Soufiene Bouallègue

PID-type fuzzy logic controller tuning based on particle swarm optimization

In this paper, a new PID-type fuzzy logic controller (FLC) tuning strategy is proposed using a particle swarm optimization (PSO) approach. In order to improve further the performance and robustness properties of the proposed PID-fuzzy approach, two self-tuning mechanisms are introduced. The scaling factors tuning problem of these PID-type FLC structures is formulated and systematically resolved, using a proposed constrained PSO algorithm. The case of an electrical DC drive benchmark is investigated, within a developed real-time framework, to illustrate the efficiency and superiority of the proposed PSO-based fuzzy control approaches. Simulation and experimental results show the advantages of the designed PSO-tuned PID-type FLC structures in terms of efficiency and robustness.

Particle Swarm Optimization-based design of polynomial RST controllers

In this paper, we propose a new method for digital RST controller design based on the Particle Swarm Optimization metaheuristic. It is a systematic RST synthesis and tuning procedure to deal with the complexity of the known classical poles placement methods. The case of an electric DC drive benchmark has been successfully obtained to illustrate the efficiency of the proposed PSO-based RST control approach. Simulation results show the advantages of the designed PSO-tuned RST structure in terms of performance and robustness. A comparison with the well known Genetic Algorithm Optimization technique is investigated in order to show the superiority and effectiveness of the proposed PSO-based approach.

DESIGN OF FUZZY FLATNESS-BASED CONTROLLER FOR A DC DRIVE

In this paper, a combined approach associating the flatness principle to the fuzzy logic control techniques is proposed and applied to a DC drive supplied by an AC―DC power converter. The basic of this control strategy consists in planning a reference trajectory using the flatness property of linear systems. The desired trajectory of the output process will be expressed by the flat output reference trajectory. After the planning step, the tracking of the reference trajectory is ensured by a PID-type fuzzy controller. The output static disturbance rejection, the tracking performances robustness under plant parameters variation and the limitation of the control signal magnitude applied to the system are improved. The hardware and software control requirements are respectively developed and the proposed control strategy is illustrated by experimental trials in real-time framework.

Structured mixed-sensitivity H ∞ design using Particle Swarm Optimization

In this paper, a new structured mixed-sensitivity ℋ∞ design approach, using Particle Swarm Optimization (PSO) technique, is proposed. The case study of an electrical DC drive benchmark is adopted to illustrate the efficiency and viability of the proposed control approach. The optimization based synthesis problem is formulated and solved by a constrained PSO algorithm. In the proposed control strategy, a PID controllers structure is adopted. Simulations and experimental results show the advantages of simple structure, lower order and robustness of the proposed controller. A comparison to another similar evolutionary algorithm, such as Genetic Algorithm (GA), shows the superiority of the PSO-based method to solve the formulated optimization problem.

On a robust real-time H∞ controller design for an electrical drive

In this paper, a robust H∞ controller is designed and applied to a DC motor, as an electrical drive. In H∞ mixed sensitivity framework, several satisfying results, mainly in terms of tracking trajectories, control signal moderation, disturbance rejection as well as robustness stability in the case of neglected dynamics uncertainty, were obtained using a four-block criterion design structure. The proposed H∞ controller algorithm was successfully implemented and tested in the real-time framework using a multi-function data acquisition PCI-1710 board.

Robust H ∞ controller design for a DC motor supplied by an AC-DC power converter

In this paper, a robust H∞ controller is designed and applied to a DC motor in speed control framework. The performance and robustness aspects of controlled system related to modelling uncertainties and external disturbances are the particular attention. In a frequency formalism, the recourse to the sensitivity functions, as tool of study, leads to develop frequency templates translating the performance and robustness concepts. The H∞ synthesis of the controller satisfying the obtained templates is designed while using the Ricatti equations based-method. The robustness of the obtained controlled system will be examined by an unstructured analysis. In order to validate the proposed controller algorithm within a speed control application, the obtained controller was tested extensively in both simulation and hardware cases.

A PID type fuzzy controller design with flatness-based planning trajectory for a DC drive

In this paper, a combined approach of a designed controller based on flatness and fuzzy logic techniques control is proposed. The basic of this control strategy consists in planning a reference trajectory using the flatness property of linear systems. The real output desired trajectory of process will be expressed by the flat output reference trajectory. After the planning phase, the tracking of the reference trajectory is ensured by a PID type fuzzy controller. Flatness and fuzzy logic lead to design a controller with high performance in terms of tracking and robustness. The rejection of a static disturbance on the output signal and the limitation of the control signal magnitude applied to the system will be improved while introducing a self-tuning controller parameters. The considered control approach is applied to a linear model of a DC motor supplied by an AC-DC power converter.

Structured loop-shaping ℋ ∞ controller design using Particle Swarm Optimization

In this paper, a structured loop-shaping ℋ∞ controller design, using Particle Swarm Optimization (PSO) technique, is proposed. The case study of an electrical DC drive benchmark is envisaged to illustrate the efficiency and viability of the proposed control strategy. The optimization-based synthesis problem is formulated and solved by a constrained PSO algorithm. In the proposed approach, a PID controllers structure is adopted. Simulations and experimental results show the advantages of simple structure, lower order and robustness of the proposed controller in relation to the standard design case. A comparison to another similar evolutionary algorithm, such as Genetic Algorithm (GA), shows the superiority of the PSO-based method to solve the optimization-based control design problem.

Multi-Layer Artificial Neural Networks Based MPPT-Pitch Angle Control of a Tidal Stream Generator

Artificial intelligence technologies are widely investigated as a promising technique for tackling complex and ill-defined problems. In this context, artificial neural networks methodology has been considered as an effective tool to handle renewable energy systems. Thereby, the use of Tidal Stream Generator (TSG) systems aim to provide clean and reliable electrical power. However, the power captured from tidal currents is highly disturbed due to the swell effect and the periodicity of the tidal current phenomenon. In order to improve the quality of the generated power, this paper focuses on the power smoothing control. For this purpose, a novel Artificial Neural Network (ANN) is investigated and implemented to provide the proper rotational speed reference and the blade pitch angle. The ANN supervisor adequately switches the system in variable speed and power limitation modes. In order to recover the maximum power from the tides, a rotational speed control is applied to the rotor side converter following the Maximum Power Point Tracking (MPPT) generated from the ANN block. In case of strong tidal currents, a pitch angle control is set based on the ANN approach to keep the system operating within safe limits. Two study cases were performed to test the performance of the output power. Simulation results demonstrate that the implemented control strategies achieve a smoothed generated power in the case of swell disturbances.

Advanced Metaheuristics-Based Approach for Fuzzy Control Systems Tuning

In this study, a new advanced metaheuristics-based optimization approach is proposed and successfully applied to design and tuning of a PID-type Fuzzy Logic Controller (FLC). The scaling factors tuning problem of the FLC structure is formulated and systematically resolved, using various constrained metaheuristics such as the Differential Search Algorithm (DSA), Gravitational Search Algorithm (GSA), Artificial Bee Colony (ABC) and Particle Swarm Optimization (PSO). In order to specify more time-domain performance control objectives of the proposed metaheuristics-tuned PID-type FLC, different optimization criteria such as Integral of Square Error (ISE) and Maximum Overshoot (MO) are considered and compared The classical Genetic Algorithm Optimization (GAO) method is also used as a reference tool to measure the statistical performances of the proposed methods. All these algorithms are implemented and analyzed in order to show the superiority and the effectiveness of the proposed fuzzy control tuning approach. Simulation and real-time experimental results, for an electrical DC drive benchmark, show the advantages of the proposed metaheuristics-tuned PID-type fuzzy control structure in terms of performance and robustness.