Sofiane Bououden

An ant colony optimization-based fuzzy predictive control approach for nonlinear processes

In this paper, a new approach for designing an adaptive fuzzy model predictive control (AFMPC) based on the ant colony optimization (ACO) is proposed. On-line adaptive fuzzy identification is introduced to identify the system parameters. These parameters are used to calculate the objective function based on a predictive approach and structure of RST control. Then the optimization problem is solved based on an ACO algorithm, used at the optimization process in AFMPC to determine optimal controller parameters of RST control. The utility of the proposed controller is demonstrated by applying it to two nonlinear processes, where the proposed approach provides better performances compared with proportional integral-ant colony optimization controller and adaptive fuzzy model predictive controller.

Fuzzy Sliding Mode Controller Design Using Takagi-Sugeno Modelled Nonlinear Systems

Adaptive fuzzy sliding mode controller for a class of uncertain nonlinear systems is proposed in this paper. The unknown system dynamics and upper bounds of the minimum approximation errors are adaptively updated with stabilizing adaptive laws. The closed-loop system driven by the proposed controllers is shown to be stable with all the adaptation parameters being bounded. The performance and stability of the proposed control system are achieved analytically using the Lyapunov stability theory. Simulations show that the proposed controller performs well and exhibits good performance.

Control of uncertain highly nonlinear biological process based on Takagi-Sugeno fuzzy models

This note deals with the control of uncertain highly nonlinear biological processes. Indeed, an adaptive fuzzy control (AFC) scheme is developed for the pre-treatment of wastewater represented by a Takagi-Sugeno (TS) fuzzy model. The proposed approach uses a fuzzy system to approximate the unknown substrate consumption rate in designing the adaptive controller, and then an observer is designed to estimate the concentration in substrate at the outlet bioreactor. The observer is employed to generate an error signal for the adaptive control law which permits to minimize the influence of the measurement noise on the estimation of the substrate concentration. An update of the fuzzy models parameters are obtained using Lyapunov?s second method. The closed-loop system behavior is then illustrated on a noisy simulation. To design and implement an automatic regulation of wastewater pre-treatment.To guarantee a level of pollution fixed on the outlet side of the sewer collector.To use Takagi-Sugeno fuzzy method for on line approximation of nonlinear functions.To implement the developed method for monitoring and control of the plant.

Fuzzy Model Predictive Control of DC-DC Converters

This paper presents a model predictive control (MPC) approach for buck-boost converter, a mathematical model is required to synthesis this controller, the typically used model is the averaged model, which describes the converter behavior on the operating point. Buck-boost converter has a nonlinear dynamic behavior; the Takagi–Sugeno (T–S) fuzzy model is used to represent the state-space model of nonlinear system where the consequent part of the fuzzy rule is replaced by linear systems.

Robust Predictive Control of a variable speed wind turbine using the LMI formalism

This paper proposes a Robust Fuzzy Multivariable Model Predictive Controller (RFMMPC) using Linear Matrix Inequalities (LMIs) formulation. The main idea is to solve at each time instant, an LMI optimization problem that incorporates input, output and Constrained Receding Horizon Predictive Control (CRHPC) constraints, and plant uncertainties, and guarantees certain robustness properties. The RFMMPC is easily designed by solving a convex optimization problem subject to LMI conditions. Then, the derived RFMMPC applied to a variable wind turbine with blade pitch and generator torque as two control inputs. The effectiveness of the proposed design is shown by simulation results.

The Pachycondyla Apicalis metaheuristic algorithm for parameters identification of chaotic electrical system

Abstract In this work, the Pachycondyla Apicalis metaheuristic algorithm (API) is used to identify and optimize control parameters for piezoelectric oscillator that exhibits frequency hysteresis behavior under strong excitation when asymmetric period which the bifurcation and chaotic behavior of higher harmonics appear by minimizing errors between actual and evaluated states of the model. In order to investigate the efficiency of the API algorithm, numerical experiments are carried out on the piezoelectric chaotic resonator. The simulation results indicate that the API algorithm can be effective in identifying the unknown parameters for given chaotic systems with high accuracy and low deviations.

LMI Approach of Constrained Fuzzy Model Predictive Control of DC-DC Boost Converter

In this paper, we propose a fuzzy model predictive control (FMPC) using Linear matrix inequalities (LMIs) approach for the voltage tracking control of a DC-DC Boost converter. A mathematical model is required to synthesis this controller, the typically used model is the averaged model, which describes the converter behavior on the operating point. Boost converter has a nonlinear dynamic behavior; the Takagi–Sugeno (T–S) fuzzy model is used to represent the state-space model of nonlinear system where the consequent part of the fuzzy rule is replaced by linear systems. Based on this model, we formulate and solve a constrained optimal control problem using linear matrix inequalities approach.

Observer Based Model Predictive Control of Hybrid Systems

In this paper, we are investigating how to adopting a hybrid optimal control law for minimizing the optimization time for hybrid system, which is used for simultaneous estimation of both systems; in case of falling one of them, the propose design allowed to process the system properly and stable, based on the estimated error dynamics which gives a robustness for the system against the uncertainties, faults and disturbances. The stability is guaranteed based on the Lyapunov function which expressed on terms of LMIs. The simulations results are show the effectiveness of proposed approach.

A Robust Model Predictive Control of a DC/DC Converter for a Solar Pumping System

A robust model predictive control approach using linear matrix inequality (LMI) is proposed for uncertain nonlinear systems. A simulation study on a PV Pumping System which comprises a PV generator, a buck DC-DC converter and a DC motor-pump is presented to evaluate the performance of the proposed controller. The LMI-based RMPC algorithm is currently under experimental stage and in near future we will publish the first results if they are satisfactory.

Model Predictive Control with Both States and Input Delays

This study investigates the problem of model predictive control (MPC) for active suspension systems with both states and input delays. The model uncertainty is assumed to be polytopic, and sufficient conditions are proposed in terms of linear-matrix inequalities (LMIs), which can be easily solved by an efficient convex optimization algorithm. The problem of minimizing an upper bound on the ‘worst-case’ performance objective function is reduced to a convex optimization involving linear matrix inequalities (LMIs). At each time set, a feasible state feedback is obtained by minimizing an upper bound of the ‘worst-case’ quadratic objective function over on infinite horizon subject on constraints on inputs. Finally, a quarter-vehicle model is exploited to demonstrate the effectiveness of the proposed method.