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Optimal Controller Parameter Tuning from Multi/Many-objective Optimization Algorithms

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Computational Intelligence and Optimization Methods for Control Engineering

Part of the book series: Springer Optimization and Its Applications ((SOIA,volume 150))

Abstract

Controller performance is evaluated with the properties of steady-state and transient response of the system at the time domain. The compensators and conventional controllers like PID are designed, so that the desired performance is reached only by adjusting the controller parameters; this adjustment mechanism is called tuning. Even many approaches are proposed for tuning; still, it remains one of the problems of control theory due to the imperfect modeling, disturbance, and problem complexity. At this stage, optimization algorithms have helped the researchers to find these parameters via designing objective function concerning the time response characteristics of the system. As the expectations (for example, steady-state error, overshoot, rise time, settling time) related to the system performance are increased in number, the number of objectives is also increased. As a result, multi-objective optimization algorithms have applied to solve these problems. In this chapter, a set of benchmark tuning problems is defined as the test—benchmark—problems. Then, by using the multi- and many-objective optimization algorithms, the performance of the controlled system with respect to the Pareto approximate set is compared with each other. The multi/many-objective optimization algorithms evaluated in this chapter are as follows: Multi-objective Evolutionary Algorithm based on Decomposition (MOEAD), Non-dominated Sorting Genetic Algorithm II (NSGA-II), Multi-objective Particle Swarm Optimization (MOPSO), Strength Pareto Evolutionary Algorithm 2 (SPEA2), Approximation-guided Evolutionary Algorithm II (AGE-II), and Reference Vector Guided Evolutionary Algorithm (RVEA).

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Authors and Affiliations

  1. Department of Electrical and Electronics Engineering, Ankara University, Ankara, Turkey

    O. Tolga Altinoz

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  1. O. Tolga Altinoz
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Correspondence to O. Tolga Altinoz .

Editor information

Editors and Affiliations

  1. Department of Mechanical and Aerospace Engineering, University of Florida, Gainesville, FL, USA

    Maude Josée Blondin

  2. Department of Industrial and Systems Engineering, University of Florida, Gainesville, FL, USA

    Panos M. Pardalos

  3. Institute of Automation and Industrial Computer, Polytechnic University of Valencia, Valencia, Spain

    Javier Sanchis Sáez

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Altinoz, O.T. (2019). Optimal Controller Parameter Tuning from Multi/Many-objective Optimization Algorithms. In: Blondin, M., Pardalos, P., Sanchis Sáez, J. (eds) Computational Intelligence and Optimization Methods for Control Engineering. Springer Optimization and Its Applications, vol 150. Springer, Cham. https://doi.org/10.1007/978-3-030-25446-9_3

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