Hicham Chaoui

Adaptive Fuzzy Logic Control of Permanent Magnet Synchronous Machines With Nonlinear Friction

In this paper, an adaptive fuzzy control scheme is introduced for permanent magnet synchronous machines (PMSMs). The adaptive control strategy consists of a Lyapunov stability-based fuzzy speed controller that capitalizes on the machines inverse model to achieve accurate tracking with unknown nonlinear system dynamics. As such, robustness to modeling and parametric uncertainties is achieved. Moreover, no explicit currents loop regulation is needed, which simplifies the control structure and unlike other control strategies, no a priori offline training, weights initialization, parameters knowledge, voltage, or current transducer is required. The systems convergence and stability are proved by Lyapunov stability theory, which yields an improved performance. Simulation results for different situations highlight the performance of the proposed controller in transient, steady-state, and standstill conditions. Furthermore, the adaptive fuzzy systems inherent parallelism makes them a good candidate for implementation in real-time PMSM drive systems.

ANN-Based Adaptive Control of Robotic Manipulators With Friction and Joint Elasticity

This paper proposes a control strategy based on artificial neural networks (ANNs) for a positioning system with a flexible transmission element, taking into account Coulomb friction for both motor and load, and using a variable learning rate for adaptation to parameter changes and accelerate convergence. A control structure consists of a feedforward ANN that approximates the manipulators inverse dynamical model, an ANN feedback control law, a reference model, and the adaptation process of the ANNs with a variable learning rate. A supervisor that adapts the neural networks learning rate and a rule-based supervisor for online adaptation of the parameters of the reference model are proposed to maintain the stability of the system for large variations of load parameters. Simulation results highlight the performance of the controller to compensate the nonlinear friction terms, particularly Coulomb friction, and flexibility, and its robustness to the load and drive motor inertia parameter changes. Internal stability, which is a potential problem in such a system, is also verified. The controller is suitable for DSP and very large scale integration implementation and can be used to improve static and dynamic performances of electromechanical systems.

Type-2 Fuzzy Logic Control of a Flexible-Joint Manipulator

A type-2 fuzzy logic controller (FLC) is proposed in this article for robot manipulators with joint elasticity and structured and unstructured dynamical uncertainties. The proposed controller is based on a sliding mode control strategy. To enhance its real-time performance, simplified interval fuzzy sets are used. The efficiency of the control scheme is further enhanced by using computationally inexpensive input signals independently of the noisy torque and acceleration signals, and by adopting a trade off strategy between the manipulator’s position and the actuators’ internal stability. The controller is validated through a set of numerical experiments and by comparing it against its type-1 counterpart. It is shown through these experiments the higher performance of the type-2 FLC in compensating for larger magnitudes of uncertainties with severe nonlinearities.

Lyapunov-Based Adaptive State of Charge and State of Health Estimation for Lithium-Ion Batteries

This paper presents an adaptive state of charge (SOC) and state of health (SOH) estimation technique for lithium-ion batteries. The adaptive strategy estimates online parameters of the battery model using a Lyapunov-based adaptation law. Therefore, the adaptive observer stability is guaranteed by Lyapunovs direct method. Since no a priori knowledge of battery parameters is required, accurate estimation is still achieved, although parameters change due to aging or other factors. Unlike other estimation strategies, only battery terminal voltage and current measurements are required. Simulation and experimental results highlight the high SOC and SOH accuracy estimation of the proposed technique.

Adaptive Lyapunov-based neural network sensorless control of permanent magnet synchronous machines

In this paper, an adaptive neural network sensorless control scheme is introduced for permanent magnet synchronous machines (PMSMs). The control strategy consists of an adaptive speed controller that capitalizes on the machine’s inverse model to achieve accurate tracking, two artificial neural networks (ANNs) for currents control, and an ANN-based observer for speed estimation to overcome the drawback associated with the use of mechanical sensors while the rotor position is obtained by the estimated rotor speed direct integration to reduce the effect of the system noise. A Lyapunov stability-based ANN learning technique is also proposed to insure the ANNs’ convergence and stability. Unlike other sensorless control strategies, no a priori offline training, weights initialization, voltage transducer, or mechanical parameters knowledge is required. Results for different situations highlight the performance of the proposed controller in transient, steady-state, and standstill conditions.

Hybrid Neural Fuzzy Sliding Mode Control of Flexible-Joint Manipulators with Unknown Dynamics

In this paper, a hybrid neural fuzzy control scheme is proposed for the control of flexible-joint robot manipulators with unknown dynamics. The control strategy is based on a feedforward artificial neural network to partially approximate the manipulators inverse dynamics. A fuzzy sliding mode feedback controller is also used for the online adaptation of the neural network-based controller. Simulation results of various scenarios highlight the performance and stability of the proposed controller in compensating for the highly nonlinear unknown dynamics of the manipulator under different dynamical conditions and external disturbances

Reference model supervisory loop for neural network based adaptive control of a flexible joint with hard nonlinearities

We propose an artificial neural network based adaptive controller for a positioning system with a flexible transmission element, taking into account hard nonlinearities in the motor and load models. A feedforward compensation module (ANN/sub FF/) learns the approximate inverse dynamics of the system and a feedback controller (ANN/sub FBK/) compensates for residual errors. The error at the output of a reference model, which defines the desired error dynamics, and the output of ANN/sub FBK/ are respectively used as the error signal for adaptation of ANN/sub FBK/ and ANN/sub FF/. The contribution of the paper is to propose a rule based supervisor for online adaptation of the parameters of the reference model to maintain stability of the system for large variations of load parameters. The controller is suitable for DSP and VLSI implementation and can be used to improve static and dynamic performance of electromechanical systems.

State-of-Charge and State-of-Health Lithium-Ion Batteries’ Diagnosis According to Surface Temperature Variation

This paper presents a hybrid state-of-charge (SOC) and state-of-health (SOH) estimation technique for lithium-ion batteries according to surface temperature variation (STV). The hybrid approach uses an adaptive observer to estimate the SOH while an extended Kalman filter (EKF) is used to predict the SOC. Unlike other estimation methods, the closed-loop estimation strategy takes into account the STV and its stability is guaranteed by Lyapunov direct method. In order to validate the proposed method, experiments have been carried out under different operating temperature conditions and various discharge currents. Results highlight the effectiveness of the approach in estimating SOC and SOH for different aging conditions.

Accurate state of charge (SOC) estimation for batteries using a reduced-order observer

In this paper, we introduce an observer-based state of charge (SOC) estimator for batteries. The estimation strategy is based on the batterys state space model. This representation makes stability analysis easier for the proposed estimation technique. Unlike other estimation strategies, only battery terminal voltage and current measurements are required. Simulation results highlight the performance of the proposed estimator in determining the SOC with high accuracy. Robustness to uncertainties such as parameter variations and battery aging effects is also verified.

Neural network modeling of cold-gas thrusters for a spacecraft formation flying test-bed

This work presents a neural network based modeling strategy to precisely identify the thrusts of cold-gas thrusters deployed in a nano-satellite experimental test-bed developed at the Canadian Space Agency (CSA). Eight thrusters are used to control the planar motion of an emulated free-floating spacecraft supported by air-bearing. Calibration experiments conducted on these thrusters revealed that the generated thrusts are highly nonlinear with respect to their inputs, the digital openings and the air pressure. Motivated by the learning and approximation capabilities of artificial neural networks (ANNs), an ANN is used to model the nonlinear thruster behavior using experimental data. The performance of the proposed strategy is satisfactory and clearly demonstrated by the resulting high precision model.