Adrian Gambier

MPC and PID control based on Multi-Objective Optimization

The design of sophisticated control systems have led in the past ten years to the necessity of satisfying more than one design criterion. Thus, it is natural to think that those criteria can be met in an optimal manner. If several criteria have simultaneously to be optimized, one is in presence of a multi-objective optimization problem. In this paper, many efforts to design the most popular control strategies, i.e. PID and MPC, by using multi-objective optimization techniques are reviewed. Both control strategies have dissimilar optimization characteristics and therefore, they can be considered as representative of two different multi-objective optimization problems, which are described including definitions, possible solutions, algorithms and available software implementations.

Dynamic Modeling of a Simple Reverse Osmosis Desalination Plant for Advanced Control Purposes

Reverse osmosis (RO) has become an important process for desalting water. It requires an efficient control system to maintain costs at acceptable level and therefore dynamic models are essential. Although it is possible to find in the literature steady-state models and some dynamic models obtained by parameter identification, there are no reports about lumped parameter dynamic models for control purposes obtained by application of physical laws. Such models are useful not only to design model-based control systems but also for the implementation of fault tolerant systems based on fault detection and isolation (FDI) methods, as well as to analyze transient characteristics of the plant. In this paper, models from the literature are shortly analyzed and a simple lumped parameter model for control purposes, which is derived from physical laws, is proposed. Moreover, a block-oriented library for MATLAB/SIMULINKtrade is presented, so that different plant configurations can be implemented as block diagram to simulate the system and to test control algorithms.

Multi-objective Optimal Control: An Overview

Optimization techniques have been a crucial tool for designing control systems and for tuning controllers. The always increasing quality requirements for new products and consequently the natural advance in control system design have lead to the introduction of more than one design criterion, which will require in turn more sophisticated techniques to solve such Multi-Objective Optimization (MOO) problems. Thus, many examples of using MOO techniques in control have appeared in the literature. In this paper, several optimal control design problems are analyzed from the MOO point of view. Moreover, some suggestions about how standard control design techniques can be extended to introduce multi-objective optimization are given.

Application of hybrid modeling and control techniques to desalination plants

One of the most recent and most intense efforts in control theory deals with handling systems whose behavior of interest is determined by interacting continuous and discrete dynamics. This approach can be applied not only to intrinsic hybrid processes but also to other systems as for example continuous processes with supervisory logic, multi-model control systems, switching control, etc. In this paper, hybrid systems are briefly introduced and possible applications to desalination plants are given by means of illustrative examples.

Optimal control of a reverse osmosis desalination plant using multi-objective optimization

In this contribution, the control of a reverse osmosis desalination plant by using an optimal multi-loop approach is presented. Controllers are assumed to be players of a cooperative game, whose solution is obtained by multi-objective optimization (MOO). The MOO problem is solved by applying a genetic algorithm and the final solution is found from this Pareto set. For the reverse osmosis plant a control scheme consisting of two PI control loops are proposed. Simulation results show that in some cases, as for example this desalination plant, multi-loop control with several controllers, which have been obtained by join multi-objective optimization, perform as good as more complex controllers but with less implementation effort.

A New Approach to Design Multi-loop Control Systems with Multiple Controllers

In this contribution, a new method to design multi-loop control systems with several controllers is proposed. Controllers are assumed to be players of a cooperative dynamic game, whose solution is obtained by multi-objective optimization (MOO). The MOO problem is solved by applying a genetic algorithm and the final solution is found from an optimal Pareto set. As illustrative example, the control system design of a reverse osmosis desalination plant is used. Simulation results are satisfactory and show that in many cases, as for example this desalination plant, multi-loop control with several controllers, which have been obtained by join multi-objective optimization, perform as good as more complex controllers but with less implementation effort

Application of model predictive control for fault tolerant system using dynamic safety margin

Model predictive control (MPC) has the ability to cope with hard constraints on control and state. It has, therefore, been widely applied in most industries specially, petrochemical industries. Dynamic safety margin (DSM) is a performance index used to measure the distance between a predefined safety boundary, described by a set of inequality constraints, in state space and system trajectory as it evolves. Designing MPC based on DSM is especially important for safety critical system to maintain a predefined margin of safety during transient and steady state. In this work, MPC based on DSM is used in fault tolerant control (FTC) design. The proposed method of FTC is suitable for single and multi-model system according to the fault type and fault information. It can compensate missed information about the fault and uncertainties in the faulty model

Real-time Implementation of Fault-tolerant Control Using Model Predictive Control

Abstract Fault-tolerant control using model predictive control with online accommodation to recover from faults is investigated. A framework for this purpose is presented and problems that one encounters by changing the control law online like error-free tracking, feasibility and computational effort are addressed. In a real-time implementation, the model predictive controller is tested under actuator faults like saturation, freezing and total loss as well as under a structural fault.

Brief Paper: Multivariable generalized state-space receding horizon control in a real-time environment

The results of a multivariable generalized state-space receding horizon control, applied to a turbo-generator pilot plant, are reported in this paper. The control algorithm consists of a state-space receding horizon tracking system, a compensator for piecewise constant random disturbances, and a state and disturbances estimator based on an augmented Kalman filter. The turbo-generator consisting of a turbine and a synchronous generator exhibits nonlinear and nonminimum-phase behaviour in a stochastic environment.

Digital PID controller design based on parametric optimization

PID control is probably the most applied control strategy around the world. There is also a huge amount of literature available about this control strategy. In the most common case, the controller is formulated in the continuous-time domain. If it is necessary to implement the controller as a computational algorithm, controllerpsilas equations are discretized. It is less frequent to find PID control laws, which are directly formulated as a discrete-time system. It is also possible, but unusual, to find design methods for digital PID controllers based on parametric optimization. Therefore, the objective of this paper is to revisit the digital PID/PI controller design based on parametric optimization including new advanced optimization methods as well as taking into consideration the new calculation machinery for several performance indices. Single as well as multi-objective optimization approaches are considered. Finally, available software and practical implementation aspects are highlighted.