Control and Coordination in Hierarchical Systems

This book presents the applied theory of control and cooordination in hierarchical systems which are those where decision making has been divided in a certain way. It concentrates on various aspects of optimal control in large scale systems and covers a range of topics from multilevel methods for optimizing by interactive feedback procedures to methods for sequential, hierarchical control in large dynamic systems

Offset-free nonlinear Model Predictive Control with state-space process models

Offset-free model predictive control (MPC) algorithms for nonlinear state-space process models, with modeling errors and under asymptotically constant external disturbances, is the subject of the paper. The main result of the paper is the presentation of a novel technique based on constant state disturbance prediction. It was introduced originally by the author for linear state-space models and is generalized to the nonlinear case in the paper. First the case with measured state is considered, in this case the technique allows to avoid disturbance estimation at all. For the cases with process outputs measured only and thus the necessity of state estimation, the technique allows the process state estimation only - as opposed to conventional approach of extended process-and-disturbance state estimation. This leads to simpler design with state observer/filter of lower order and, moreover, without the need of a decision of disturbance placement in the model (under certain restrictions), as in the conventional approach. A theoretical analysis of the proposed algorithm is provided, under applicability conditions which are weaker than in the conventional approach. The presented theory is illustrated by simulation results of nonlinear processes, showing competitiveness of the proposed algorithms

A computationally efficient multivariable predictive control algorithm for input-output models

W pracy przedstawiono kompletne wyprowadzenie algorytmu regulacji predykcyjnej wielowymiarowych procesów liniowych modelowanych za pomocą dyskretnych równań różnicowych. W przeciwieństwie do algorytmu GPC, prognozowana trajektoria wymuszona i swobodna sygnałów wyjściowych wyznaczana jest bez potrzeby rozwiązania macierzowego równania diofantycznego. W przypadku braku ograniczeń zmiennych procesowych zadanie optymalizacji funkcji kryterialnej rozwiązuje się numerycznie efektywną metodą najmniejszych kwadratów. Omówiono również sposób wyprowadzenia analitycznego przwa regulacji.This paper develops a predictive control algorithm for multivariable processes modelled by means of linear input-output models. Unlike the GPC algorithm, predicted forced and free trajectories are calculated without the necessity of solving a matrix Diophantine equation. In the unconstrained case the optimal input profile, using a numerically reliable least-squares method, is derived as an analytical formula, calculated beforehand

Soft computing in model-based predictive control

The application of fuzzy reasoning techniques and neural network structures to model-based predictive control (MPC) is studied. First, basic structures of MPC algorithms are reviewed. Then, applications of fuzzy systems of the Takagi-Sugeno type in explicit and numerical nonlinear MPC algorithms are presented. Next, many techniques using neural network modeling to improve structural or computational properties of MPC algorithms are presented and discussed, from a neural network model of a process in standard MPC structures to modeling parts or entire MPC controllers with neural networks. Finally, a simulation example and conclusions are given

Analysis of an Isope-Type Dual Algorithm for Optimizing Control and Nonlinear Optimization

First results concerning important theoretical properties of the dual ISOPE (Integrated System Optimization and Parameter Estimation) algorithm are presented. The algorithm applies to on-line set-point optimization in control structures with uncertainty in process models and disturbance estimates, as well as to difficult nonlinear constrained optimization problems. Properties of the conditioned (dualized) set of problem constraints are investigated, showing its structure and feasibility properties important for applications. Convergence conditions for a simplified version of the algorithm are derived, indicating a practically important threshold value of the right-hand side of the conditioning constraint. Results of simulations are given confirming the theoretical results and illustrating properties of the algorithms

Effective dual-mode fuzzy DMC algorithms with on-line quadratic optimization and guaranteed stability

Dual-mode fuzzy dynamic matrix control (fuzzy DMC-FDMC) algorithms with guaranteed nominal stability for constrained nonlinear plants are presented. The algorithms join the advantages of fuzzy Takagi-Sugeno modeling and the predictive dual-mode approach in a computationally efficient version. Thus, they can bring an improvement in control quality compared with predictive controllers based on linear models and, at the same time, control performance similar to that obtained using more demanding algorithms with nonlinear optimization. Numerical effectiveness is obtained by using a successive linearization approach resulting in a quadratic programming problem solved on-line at each sampling instant. It is a computationally robust and fast optimization problem, which is important for on-line applications. Stability is achieved by appropriate introduction of dual-mode type stabilization mechanisms, which are simple and easy to implement. The effectiveness of the proposed approach is tested on a control system of a nonlinear plant-a distillation column with basic feedback controllers

Actuator fault tolerance in control systems with predictive constrained set-point optimizers

Mechanisms of fault tolerance to actuator faults in a control structure with a predictive constrained set-point optimizer are proposed. The structure considered consists of a basic feedback control layer and a local supervisory set-point optimizer which executes as frequently as the feedback controllers do with the aim to recalculate the set-points both for constraint feasibility and economic performance. The main goal of the presented reconfiguration mechanisms activated in response to an actuator blockade is to continue the operation of the control system with the fault, until it is fixed. This may be even long-term, if additional manipulated variables are available. The mechanisms are relatively simple and consist in the reconfiguration of the model structure and the introduction of appropriate constraints into the optimization problem of the optimizer, thus not affecting the numerical effectiveness. Simulation results of the presented control system for a multivariable plant are provided, illustrating the efficiency of the proposed approach

An infinite horizon predictive control algorithm based on multivariable input-output models

In this paper an infinite horizon predictive control algorithm, for which closed loop stability is guaranteed, is developed in the framework of multivariable linear input-output models. The original infinite dimensional optimisation problem is transformed into a finite dimensional one with a penalty term. In the unconstrained case the stabilising control law, using a numerically reliable SVD decomposition, is derived as an analytical formula, calculated off-line. Considering constraints needs solving on-line a quadratic programming problem. Additionally, it is shown how free and forced responses can be calculated without the necessity of solving a matrix Diophantine equation

Cooperation of predictive control and set-point optimisation in control structures with Wiener models

Numerycznie efektywne struktury sterowania alternatywne do klasycznej, warstwowej struktury sterowania są przedmiotem badań. W pierwszej strukturze dodano pomocnicze, liniowe zadanie optymalizacji punktu pracy. W drugiej strukturze, zadania optymalizacji punktu pracy i regulacji predykcyjnej są integrowane w jednym zadaniu optymalizacji kwadratowej. Użycie w tych strukturach modelu Wienera dodatkowo je upraszcza dzięki możliwości łatwego otrzymania liniowych aproksymacji dynamiki i statyki procesu.Two numerically efficient control system structures alternative to the classical one are considered. In the first one the supplementary Steady State Target Optimization (SSTO) is performed at each sampling instant. In the second one set-point optimization and predictive control are integrated into one optimization task. In the proposed approaches, thanks to using a Wiener process model, both predictive control and set-point optimization problems are simplified. Using the nonlinear model a linear dynamic and linear static approximations are easily obtained and used both for set-point optimization and predictive control