Constrained Fuzzy Predictive Control Using Particle Swarm Optimization

Abstract

A fuzzy predictive controller using particle swarm optimization (PSO) approach is proposed. The aim is to develop an efficient algorithm that is able to handle the relatively complex optimization problem with minimal computational time. This can be achieved using reduced population size and small number of iterations. In this algorithm, instead of using the uniform distribution as in the conventional PSO algorithm, the initial particles positions are distributed according to the normal distribution law, within the area around the best position. The radius limiting this area is adaptively changed according to the tracking error values. Moreover, the choice of the initial best position is based on prior knowledge about the search space landscape and the fact that in most practical applications the dynamic optimization problem changes are gradual. The efficiency of the proposed control algorithm is evaluated by considering the control of the model of a 4 × 4 Multi-Input Multi-Output industrial boiler. This model is characterized by being nonlinear with high interactions between its inputs and outputs, having a nonminimum phase behaviour, and containing instabilities and time delays. The obtained results are compared to those of the control algorithms based on the conventional PSO and the linear approach.

Parameter Optimization of MIMO Fuzzy Optimal Model Predictive Control By APSO

“…The second example is the CSTR. We compare our results with those obtained by other existing methods such as NMPC [31] and FMPC using the APSO algorithm [32]. In this paper, the Tr, Ts, Ov%, pic, and Es are used as the performance indexes.…”

A Non-linear Model Predictive Control Based on Grey-Wolf Optimization Using Least-Square Support Vector Machine for Product Concentration Control in l-Lysine Fermentation

“…Conventionally, NP is involved in computationally expensive step of determining the hessian matrix and its inverse [ 25 ]. In addition, these solutions are highly dependent on the selection of initial point value and can easily fall in local optimal region (solution) [ 26 ]. The biggest challenge in designing an NMPC is to find an algorithm that minimizes a cost function in real time.…”

Constrained Uncertain System Stabilization with Enlargement of Invariant Sets

“…Moreover, model predictive control is an interesting approach to represent systems using fuzzy logic for designing controllers. Several works have focused on the use of fuzzy-model-based sliding mode control of nonlinear systems in combination with MPC algorithms [11][12][13][14]. In fact, the fuzzy logic technique is quite attractive in terms of time, simplicity of implementation, relatively low cost, and ability to rapidly model complex systems.…”