By Guillaume Sandou
The vintage method in computerized regulate is dependent upon using simplified types of the platforms and reformulations of the necessities. during this framework, the keep an eye on legislations might be computed utilizing deterministic algorithms. in spite of the fact that, this procedure fails while the approach is simply too complicated for its version to be sufficiently simplified, whilst the fashion designer has many constraints take into consideration, or whilst the objective is not just to layout a regulate but additionally to optimize it. This publication offers a brand new development in automated regulate with using metaheuristic algorithms. these types of set of rules can optimize any criterion and constraint, and accordingly don't have such simplifications and reformulations.
The first bankruptcy outlines the author’s major motivations for the method which he proposes, and provides the benefits which it bargains. In bankruptcy 2, he bargains with the matter of approach identity. The 3rd and fourth chapters are the middle of the booklet the place the layout and optimization of regulate legislations, utilizing the metaheuristic approach (particle swarm optimization), is given. The proposed method is gifted besides real-life experiments, proving the potency of the method. eventually, in bankruptcy five, the writer proposes fixing the matter of predictive regulate of hybrid systems.
1. advent and Motivations.
2. Symbolic Regression.
3. PID layout utilizing Particle Swarm Optimization.
4. Tuning and Optimization of H-infinity keep watch over Laws.
5. Predictive keep watch over of Hybrid Systems.
About the Authors
Guillaume Sandou is Professor within the computerized division of Supélec, in Gif Sur Yvette, France. He has had 12 books, eight magazine papers and 1 patent released, and has written papers for 32 overseas conferences.His major learn pursuits contain modeling, optimization and regulate of commercial structures; optimization and metaheuristics for automated regulate; and limited control.
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Additional resources for Metaheuristic Optimization for the Design of Automatic Control Laws (Focus Automation and Control)
The interest is to find a global input/output relation that fits with the data. – In the real world, data are measured and so measurement noise has to be taken into account: in this case, the cost can never be zero. – In the real world, the system can be expressed by symbols that are not considered in the algorithm. The value of the threshold is not so crucial for “pure data”, in other words, without any noise. The last case is a more realistic case, as measurement noise is added to the data. In such a situation, the value of the threshold should be adapted to the level of the noise.
4. 3, some well-known specifications have been called up. ), non-differentiable or even non-analytic. 1, numerous classical approaches do exist to compute a controller that satisfies a given set of specifications (taking into account the fact that specifications may have to be formulated in a particular framework, or that experiments can be performed on the system). For instance, graphical methods can be used to define lead or lag controllers to satisfy some constraints on the phase margin. However, the problem is now: – not only to satisfy a set of constraints, but to optimize the performances of the system; – to take into account all constraints in the design procedure and in one shot.
In this chapter, a new approach is proposed to manage both problems: particle swarm optimization (PSO) is used first to compute the parameters of the weighting filters (assuming that a full-order controller is looked for). Then, the computation of a reduced-order controller is performed using the obtained filters. PSO was first introduced by Eberhart and Kennedy in the mid-1990s [EBE 95] and has been described in Chapter 3 for the optimization of proportional integral derivative (PID) controllers.
Metaheuristic Optimization for the Design of Automatic Control Laws (Focus Automation and Control) by Guillaume Sandou