Multiobjective PID controller design for active suspension system: scalarization approach

Abstract

In this study, the PID tuning method (controller design scheme) is proposed for a linear quarter model of active suspension system installed on the vehicles. The PID tuning scheme is considered as a multiobjective problem which is solved by converting this multiobjective problem into single objective problem with the aid of scalarization approaches. In the study, three different scalarization approaches are used and compared to each other. These approaches are called linear scalarization (weighted sum), epsilon-constraint and Bensons methods. The objectives of multiobjective optimization are selected from the time-domain properties of the transient response of the system which are overshoot, rise time, peak time and error (in total there are four objectives). The aim of each objective is to minimize the corresponding property of the time response of the system. First, these four objective is applied to the scalarization functions and then single objective problem is obtained. Finally, these single objective problems are solved with the aid of heuristic optimization algorithms. For this purpose, four optimization algorithms are selected, which are called Particle Swarm Optimization, Differential Evolution, Firefly, and Cultural Algorithms. In total, twelve implementations are evaluated with the same number of iterations. In this study, the aim is to compare the scalarization approaches and optimization algorithm on active suspension control problem. The performance of the corresponding cases (implementations) are numerically and graphically demonstrated on transient responses of the system.

Prediction of anemia with a particle swarm optimization-based approach

“…In literature, various versions of the PSO-SVM algorithm have been developed for different issues in multiple scientific fields. In comparison to the literature, the PSO and SVM clustering algorithms have been observed to be extremely successful for their own concerns [29][30][31][34][35][36][37][38][39][40][41][42][43]. This study successfully created a combined version of the algorithm, as well as its computer code.…”

Parameter effect analysis of particle swarm optimization algorithm in PID controller design

“…Because these algorithms have been agreed as an alternative method of solving deterministic optimization problems or stochastic programming whose solution is not feasible in most cases even though optimality is proven. Genetic algorithm (GA) [1,2], ant colony optimization (ACO) [3], cuckoo search algorithm (CSA) [4], flower pollination algorithm (FPA) [5], differential evolution [6], artificial bee colony algorithm (ABC) [8], and particle swarm optimization (PSO) [7,[9][10][11] are some of meta-heuristic optimization algorithms used in controller design process in control system area.…”