Xavier Blasco Ferragud

Generalized Predictive Control Using Genetic Algorithms (GAGPC). An Application to Control of a Non-linear Process with Model Uncertainty

Predictive Control is one of the most powerful techniques in process control, but its application in non-linear processes is challenging. This is basically because the optimization method commonly used limits the kind of functions which can be minimized. The aim of this work is to show how the combination of Genetic Algorithms (GA) and Generalized Predictive Control (GPC), what we call GAGPC, can be applied to nonlinear process control. This paper also shows GAGPC performance when controlling non-linear processes with model uncertanties. Success in this area will open the door to using GAGPC for a better control of industrial processes.

MIMO Predictive Control of Temperature and Humidity Inside a Greenhouse Using Simulated Annealing (SA) as Optimizer of a Multicriteria Index

Correct climate control improves the quality of productions in greenhouses. Those control techniques that do not take into account the non-linear and multivariable features of the climate in the greenhouse, cannot achieve good performance (set-points will not be accomplished). This paper presents a Predictive Contro based technique using a mathematical model of the climate behaviour and Simulated Annealing as optimizer. Results show that this technique can be useful when dealing with non-linear and multivariable plants, even if constraints in the control actions are considered.

Background on Multiobjective Optimization for Controller Tuning

In this chapter a background on multiobjective optimization and a review on multiobjective optimization design procedures within the context of control systems and the controller tuning problem are provided. Focus is given on multiobjective problems where an analysis of the Pareto front is required, in order to select the most preferable design alternative for the control problem at hand.

Model Based Predictive Control Using Genetic Algorithms. Application to Greenhouses Climate Control

Solving multivariables and non-linear problems with constrains is usual when dealing with control problems. The classical way to solve this was through the decomposition into less complex problems: sub-problems with less variables and through the use of linear approximated models. These methodologies can present good results, but for some, only a suboptimal solution with a poor quality can be reached. The aim of this work is to combine Model Based Predictive Control (MBPC), a powerful control technique, with Genetic Algorithms, a powerful optimization technique. This combination can overcome limitations when approaching very complex problems in an integral way. This work extends this application to Multi Inputs Multi Outputs modeled with state space representation (a general way to include a wide range of non-linearities) and shows its application to Greenhouse Climate Control.

Controller Tuning for Multivariable Processes

In this chapter, a multivariable controller is tuned by means of a multiobjective optimization design procedure. For this design problem, several specifications are given, regarding individual control loops and overall performance. Due to this fact, a many-objectives optimization problem is stated. In such problems, algorithms could face problems due to the dimensionality of the problem, since their mechanisms to improve convergence and diversity may conflict. Therefore, some guidelines to deal with this optimization process are commented. The aforementioned procedure will be used to tune a multivariable PI controller for the well known Wood and Berry distillation column process using different algorithms.

The 2012 IFAC Control Benchmark: An Industrial Boiler Process

In this chapter, a multiobjective optimization design procedure is applied to the multivariable version of the Boiler Control Problem, defined in 2nd IFAC Conference on Advances in PID Control, 2012. The chapter follows a realistic approach, closer to industrial practices: a nominal linear model will be identified and afterwards a constrained multiobjective problem with 5 design objectives will be stated. Such objectives will deal with overall robust stability, settling time performance and noise sensitivity. After approximating the Pareto Front and performing a multicriteria decision-making stage, the selected control system will be tested using the original nonlinear model.

Motivation: Multiobjective Thinking in Controller Tuning

Throughout this chapter, we intend to provide a multiobjective awareness of the controller tuning problem. Beyond the fact that several objectives and requirements must be fulfilled by a given controller, we will show the advantages of considering this problem in its multiobjective nature. That is, optimizing simultaneously several objectives and following a multiobjective optimization design (MOOD) procedure. Since the MOOD procedure provides the opportunity to obtain a set of solutions to describe the objectives trade-off for a given multiobjective problem (MOP), it is worthwhile to use it for controller tuning applications. Due to the fact that several specifications such as time and frequency requirements need to be fulfilled by the control engineer, a procedure to appreciate the trade-off exchange for complex processes is useful.

Comparing Control Structures from a Multiobjective Perspective

This chapter will illustrate the tools presented in previous chapters for the analysis and comparison of different design concepts. In particular, three different control structures (PI, PID and GPC) will be compared, analysing their benefits and drawbacks within a multiobjective approach. First, a two objective approach, where robustness and disturbance rejection are analyzed, will be developed. Later, a third objective will be added related to setpoint tracking. Since PI design concept has only two parameters to be tuned, the PID design concept will be set with a derivative gain \(K_d\) depending on other controller parameters for a fair comparison. Regarding the Generalized Predictive Controller (GPC) all parameters except prediction horizon and filter parameter will be fixed. Development of the example let the reader know how the tools can help to compare different control structures and how to choose the parameters for the best controller from the point of view of DM within a MOOD approach.

Tools for the Multiobjective Optimization Design Procedure

In this chapter, tools for the evolutionary multiobjective optimization process and the multicriteria decision making stage to be used throughout this book (as a reference) are presented. Regarding the optimization process, three different versions of a multiobjective evolutionary algorithm based on Differential Evolution will be commented; with those proposals, features such as convergence, diversity and pertinency are considered. Regarding the decision making stage, Level Diagrams will be introduced, due to their capabilities to analyze m-dimensional Pareto fronts.

Multiobjective Optimization Design Procedure for an Aircraft’s Flight Control System

In this chapter, the multiobjective optimization design procedure will be used to tune the autopilot controllers for an autonomous Kadett\(\copyright \) aircraft. For this aim, a multivariable PI controller is defined, and a many-objectives optimization instance is tackled using designer preferences. After the multicriteria decision making stage, the selected controller is implemented and evaluated in a real flight test.