formation control algorithm and to communicate with a Pixhawk Cube autopilot board which contains the low-level control algorithm. The autopilot board is then connected to a 3D Simulator (Gazebo) and Ground Control System (QGroundControl). The proposed HITL platform promises to facilitate the testing and validation of guidance and formation laws in a much more realistic way than a Matlab/Simulink environment can do.

A Ball Balancing Robot (BBR) is a highly agile machine which is an unstable system. To stabilize, it uses three omni wheels connected to motors as an input of the system. Since it has less inputs than outputs, the BBR is an underactuated system. Due to the underactuated character and the non-minimum phase plant, the BBR is an interesting mechanism to study the effectiveness of control design.

An adaptive controller will be designed for the BBR such that it can cope with a changing environment, for example added mass to the robot or change of the surface it rides on. Due to the non-minimum phase characteristic of the plant, an Adaptive Pole Placement Controller (APPC) is the most suitable adaptive controller for the BBR.

In order to study which adaptive law in combination with the APPC gives the best performance simulations are done. For these simulations a 2D planar model is derived, which is compared and verified with the actual system.

The cooperative output regulation problem has attracted considerable attention due to its wide applications in several real life problems; in the past decade, the cooperative control of multi-agent systems has become a trendy topic in the control community.
In the cooperative output regulation problem the main purpose consists in achieving reference tracking and disturbance rejection.
State-of-the-art methodologies can solve cooperative output regulation only by assuming some critical a priori knowledge: either the exosystem dynamics (e.g. their harmonic frequencies) can be globally shared; or some structural parameters of the communication graph (e.g. structural eigenvalues) are known; or initial stabilizing controllers are available for each system.
However, from a practical point of view, it is crucial to develop adaptive methodologies to effectively handle uncertainty in cooperative control of network systems, exploiting as little a priori information as possible.
This work addresses and solves the cooperative output regulation problem without exploiting any of the aforementioned critical knowledge.
In fact, the distinguishing feature of the proposed solution is to assume an uncertain cooperative scenario where neither follower nor leader dynamics are globally known. In particular, the exosystem dynamics are assumed to correspond to harmonic oscillators with unknown frequencies.
Cooperative output regulation is achieved by designing, for each system in the network, fully distributed adaptive controllers, i.e. requiring no knowledge of the structural eigenvalues nor initial stabilizing control law.

Synchronization of Multi-Agent Systems (MASs) has the potential to benefit many technological
areas such as formation control for unmanned vehicles, cooperative adaptive
cruise control, and spacecraft attitude control. Information plays a crucial role in MASs:
in centralized approaches, a central node utilizes global information to achieve synchronization,
while in distributed approach agents only utilize local information, i.e. neighbors’
information. A big concern in MASs is the presence of parametric uncertainties
(unknown dynamics), which might require adaptive control gains instead of fixed control
gains.

This work thus provides a novel adaptive distributed control for MASs of heterogeneous
agents with unknown dynamics based on model reference adaptive control (MRAC).
We study both the synchronization of linear systems and the synchronization of Euler-
Lagrange (EL) systems. The implementation of this scheme is based on distributed
matching condition assumptions. We study such matching conditions both for the statefeedback
case and output-feedback case. Since all matching gains are unknown in view
of the unknown dynamics, the gains are adapted online via Lyapunov-based estimation.
The asymptotic convergence of the synchronization error is analytically proven
by introducing an appropriately defined Lyapunov function, and numerical examples
show the effectiveness of the approach. The practical advantage of the proposed distributed
MRAC is the possibility of handling unknown dynamics by simply exchanging
the states/output, and inputs with neighbors, without any extra auxiliary variables (distributed
observer) nor sliding mode. Because of the mutual dependence of control inputs,
well-posedness problems will arise in the presence of cyclic communication, if the inputs
are generated without a prescribed priority. In this work, we study such well-posedness
problems via parameter projection methods.

A Plug-in Hybrid Electric Vehicle (PHEV) can achieve a considerably higher overall fuel economy than conventional vehicles. The fuel economy of PHEVs however, strongly depends on the supervisory control strategy of the hybrid powertrain. Compared to conventional, non-predictive, supervisory control systems, predictive control methods can further improve the fuel economy by anticipating on future driving conditions. This thesis presents a predictive optimal control method for PHEVs that minimizes the fuel consumption in real-time operation, based on the preview of the future topographic profile and velocity profile. This strategy is realized by a global estimation method that determines the optimal battery depletion strategy on the global range, which is tracked by a novel adaptive heuristic control method that optimizes the driving mode and power distribution between the two power sources on the local range. Compared to state of the art predictive control methods, this control method makes it possible to include feasibility and drivability conditions (such as the number of engine starts and variation of the torque demand) in the control strategy, while allowing high update frequencies of the control actions.

The proposed control method is fine-tuned on a P2 parallel PHEV model based on a Volkswagen Jetta HEV, and its effectiveness is evaluated by implementation in a high fidelity simulation environment. It was found that route-preview based control strategies can obtain substantial improvements of up to 7.3% in fuel economy compared to a non-predictive heuristic control method. Simulation results also demonstrate that the application of optimization based control methods without any drivability considerations (such as A-ECMS) often leads to unacceptable control policies. The proposed control method on the other hand, was able to take feasibility limitations into account, which led to a reduction of the number of combustion engine starts of up to 52%.

In the past years a number of SAE level 2 driving automation systems have come available in commercial vehicles. An example is Volvo's Pilot Assist, which provides a more comfortable journey for the driver as certain parts of the dynamic driving task are taken away, but it still requires the driver to be attentive in order to supervise the system and resume vehicle operation if necessary. Car manufacturers and high-tech companies all around the world are working on the development of an Automated Driving System (ADS) of level 3 or higher where the driver does not need to supervise, but have so far not been able to introduce any to the public. Operating a vehicle on an empty highway is relatively easy, but roads are getting more and more congested so dense traffic driving needs to be accounted for as well.

Unsupervised driving does not only require that a system itself operates the vehicle correctly, but requires that it is able to cope with mistakes of other drivers. In order to take these into account as well as other unpredictable events, current driving automation systems tend to adopt large longitudinal margins with respect to surrounding traffic to use their ability to brake to avoid a collision. In dense traffic situations this conservative behaviour prohibits the ADS from making lane changes.

This thesis was setup in cooperation with Volvo Cars / Zenuity to develop a lane change path planning algorithm that can plan active lane changes in dense traffic, by pushing into a gap without compromising the ability to avoid a possible collision.

Earlier work at Volvo suggested considering the ability of a vehicle to not only brake, but also to make an evasive maneuver. If an evasion maneuver is available the necessary safety margins are reduced considerably compared to a `braking-only' scenario.

In this work, the evasive maneuver was modeled and a constraint formulation was developed that if met, guarantees the availability of the designed evasion maneuver. This constraint translates to a state-dependent safety zone which the ADS should keep the vehicle clear off at all times.

Model Predictive Control allows the implementation of such a safety zone by considering it as a constraint on the optimisation, guaranteeing that an evasion maneuver is available along any point on the prediction horizon. This negates the necessity to plan all possible evasion maneuvers separately, and it was therefore chosen as the path planning method.

Implementation of the safety zone constraint on multiple surrounding vehicles, spread over multiple lanes in a Model Predictive Control framework, together with the requirements of being able to overtake other vehicles within the prediction horizon, necessitated the development of a novel 3-step algorithm. In this algorithm three Optimal Control Problems are solved consecutively to plan a path which positions the ego-vehicle optimally in the targeted gap without breaching the safety zones.

To be able to implement the algorithm in real-time, a computationally expedient vehicle model is chosen and the OCP's are solved using state-of-the-art structure exploiting Interior Point solvers, generated as efficient C-code using FORCES Pro.

With this setup, computation times of $\sim$30[ms] are achieved for a 10[s] prediction horizon sampled at 100[ms]. The algorithm is demonstrated in simulation and shown to be able to find the point of maximum lateral intrusion, to operate on the edge of the safety zone constraint, and to avoid a collision when it is being cut off from behind or when the vehicle in front comes to a sudden stop.

The results show that vehicle operation by an ADS is possible in dense traffic without compromising safety. By using the developed safety zones and novelty solution framework the necessary margins can be reduced, increasing the availability of the lane change maneuver. Furthermore the control strategies necessary for implementation are shown to be applicable in real-time, paving the way for implementation in real vehicles. This way the research contributes to the next step in developing a usable Automated Driving System.

A challenging task in synchronization of multi-agent systems is steering the network towards a coherent solution when the dynamics of the constituent systems are heterogeneous and residing in a possibly large uncertainty set. In this situation, synchronization can be achieved via adaptive protocols (with adaptive feedback gains, or adaptive coupling gains, or both). However, as state-of-the-art synchronization methods adopt a distributed observer architecture, they require to communicate extra local observer variables among neighbors, in addition to the neighbors' states (or outputs). The distinguishing feature of this work is to show that synchronization in heterogeneous and uncertain multi-agent systems is possible without the need for any distributed observer. This can be achieved by designing adaptive distributed synchronization protocols, based on homogenization reasoning. Specifically, ideal gains are defined (feedback and coupling gains) that could lead all the heterogeneous agents to a desired homogeneous behavior and thus synchronization. However, since these gains are unknown in view of the unknown dynamics, we design adaptive laws for these gains that lead the agents toward synchronization. In this thesis, different control protocols are designed to address both leaderless and leader-follower synchronization of linear multi-agent systems. The protocols are then extended to achieve synchronization over a special class of nonlinear multi-agent systems, whose units are modeled as Kuramoto-like systems. Throughout this work, convergence of the synchronization error to zero is shown via Lyapunov analysis, and numerical examples demonstrate the effectiveness of the proposed protocols.

Over the past century, cars have become a fundamental part of our society. With the increasingly larger number of vehicles on the road, safety is, now more than ever, a topic of paramount importance. It is estimated that every year 26,000 people are killed on European roads, corresponding to a social cost of 100 billion euros. In the last two decades, awareness about this issue has however increased substantially, and great effort is now put into improving these numbers.
The introduction of driver assistance systems in recent years has led to a significant decrease in fatal accidents. One of the first implemented ones was Anti-lock Braking System (ABS), introduced on the market in 1978. ABS is an active safety system nowadays mandated on every new car sold in Europe. By preventing wheel lock during emergency braking, the system allows for shorter braking distances and steering ability retention. Despite the fact that forty years went by since ABS introduction, the control strategy behind it has changed very little, still retaining a rule-based approach. Extensive review of literature of the subject, highlighted the possibility that significant improvements could be achieved if the control strategy was to be redesigned in a way that takes advantage of the many technological improvements achieved in the last decade.
The aim in this thesis is to verify this statement and quantify any potential improvement in Anti-lock Braking System. In order to achieve the research objective, a novel ABS algorithm was designed. The controller, based on state-of-the-art hardware, uses a model predictive control approach and potentially available wheel force information as the pillars of its design.
A supervisory activation logic is also developed to replicate full ABS functionalities and keep the driver in the loop. The controller is then tested on Toyota’s high-end vehicle simulator and benchmarked against the current industrial state of the art.
The vehicle simulator has its core in a multibody model augmented with kinematics and compliance measurements, and it is largely validated against experiments. A complex multi-physics model of the brake system, together with the short wavelength rigid ring tire model, complete the setup.
A comprehensive set of manoeuvres, including friction jumps and rough road braking scenarios, is developed to assess performance and robustness of the proposed controller. Additionally, a list of aspect-specific performance metrics is drafted and deployed to accurately investigate the simulations results and identify relative gains with respect to the benchmark in different instances of the braking manoeuvre. The analysis showed substantial reduction of the braking distance and improved steering ability in each of the simulated scenarios. Furthermore, robustness of the controller against external factors, such as high frequency noise generated by road irregularities or friction transitions, is demonstrated to be comparable to that of industrial state of the art controllers. Based on the now verified potential of the proposed controller, and a list of the future engineering steps mapping the way until an eventual controller deployment on the market is identified.

Extensive advancements in the field of mobility and safety in transportation in last few decades
have acted as a catalyst for the expansion of automobile industry. The research for the evolution
of new technologies, vehicle safety regulations and specifications and the market requirements
has given this progress a greater push. The passenger cars these days are equipped with ample
collection of active safety systems such as Anti-lock Braking Systems (ABS), Electronic Stability
Program (ESP), Active Front Steering (AFS) and many more. The amalgamation of these active
safety systems in the vehicle chassis allows the vehicle to accomplish the desired stability.
Both academia and industry understood the requirement of more research in the field of vehicle
industry. This leads to the comprehensive enhancement in the field of research and development
with respect to integrated chassis control (ICC). The new research delivered some remarkable advantages
to this industry, such as cost reduction of the complete system, multi-objective performance
growth, hardware entanglement, fault-tolerant capability and system plausibility.
Despite the huge diversity in research of integrated chassis, there are certain adaptive design methods
which can be used to enhance the ICC performance. This dissertation intends to provide a
methodology to fully exploit the nonlinear dynamics of the vehicle concerning the forces in between
tire and road surface and to discuss the integration of AFS and ESP. It establishes a novel
adaptive control approach in order to attain asymptotic tracking for switched affine vehicle models
with parameter uncertainties and disturbances acting on the model. This, therefore, will help
to attain the desired performance. Further, comparisonwith strategies that merely exploits the linear
region of the vehicle dynamics are discussed, and performance improvements of the proposed
methodology are assessed. Finally, analytically the endurance of the prospective control strategy
is proven and simulations are conducted to validate the theoretical analysis.

Automated driving, one of the rapidly growing research topics in the field of smart traffic, has proved to be a recognized solution for potentially improving road throughput and reducing vehicles' energy consumption by grouping individual vehicles into platoons controlled by one leading vehicle.

The advances in distributed inter-vehicle communication networks have stimulated a fruitful line of research in Cooperative Adaptive Cruise Control (CACC). In CACC, individual vehicles, grouped into platoons, must automatically adjust their own speed using on-board sensors and communication with the preceding vehicle so as to maintain a safe inter-vehicle distance. The importance of CACC lies in the fact that it enables small inter-vehicle time gaps, which leads to a major reduction of the aerodynamical drag force applied on vehicles in such a driving pattern. Consequently, vehicle emissions, which play a main role in trucks and heavy automobiles, are expected to be highly reduced.

However, a crucial limitation of the state-of-the-art research in this control scheme is that the string stability of the platoon can be proven only when the vehicles in the platoon have identical driveline dynamics and perfect engine performance (homogeneous platoon), and possibly an ideal communication channel.

Thus, the objective of this MSc thesis is to address the problem of CACC for heterogeneous platoons under realistic inter-vehicle network conditions. In the first part, we propose a novel CACC strategy that overcomes the homogeneity assumption and that is able to adapt its action and achieve string stability for uncertain heterogeneous platoons under ideal inter-vehicle network conditions. In the second part, in order to handle the inevitable communication losses, we formulate an extended average dwell-time framework and design an adaptive switched control strategy which activates an augmented CACC or an augmented Adaptive Cruise Control strategy depending on communication reliability.

Stability of the proposed control strategies is proven analytically and simulations are conducted to validate the theoretical analysis.

Major advancements over the last few decades in communication networks gave rise to the
new paradigm of Networked Control Systems (NCSs). Within this paradigm, sensing and
actuation signals are exchanged among various parts of a single system or among many
subsystems via communication networks. Although this enables one to perform more complex
tasks than traditional control paradigms, it comes at the cost of complicating the design phase
and the required analysis tools. One of the major challenges when considering a network is
quantization effect which affects the performance of any control laws that were designed
without taking the network effects into account.
Even if the NCS paradigm is well established, few works are available on adaptive methods for
NCSs: this MSc thesis establishes novel adaptive control approaches that attain asymptotic
tracking for linear systems and switched linear systems with parametric uncertainties, when
input measurements are quantized due to the presence of a communication network closing
the control loop. In addition to enlarging the class of systems for which the adaptive quantized
control can be solved, a hybrid control policy is applied to a novel dynamic quantizer with
dynamic offset to address the tracking problem.
The MSc thesis is split into two parts: in the first part we consider the model reference
adaptive control of a linear uncertain system, where a Lyapunov-based approach is used to
derive the adaptive adjustments for the dynamic range, the dynamic offset and the control
parameters. In the second part, the approach is extended to switched uncertain linear systems
with dwell-time switching, where a new time-varying Lyapunov-like function is adopted: it
is proven analytically that the new Lyapunov function we introduce, overcomes the need
for zooming-out at every time instant in order to compensate the possible increment of the
Lyapunov function.
The proposed quantized adaptive control schemes are applied to two benchmark examples:
an electro-hydraulic system and the piecewise linear model of the NASA GTM aircraft, respectively.

This Report covers the initial research into modeling the Scanning Electrochemical Microscopy (SECM) system for the purposes of high precision control. The SECM system is a microscope based around electro-chemical effects. It operates in several modes relating on degrees of conductivity of the involved elements. The control is to be based on the observed additional damping the system experiences in control critical conditions. The improvement of this control is necessary to improve the precision and resolution of the system as well as gaining insight in its physical function.
Experiments where conducted using an industry setup of the system. This time with the system was used gather data important for model generation as well as requiring intuition on the intricacies of using the system and what type of precision can be used.
Next, mathematical descriptions are developed for all elements of the \ac{SECM} system. First of the electrical components involved in the measurement and subsequently of the physics effecting the system. The approximations are picked with the goal of developing a state space model and maintaining a link to the physical world in its parameters. The most complex and relevant being the effect of hydrodynamic damping which is critical in the observed damping process.
It is modeled in the Lagrangian Energy approach, which captures only a part of the dynamics. Some additional factors are still missing. A departure from the homogeneous nature of the model is suggested. Experience with the system has led to increased insight into the systems behavior under various conditions and has provided a stepping stone towards more intricate future modeling attempts. While also providing inspiration for possible model free approaches.

One of the most crucial links to building an autonomous system is the task of decision making. The ability of a vehicle to make robust decisions on its own by predicting and assessing future consequences is what makes it intelligent. This task of decision making becomes even more complex due to the fact that the real world is uncertain, continuous and vehicles interact with each other. Sensors that perceive the real world and measure quantities such as position and speed of other traffic objects are inherently noisy and are further susceptible to external conditions. On the other hand, the road users’ intentions are stochastic and not measurable, and the presence of partial or complete vision based occlusions can make any measurements obtained useless. The decision making unit thus has to be aware of these issues and use the limited knowledge available to anticipate future situations that could unfold in an infinite number of ways to then maximize a reward (or minimize a cost).

In this thesis, a method to make automated longitudinal decisions along a predetermined path for autonomous vehicles in unsignalized urban scenarios is proposed. The decision mak- ing problem is formulated as a continuous Partially Observable Markov Decision Process (POMDP) with a discrete Bayesian Network estimating the behavior of other traffic objects. The future evolution of states are predicted using a multi-model Trajectory Estimator along with the digital map of the current driving area. Since continuous spaces make the belief space infinitely large, calculation of the value function for this problem becomes computa- tionally intractable. The presented solver algorithm approximates the value function instead of computing it directly, and additional optimizations reduce the belief space exploration area in order to improve performance. The results show that this single generalized framework is able to handle all the tested urban scenarios with a good safety margin in scenes with multiple traffic objects, even under zero visibility of the other traffic objects due to the presence of occlusions.

Heating, Ventilation and Air-Conditioning (HVAC) units in commercial buildings account for a huge portion of global energy consumption. There is an ever growing need to optimize the energy consumption of an HVAC system along with a system-of-subsystems entity that must be accurately integrated and controlled by the building automation system to ensure the occupants' comfort with reduced energy consumption.

To achieve these goals, it is necessary that accurate models be developed that describe the internal dynamics of the system to employ a satisfactory control architecture. This thesis work aims at provide sufficiently accurate models which are able to estimate the temperature, humidity and Carbon Dioxide dynamics in an occupied room. A simplified linear model which describes the dynamics was developed by reformulating the physical equations into a linear regression format. This was followed by the employment of a suitable identification technique to estimate the physical parameters of the system.

The second part of this thesis involves the formulation of a two level control architecture to optimize comfort and energy. In this work we propose a model-based framework to maximize the comfort of the occupants using the Predicted Mean Vote (PMV) index. In particular, the set-point control is based on a predictive controller based on a joint optimization of PMV and energy consumption; the low-level Proportional Integral HVAC controllers are autotuned based on simulations of a thermal model. A simulation based validation via a three room test case is presented: the results show the potential for good temperature tracking with a high degree of comfort while also reducing overall energy consumption.

In high-tech industry (sub)nanometre precision motion control is essential. For example wafer scanners, used for production of integrated circuits, have to provide (sub)nanometre precision positioning whilst satisfying challenging requirements on speed at the same time. It is in demanding cases like this that different requirements begin to conflict with each other. A fundamental trade-off between robustness and performance exists as an inevitable result of the waterbed effect in linear control. PID controllers, which have been an industry-standard for many years, do not satisfy the ever increasing demands.

In this MSc thesis a novel reset control synthesis method is proposed: CRONE reset control, which combines a robust fractional CRONE controller (Commande Robuste d’Ordre Non-Entier) with non-linear reset control to overcome waterbed effect. In CRONE control, robustness is achieved against gain deviations by creation of constant phase behaviour around bandwidth with the use of fractional operators. The use of fractional operators also introduces more freedom in shaping the open- loop frequency response, allowing fractional factors in Bode’s gain-phase relation. However, waterbed effect remains, which motivates introduction of non-linear reset control in the CRONE design. In reset control, controller states are reset when the error between output- and reference- signal is zero. In frequency domain, using describing function analysis it is predicted that reset filters generate less phase lag for similar gain behaviour. For instance, a reset integrator gives a phase lag of about 38 degrees, which is 52 degrees less compared to the linear integrator. This allows for relief from Bode’s gain-phase relation, breaking aforementioned trade-offs.
In the new controller design, reset phase advantage is approximated using describing function analysis and used to achieve better open-loop shape. New design rules for CRONE reset control have been developed in this thesis. Three different reset control strategies have been investigated: integrator reset, lag reset and lead- lag/lag-lead reset. For these controllers, a two-degree-of-freedom non-linearity tuning CRONE reset control structure has been developed. This control structure has been implemented digitally within a MATLAB/Simulink environment on a Lorentz-actuated (dual) precision stage via a real-time dSPACE DS1103 controller interface. For the implemented controllers sufficient quadratic stability has been shown using the Hβ-condition.

It has been shown that simulated frequency responses using describing function correspond well to experimental identified frequency responses. Moreover, frequency domain measurements have shown that better performance for CRONE reset control can be achieved for same phase margin compared to linear CRONE. Relief from both waterbed effect and Bode’s gain-phase relation has been accomplished. Furthermore, for the same bandwidth, average noise reduction between 1.79dB and 3.93dB has been attained at a number of distinct frequencies above bandwidth.
In the fine stage separately and also in dual stage configuration, tracking of a fourth-order input-shaped reference signal (with second-order- and fourth-order feedforward respectively) showed improvement in CRONE reset control compared to linear CRONE. In the dual stage configuration, after decoupling fine stage and coarse stage, tracking performance of a linear CRONE controller has been compared to a CRONE reset controller. In both cases the same linear CRONE-2 controller with a bandwidth of 80 Hz and phase margin of 50° was applied to the coarse stage. On the fine stage a CRONE-1 lag-lead controller was applied with a bandwidth of 150 Hz and a phase margin of 55°. RMS error for a triangular scanning wave with maximal acceleration of 0.25 m/s2 and maximal velocity of 75 mm/s over a stroke of 2 cm, was reduced from 929.8 nm to 443.7 nm.