128 research outputs found
Optimal control and robust estimation for ocean wave energy converters
This thesis deals with the optimal control of wave energy converters and some associated
observer design problems. The first part of the thesis will investigate model
predictive control of an ocean wave energy converter to maximize extracted power.
A generic heaving converter that can have both linear dampers and active elements
as a power take-off system is considered and an efficient optimal control algorithm
is developed for use within a receding horizon control framework. The optimal
control is also characterized analytically. A direct transcription of the optimal control
problem is also considered as a general nonlinear program. A variation of
the projected gradient optimization scheme is formulated and shown to be feasible
and computationally inexpensive compared to a standard nonlinear program solver.
Since the system model is bilinear and the cost function is not convex quadratic, the
resulting optimization problem is shown not to be a quadratic program. Results are
compared with other methods like optimal latching to demonstrate the improvement
in absorbed power under irregular sea condition simulations.
In the second part, robust estimation of the radiation forces and states inherent in
the optimal control of wave energy converters is considered. Motivated by this, low
order H∞ observer design for bilinear systems with input constraints is investigated
and numerically tractable methods for design are developed. A bilinear Luenberger
type observer is formulated and the resulting synthesis problem reformulated as that
for a linear parameter varying system. A bilinear matrix inequality problem is then
solved to find nominal and robust quadratically stable observers. The performance
of these observers is compared with that of an extended Kalman filter. The robustness
of the observers to parameter uncertainty and to variation in the radiation
subsystem model order is also investigated.
This thesis also explores the numerical integration of bilinear control systems with
zero-order hold on the control inputs. Making use of exponential integrators, exact
to high accuracy integration is proposed for such systems. New a priori bounds
are derived on the computational complexity of integrating bilinear systems with a
given error tolerance. Employing our new bounds on computational complexity, we
propose a direct exponential integrator to solve bilinear ODEs via the solution of
sparse linear systems of equations. Based on this, a novel sparse direct collocation
of bilinear systems for optimal control is proposed. These integration schemes are
also used within the indirect optimal control method discussed in the first part.Open Acces
Robust Model Predictive Control for Linear Parameter Varying Systems along with Exploration of its Application in Medical Mobile Robots
This thesis seeks to develop a robust model predictive controller (MPC) for Linear Parameter Varying (LPV) systems. LPV models based on input-output display are employed. We aim to improve robust MPC methods for LPV systems with an input-output display. This improvement will be examined from two perspectives. First, the system must be stable in conditions of uncertainty (in signal scheduling or due to disturbance) and perform well in both tracking and regulation problems. Secondly, the proposed method should be practical, i.e., it should have a reasonable computational load and not be conservative.
Firstly, an interpolation approach is utilized to minimize the conservativeness of the MPC. The controller is calculated as a linear combination of a set of offline predefined control laws. The coefficients of these offline controllers are derived from a real-time optimization problem. The control gains are determined to ensure stability and increase the terminal set.
Secondly, in order to test the system's robustness to external disturbances, a free control move was added to the control law. Also, a Recurrent Neural Network (RNN) algorithm is applied for online optimization, showing that this optimization method has better speed and accuracy than traditional algorithms. The proposed controller was compared with two methods (robust MPC and MPC with LPV model based on input-output) in reference tracking and disturbance rejection scenarios. It was shown that the proposed method works well in both parts. However, two other methods could not deal with the disturbance.
Thirdly, a support vector machine was introduced to identify the input-output LPV model to estimate the output. The estimated model was compared with the actual nonlinear system outputs, and the identification was shown to be effective. As a consequence, the controller can accurately follow the reference.
Finally, an interpolation-based MPC with free control moves is implemented for a wheeled mobile robot in a hospital setting, where an RNN solves the online optimization problem. The controller was compared with a robust MPC and MPC-LPV in reference tracking, disturbance rejection, online computational load, and region of attraction. The results indicate that our proposed method surpasses and can navigate quickly and reliably while avoiding obstacles
Distributed Model-based Control for Gas Turbine Engines
Controlling a gas turbine engine is a fascinating problem. As one of the most complex systems developed, it relies on thermodynamics, fluid mechanics, materials science as well as electrical, control and systems engineering. The evolution of gas turbine engines is marked with an increase in the number of actuators. Naturally, this increase in actuation capability has also been followed by the improvement of other technologies such as advanced high-temperature and lighter materials, improving the efficiency of the aero engines by extending their physical limits. An improvement in the way to control the engine has to be undertaken in order for these technological improvements to be fully harnessed. This starts with the selection of a novel control system architecture and is followed by the design of new control techniques. Model-based control methods relying on distributed architectures have been studied in the past for their ability to handle constraints and to provide optimal control strategies. Applying them to gas turbine engines is interesting for three main reasons. First of all, distributed control architectures provide greater modularity during the design than centralized control architectures. Secondly, they can reduce the life cycle costs linked to both the fuel burnt and the maintenance by bringing optimal control decisions. Finally, distributing the control actions can increase flight safety through improved robustness as well as fault tolerance. This thesis is concerned with the optimal selection of a distributed control system architecture that minimizes the number of subsystem to subsystem interactions. The control system architecture problem is formulated as a binary integer linear programming problem where cuts are added to remove the uncontrollable partitions obtained. Then a supervised-distributed control technique is presented whereby a supervisory agent optimizes the joint communication and system performance metrics periodically. This online optimal technique is cast as a semi-definite programming problem including a bilinear matrix equality and solved using an alternate convex search. Finally, an extension of this online optimal control technique is presented for non-linear systems modelled by linear parameter-varying models
Transition Between TS Fuzzy Models and the Associated Convex Hulls by TS Fuzzy Model Transformation
One of the primary objectives underlying the extensive 20-year development of the TS Fuzzy model transformation (originally known as TP model transformation) is to establish a framework capable of generating alternative Fuzzy rules for a given TS Fuzzy model, thereby manipulating the associated convex hull to enhance further design outcomes. The existing methods integrated into the TS Fuzzy model transformation offer limited capabilities in deriving only a few types of loose and tight convex hulls. In this article, we propose a radically new approach that enables the derivation of an infinite number of alternative Fuzzy rules and, hence, convex hulls in a systematic and tractable manner. The article encompasses the following key novelties. Firstly, we develop a Fuzzy rule interpolation method, based on the pseudo TS Fuzzy model transformation and the antecedent Fuzzy set rescheduling technique, that leads to a smooth transition between the Fuzzy rules and the corresponding convex hulls. Then, we extend the proposed concept with the antecedent Fuzzy set refinement and reinforcement technique to tackle large-scale problems characterized by a high number of inputs and Fuzzy rules. The paper also includes demonstrative examples to illustrate the theoretical key steps, and concludes with an examination of a real complex engineering problem to showcase the effectiveness and straightforward execution of the proposed convex hull manipulation approach
Commande dynamique de robots déformables basée sur un modèle numérique
This work focuses on modeling and control of soft robots. It covers the entire development of the controller, from the modeling step to the practical experimental validation.From a theoretical point a view, large-scale dynamical systems along with model reduction algorithms are studied. In addition to the theoretical studies, different experimental setups are used to illustrate the results. A cable-driven soft robot and a pressurized soft arm are used to test the control algorithms. Through these different setups, we show that the method can handle different types of actuation, different geometries and mechanical properties. This emphasizes one of the interests of the method, its genericity.Cette thèse s'intéresse à la modélisation et à la commande de robots déformables (robots dont le mouvement se fait par déformation). Nous nous intéressons à la conception de lois de contrôle en boucle fermée répondant aux besoins spécifiques du contrôle dynamique de ces robots, sans restrictions fortes sur leur géométrie. La résolution de ce défi soulève des questions théoriques qui nous amènent au deuxième objectif de cette thèse: développer de nouvelles stratégies pour étudier les systèmes de grandes dimensions
A Human Driver Model for Autonomous Lane Changing in Highways: Predictive Fuzzy Markov Game Driving Strategy
This study presents an integrated hybrid solution to mandatory lane changing problem
to deal with accident avoidance by choosing a safe gap in highway driving. To manage
this, a comprehensive treatment to a lane change active safety design is proposed from
dynamics, control, and decision making aspects.
My effort first goes on driver behaviors and relating human reasoning of threat in
driving for modeling a decision making strategy. It consists of two main parts; threat assessment
in traffic participants, (TV s) states, and decision making. The first part utilizes
an complementary threat assessment of TV s, relative to the subject vehicle, SV , by evaluating
the traffic quantities. Then I propose a decision strategy, which is based on Markov
decision processes (MDPs) that abstract the traffic environment with a set of actions, transition
probabilities, and corresponding utility rewards. Further, the interactions of the TV s
are employed to set up a real traffic condition by using game theoretic approach. The question
to be addressed here is that how an autonomous vehicle optimally interacts with the
surrounding vehicles for a gap selection so that more effective performance of the overall
traffic flow can be captured. Finding a safe gap is performed via maximizing an objective
function among several candidates. A future prediction engine thus is embedded in the
design, which simulates and seeks for a solution such that the objective function is maximized
at each time step over a horizon. The combined system therefore forms a predictive
fuzzy Markov game (FMG) since it is to perform a predictive interactive driving strategy
to avoid accidents for a given traffic environment. I show the effect of interactions in decision
making process by proposing both cooperative and non-cooperative Markov game
strategies for enhanced traffic safety and mobility. This level is called the higher level
controller. I further focus on generating a driver controller to complement the automated
car’s safe driving. To compute this, model predictive controller (MPC) is utilized. The
success of the combined decision process and trajectory generation is evaluated with a set
of different traffic scenarios in dSPACE virtual driving environment.
Next, I consider designing an active front steering (AFS) and direct yaw moment control
(DYC) as the lower level controller that performs a lane change task with enhanced
handling performance in the presence of varying front and rear cornering stiffnesses. I propose
a new control scheme that integrates active front steering and the direct yaw moment
control to enhance the vehicle handling and stability. I obtain the nonlinear tire forces
with Pacejka model, and convert the nonlinear tire stiffnesses to parameter space to design
a linear parameter varying controller (LPV) for combined AFS and DYC to perform a
commanded lane change task. Further, the nonlinear vehicle lateral dynamics is modeled
with Takagi-Sugeno (T-S) framework. A state-feedback fuzzy H∞ controller is designed
for both stability and tracking reference. Simulation study confirms that the performance
of the proposed methods is quite satisfactory
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