Predictive Control of Linear Uncertain Systems

Predictive control is a very useful tool in controlling constrained systems, since the constraints can be satisfied explicitly by the optimisations. Sets, namely, reachable sets, controllable sets, invariant sets, etc, play fundamental roles in designing predictive control strategies for uncertain systems. Meanwhile, in addition to the commonly assumed boundedness of the uncertainty, the explicit use of its stochastic properties can lead to imprq\fement in system response. This thesis is concerned with robust set theories, mainly for reachable sets, with applications to time-optimal control; and the use of stochastic properties of the uncertainty to achieve less conservative controls. In the first part of this thesis, we focus on LTI systems subject to, additional to the usual constraints, a constraint on the control change between sample times. One key ingredient in controlling such constrained systems is the initial control value, which, via analyses and simulations, is shown to be a useful extra degree of freedom. Reachable sets that incorporate this influential initial control value are derived and analyzed, with theoretical as well as computational algorithms developed for both nominal and uncertain systems under different types of feedback policy. Following this, the reachable set is discussed in connection with time-optimal control to obtain desired control laws. In addition, controllable sets, stabilisable sets and invariant sets for such constrained uncertain systems are studied. In the second part, the uncertainties are assumed to have stochastic properties. They are exploited in three different ways: the expected worst-case is used instead of the worst-case to achieve less conservative control even when the uncertainty is relatively large; the stochastic invariant set is proposed to provide alternative methods for approximating disturbance invariant sets; the relaxed set difference is developed to obtain less restrictive controls and/or replacing probabilistic constraint or slack variables.Imperial Users onl

Design of of model-based controllers via parametric programming

Imperial Users onl

CONTROL PREDICTIVO SUJETO A RESTRICCIONES POLIÉDRICAS NO CONVEXAS: SOLUCIÓN EXPLÍCITA Y ESTABILIDAD

En esta tesis doctoral se aborda el problema del control predictivo sujeto a restricciones definidas como la unión no convexa de varios poliedros. Los controladores propuestos son de utilidad, por un lado, para procesos que presentan de manera natural restricciones de dicha forma y, por otra parte, como una alternativa al control predictivo no lineal cuando períodos de muestreo bajos no permiten la aplicación de programación no lineal. En los primeros capítulos del trabajo se demuestra la existencia de una solución explícita a los problemas de optimización que aparecen al plantear este tipo de controladores predictivos. Dicha solución es afín a tramos definidos mediante desigualdades lineales y cuadráticas. Se introducen dos metodologías diferentes para la obtención de esta solución explícita: la metodología de intersección, división y unión y la de la envolvente convexa. La primera de estas metodologías se basa en formular subproblemas con las restricciones convexas cuya unión forma las restricciones originales y obtener la solución explícita del problema original a partir de las soluciones de dichos subproblemas. La segunda metodología planteada se basa en el cálculo de la envolvente convexa de los conjuntos de restricciones y la obtención de la solución explícita del problema convexo definido por estas nuevas restricciones. Se demuestra como parte de las regiones de la solución explícita del problema original coinciden con las del nuevo problema, y se propone un procedimiento para identificarlas y obtener el resto de regiones, completando la solución explícita buscada. Se estudian también algoritmos eficientes para la implementación en línea de leyes de control explícitas como las obtenidas. En particular, se propone un algoritmo basado en un árbol binario de una partición lineal y una comparación de índices de costes en las regiones en las que sea necesario.Pérez Soler, E. (2011). CONTROL PREDICTIVO SUJETO A RESTRICCIONES POLIÉDRICAS NO CONVEXAS: SOLUCIÓN EXPLÍCITA Y ESTABILIDAD [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/9315Palanci

Fast numerical methods for mixed--integer nonlinear model--predictive control

This thesis aims at the investigation and development of fast numerical methods for nonlinear mixed--integer optimal control and model- predictive control problems. A new algorithm is developed based on the direct multiple shooting method for optimal control and on the idea of real--time iterations, and using a convex reformulation and relaxation of dynamics and constraints of the original predictive control problem. This algorithm relies on theoretical results and is based on a nonconvex SQP method and a new active set method for nonconvex parametric quadratic programming. It achieves real--time capable control feedback though block structured linear algebra for which we develop new matrix updates techniques. The applicability of the developed methods is demonstrated on several applications. This thesis presents novel results and advances over previously established techniques in a number of areas as follows: We develop a new algorithm for mixed--integer nonlinear model- predictive control by combining Bock's direct multiple shooting method, a reformulation based on outer convexification and relaxation of the integer controls, on rounding schemes, and on a real--time iteration scheme. For this new algorithm we establish an interpretation in the framework of inexact Newton-type methods and give a proof of local contractivity assuming an upper bound on the sampling time, implying nominal stability of this new algorithm. We propose a convexification of path constraints directly depending on integer controls that guarantees feasibility after rounding, and investigate the properties of the obtained nonlinear programs. We show that these programs can be treated favorably as MPVCs, a young and challenging class of nonconvex problems. We describe a SQP method and develop a new parametric active set method for the arising nonconvex quadratic subproblems. This method is based on strong stationarity conditions for MPVCs under certain regularity assumptions. We further present a heuristic for improving stationary points of the nonconvex quadratic subproblems to global optimality. The mixed--integer control feedback delay is determined by the computational demand of our active set method. We describe a block structured factorization that is tailored to Bock's direct multiple shooting method. It has favorable run time complexity for problems with long horizons or many controls unknowns, as is the case for mixed- integer optimal control problems after outer convexification. We develop new matrix update techniques for this factorization that reduce the run time complexity of all but the first active set iteration by one order. All developed algorithms are implemented in a software package that allows for the generic, efficient solution of nonlinear mixed-integer optimal control and model-predictive control problems using the developed methods