Samenvatting
The main topic of this thesis is control of dynamic systems that are subject to stochastic
disturbances and constraints on the input and the state. The main motivation for
dealing with control of such systems is that there is no method available that adequately
deals with this problem, despite the fact that stochastic, constrained systems
are often encountered in real world problems. For example, in process industry the
margins of physical quantities such as temperature, pressure, concentration, velocity
and position can be expressed as amplitude constraints in a natural way. Such constraints
are usually persistent in that suitable control actions need to be implemented
that respect these constraints irrespective of the presence of uncontrolled disturbances
that effect the system.
Goals of the thesis are to
1. Formulate a mathematical problem for the synthesis of a controller that will
achieve desired performance of the controlled system. More precisely, to minimize
a performance measure that captures desired performance while respecting
constraints in the face of stochastic disturbances.
2. Deduce verifiable conditions under which the problem formulated in 1. is solvable.
3. Formulate a solution concept for the problem in 1. that is based on the model
predictive control technique.
4. Create feasible computational algorithms for the synthesis of controllers that
solve control problems from 1. within the solution setup from 3.
5. Investigate convergence properties of the approximate solutions obtained by
computational algorithms from 4.
The main tool that is used in the thesis to solve the problem formulated in 1. is the
model predictive control technique. Model predictive control has had a significant and
widespread impact on industrial process control. When dealing with stochastic systems,
however, application of the standard model predictive control algorithms results
in a significant loss in the controlled system performance. Therefore, to deal with
the problem 1. within the model predictive control framework, it was necessary to
develop alternative model predictive control techniques.
Contributions of the thesis are twofold. The first set of contributions is made with
regard to the model predictive control of constrained, stochastic systems. In this thesis,
we develop a novel approach to the model predictive control of such systems, that
is based on the optimization in closed loop over the control horizon and stochastic
sampling of the disturbance i.e. a randomized algorithm.
The second set of contributions has been made in more general framework of the
optimal control of stochastic systems that are subject to input and state constraints.
We present a novel problem setup for control of such systems and give initial results
that are concerned with solvability conditions for the posed optimization problem and
the characterization of the optimal solution.
Originele taal2  Engels 

Kwalificatie  Doctor in de Filosofie 
Toekennende instantie 

Begeleider(s)/adviseur 

Datum van toekenning  14 jan 2004 
Plaats van publicatie  Eindhoven 
Uitgever  
Gedrukte ISBN's  9038608128 
DOI's  
Status  Gepubliceerd  2004 
Vingerafdruk Duik in de onderzoeksthema's van 'Model predictive control for stochastic systems by randomized algorithms'. Samen vormen ze een unieke vingerafdruk.
Citeer dit
Batina, I. (2004). Model predictive control for stochastic systems by randomized algorithms. Technische Universiteit Eindhoven. https://doi.org/10.6100/IR573294