Faouzi M'Sahli

High gain observer based on-line rotor and stator resistances estimation for IMs

One of the main challenging issues in induction motor drives is the lack of knowledge about the actual values of some critical parameters, such as rotor and stator resistances which are subject to large variations during operation. Such problem is difficult to resolve due to the strong interconnection between states and parameters in the nonlinear motor model, besides the unavailability of both rotor flux and load torque. In the spirit to accurately follow the on-line machine variables, this paper focuses on the simultaneous estimation of internal states and time varying parameters. Especially, a new identification scheme for rotor resistance and/or stator resistance is introduced. In the aim of decoupling the unknown electrical parameters, we adopt a mild change of coordinates that allows to easily design a two-stage of high gain observer. The simplicity of presented procedures and the efficiently for real time computation constitute both main features of the proposed approach. Moreover, possible exploitation of our algorithm in the fault detection issue is discussed through a simulation of an abrupt rotor short-circuit.

Adaptive control of nonlinear system using neuro-fuzzy learning by PSO algorithm

This paper proposes the optimization of parameters of neuro-fuzzy system using the particle swarm optimization. Neuro-fuzzy techniques have emerged from the fusion of neural networks and fuzzy inference systems. They could serve as a powerful tool for system modeling and control. These fuzzy systems are optimized by adapting the antecedent and consequent parameters. Among them, the ANFIS use the least square to optimize the consequent parameters and retropropagation to train the antecedent parameters. Several learning algorithms of fuzzy models have been proposed, e.g. evolutionary algorithms, such as particle swarm optimization. These different methods have been developed to learn the parameters of neuro-fuzzy system and to test them in the on-line control of nonlinear system.

Observer-Based Backstepping Controller for a State-Coupled Two-Tank System

ABSTRACT This paper addresses the nonlinear control design problem for a state-coupled two-tank liquid level system. For this systems dynamics, motivated by a desire to provide precise and economic liquid level control, a new output feedback control scheme is developed. In the proposed approach, a robust controller based on backstepping strategy is designed in order to ensure globally asymptotically stabilisation for a particular class of second-order nonlinear systems. Moreover, a model-based backstepping controller combined with a high gain observer is designed for the two-tank, liquid level system. Only one sensor is available to measure the liquid level in the bottom tank and the other processs state is assumed unavailable. The observer converges in a finite time and leads to good estimate of the liquid level in the top tank as well as a good track of the liquid level in the bottom tank with the reference trajectory. To highlight the efficiency and applicability of the proposed control scheme, a comparison with proportional plus integral plus derivative controller as well as a simulation and experimental results are provided.

A set of observers design to a quadruple tank process

In this paper, quadruple tank process is exploited to study different type of nonlinear observer. Indeed, from the physical model of the process, and with liquid levels measurement in the bottom of two tanks, we search to reconstruct liquid levels of the two upper ones. Three approaches are analyzed namely extended Kalman filter (EKF), high gain observer (HGO) and high gain-extended Kalman filter (HG-EKF). The behavior of estimated state, in the presence of disturbance, are provided to appropriate the feature of every observer.

A new stability analysis and stabilization of uncertain switched linear systems based on vector norms approach

In the present paper a new stability analysis and stabilization of continuous-time uncertain switched linear systems is considered. This approach is based on the comparison, the overvaluing principle, the application of Borne-Gentina criterion and the Kotelyanski conditions. The stability conditions issued from vector norms correspond to a vector Lyapunov function. Indeed, the switched system to be controlled will be represented in the Companion form. A comparison system relative to regular vector norms is used in order to get the simple arrow form of the state matrix that yields to a suitable use of Borne-Gentina criterion for the establishment of sufficient conditions as function of the uncertain parameters for global asymptotic stability.

Output Feedback Nonlinear GPC Using High Gain Observer

In this paper, Nonlinear Generalized Predictive Control (NGPC), proposed by Chen [4] in continuous time, is reformulated into a quadratic optimization problem in order to take into account constraints, and this approach is applied to a particular class of nonlinear systems. The states which are assumed available will be estimated in this work by a high gain observer (HGO). The closed loop dynamics, under combination of predictive controller and observer synthesis, is shown transparent to the designers, and merely manipulated in the sense that output feedback controller explicitly depend on choice of only two parameters (prediction time and high gain parameter). For a nonlinear example of considered class, we show satisfactory performances and robustness in presence of disturbance.

Output feedback predictive control for hydrographic process

This paper introduces a new output feedback control scheme, which combine nonlinear generalized predictive control (NGPC) with a high gain observer (HGO). We consider the input affine nonlinear MIMO class of systems, that include the model of our hydrographic process. Indeed, from the liquid levels measurements in both bottoms tanks, we reconstruct the levels of both upper ones, and we use them in the state feedback control loop. The predictive control law synthesized is an explicit continuous solution of optimization problem, it arise from receding horizon index minimization. As it is known, to deal with the separation principle requirement, we need to lead the system in the new state coordinate and to impose control constraints. This last can inherently taking into account by the predictive controller, when we reformulate control law through the on-line solution of quadratic programming (QP) problem.

APPLICATION OF ADAPTIVE CONTROLLERS FOR THE TEMPERATURE CONTROL OF A SEMI-BATCH REACTOR

This paper investigates experimentally the application of several adaptive control algorithms for the control of a semi-batch chemical reactor. These algorithms combine parameter estimation algorithms and conventional control design methods to update the coefficients of the control algorithm. To make practical use of these control strategies, simple computational procedures are presented. The control objective is to keep the reactor temperature within safe operating specifications by manipulating the electrical heating power. Experimental results demonstrate that this class of adaptive controllers works well in the presence of large time delay, hard constraints and disturbances.

FPGA implementation of high gain observer for induction machine using Simulink HDL coder

This paper focus on the implementation in Field Programmable Gate Arrays (FPGA) of high gain observer (HGO) applied to induction machine. We use the evaluated board Spartant3E with Xc3s500 FPGA for this purpose. The three phases supply and its corresponding pulse width modulation (PWM) are softly generated. Then, with only the both stator currents measurements, an estimation of rotor flux, mechanical speed achieved. Due to the limitation resources of Xc3s500, we have choice a reduced size format of the internals variables as well as the software algorithm can give an acceptable signals with moderate ripples. We have highlight different ways to obtain the implementation of complex functions inherent of the HGO. With the flexibility of our method, other algorithm related to observation or advanced control law can easily embedded into the FPGA.

Sensorless speed and flux control scheme for an Induction Motor with an adaptive backstepping observer

This paper deals with the synthesis of an adaptive nonlinear backstepping-observer for sensorless speed and flux backstepping control of Induction Motor (IM) drive with online rotor resistance adaptation. The backstepping control purpose is based on stability analysis established from Lyapunov theory. This approach takes system nonlinearities into account in the control system design stage. Controlled rotor flux and speed are given from an adaptive backstepping observer. The effectiveness of this strategy has been successfully verified through computer simulations in terms of the reference tracking ability and the robustness against parameters variation.