Fuzzy logic control was originally introduced and developed as a model free control design approach. However, it unfortunately suffers from criticism of lacking of systematic stability analysis and controller design though it has a great success in industry applications. In the past ten years or so, prevailing research efforts on fuzzy logic control have been devoted to model-based fuzzy control systems that guarantee not only stability but also performance of closed-loop fuzzy control systems. This paper presents a survey on recent developments (or state of the art) of analysis and design of model based fuzzy control systems. Attention will be focused on stability analysis and controller design based on the so-called Takagi-Sugeno fuzzy models or fuzzy dynamic models. Perspectives of model based fuzzy control in future are also discussed

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A Survey on Analysis and Design of Model-Based Fuzzy Control Systems

Observer-based fuzzy adaptive control for strict-feedback nonlinear systems

In this paper, an adaptive fuzzy output feedback control approach is proposed for single-input-single-output nonlinear systems without the measurements of the states. The nonlinear systems addressed in this paper are assumed to possess unmodeled dynamics in the presence of unstructured uncertainties and dynamic disturbances, where the unstructured uncertainties are not linearly parameterized, and no prior knowledge of their bounds are available. Fuzzy logic systems are used to approximate the unstructured uncertainties, and a state observer is developed to estimate the unmeasured states. By combining the backstepping technique with the small-gain approach, a stable adaptive fuzzy output feedback control method is proposed. It is shown that by applying the proposed adaptive fuzzy control approach, the closed-loop systems are semiglobally uniformly ultimately bounded. The effectiveness of the proposed approach is illustrated from simulation results.

A Combined Backstepping and Small-Gain Approach to Robust Adaptive Fuzzy Output Feedback Control

A robust adaptive fuzzy control approach is developed for a class of multi-input-multi-output (MIMO) nonlinear systems with modeling uncertainties and external disturbances by using both the approximation property of the fuzzy logic systems and the backstepping technique. The MIMO systems are composed of interconnected subsystems in the strict-feedback form. The main characteristics of the developed approach are that the online computation burden is alleviated and the robustness to dynamic uncertainties and external disturbances is improved. It is proven that all the signals of the resulting closed-loop system are uniformly bounded and that the tracking errors converge to a small neighborhood around zero. Two simulation experiments are presented to demonstrate the feasibility of the approach developed in this paper.

Robust Adaptive Tracking Control for Nonlinear Systems Based on Bounds of Fuzzy Approximation Parameters

Fuzzy adaptive observer backstepping control for MIMO nonlinear systems

This paper investigates a fuzzy model reference adaptive controller (FMRAC) for continuous-time multiple-input-multiple-output (MIMO) nonlinear systems. The proposed adaptive scheme uses a Takagi-Seguno (TS) fuzzy adaptive system, which allows for the inclusion of a priori information in terms of qualitative knowledge about the plant operating points or analytical regulators (e.g., state feedback) for those operating points. A proportional-integral update law is used to obtain a fast parameters adaptation. Stability and robustness of this adaptive scheme are established using Lyapunov stability tools. The simulation results, for a two-link robot, confirm the performance of the proposed approach.

Fuzzy model reference adaptive control

Stable indirect fuzzy adaptive control

This paper presents a fuzzy adaptive control suitable for motion control of multi-link robot manipulators with structured and unstructured uncertainties. When joint velocities are available, full state fuzzy adaptive feedback control is designed to ensure the stability of the closed loop dynamic. If the joint velocities are not measurable, an observer is introduced and an adaptive output feedback control is designed based on the estimated velocities. In the proposed control scheme, we need not derive the linear formulation of robot dynamic equation and tune the parameters. To reduce the number of fuzzy rules of the fuzzy controller, we consider the properties of robot dynamics and the decomposition of the uncertainties terms. The proposed controller is robust against uncertainties and external disturbance. Further, it is shown that required stability conditions, in both cases, can be formulated as LMI problems and solved using dedicated software. The validity of the control scheme is demonstrated by computer simulations on a two-link robot manipulator.

Observer-based adaptive control of robot manipulators: Fuzzy systems approach

This paper shows that a predictive control (PC) combined with the principle of direct torque control (DTC) leads to an excellent dynamic behavior of the permanent magnets synchronous machine (PMSM) and mitigates the drawbacks of the conventional DTC. In this paper, a predictive direct torque control (PDTC) of a PMSM based on classical PI regulator and particle swarm optimization (PSO) algorithm is developed. The PSO adjusts the parameters of the PI controller, which improves its adjustive capability. The effectiveness of the proposed PDTC based a PSO controller is then compared with that of the conventional PI controller. The numerical simulation is performed using MATLAB-Simulink and SimPowerSystem Toolbox. The main contribution of this work is the implementation of the proposed controller on a test bench around a dSPACE 1104. Simulation and experimental results are presented in order to demonstrate the effectiveness of the proposed techniques. Besides, the system associated with these techniques reduces effectively the flux and the torque ripples with better dynamic and steady state performance. The results with the PSO show more performances and a considerable reduction in torque and flux ripples than the results with the conventional PI.

Predictive DTC schemes with PI regulator and particle swarm optimization for PMSM drive: comparative simulation and experimental study

This paper presents the design of a new adaptive control of permanent magnet synchronous motor (PMSM). The used Model reference (MRAC) adaptive control technique permits systematic and transparent design of the speed and currents control loops. The resulting system shows robust performance against parametric variations and disturbances. The control scheme is assessed through simulation study to demonstrate the system performances under various operating conditions.

Noureddine Goléa

Papers

Fuzzy model reference adaptive control

Stable indirect fuzzy adaptive control

Observer-based adaptive control of robot manipulators: Fuzzy systems approach

Predictive DTC schemes with PI regulator and particle swarm optimization for PMSM drive: comparative simulation and experimental study

Robust MRAC Adaptive Control of PMSM drive under General Parameters Uncertainties