Adaptive complementary sliding-mode control for thrust active magnetic bearing system

An adaptive complementary sliding-mode control (ACSMC) system with a multi-input-multi-output (MIMO) recurrent Hermite neural network (RHNN) estimator is proposed to control the position of the rotor in the axial direction of a thrust active magnetic bearing (TAMB) system for the tracking of various reference trajectories in this study. First, the operating principles and dynamic model of the TAMB system using a linearized electromagnetic force model is derived. Then, a conventional sliding-mode control (SMC) system is designed for the tracking of various reference trajectories. Moreover, a complementary sliding-mode control (CSMC) system is adopted to reduce the guaranteed ultimate bound of the tracking error by half while using the saturation function as compared with the SMC. Furthermore, since the system parameters and the external disturbance are highly nonlinear and time-varying, the ACSMC is proposed to further improve the control performance and increase the robustness of the TAMB system. In the ACSMC, the MIMO RHNN estimator with estimation laws is proposed to estimate two complicated dynamic functions of the system on-line. In addition, a robust compensator is proposed to confront the minimum approximated errors and achieve the robustness. Finally, some experimental results for the tracking of various reference trajectories show that the control performance of the ACSMC is significantly improved comparing with the SMC and CSMC.     

Robust H/sub /spl infin// controller design with recurrent neural network for linear synchronous motor drive

In this paper, a robust controller design with H/sub /spl infin// performance using a recurrent neural network (RNN) is proposed for the position tracking control of a permanent-magnet linear synchronous motor. The proposed robust H/sub /spl infin// controller, which comprises a RNN and a compensating control, is developed to reduce the influence of parameter variations and external disturbance on system performance. The RNN is adopted to estimate the dynamics of the lumped plant uncertainty, and the compensating controller is used to eliminate the effect of the higher order terms in Taylor series expansion of the minimum approximation error. The tracking performance is ensured in face of parameter variations, external disturbance and RNN estimation error once a prespecified H/sub /spl infin// performance requirement is achieved. The synthesis of the RNN training rules and compensating control are based on the solution of a nonlinear H/sub /spl infin// control problem corresponding to the desired H/sub /spl infin// performance requirement, which is solved via a choice of quadratic storage function. The proposed control method is able to track both the periodic step and sinusoidal commands with improved performance in face of large parameter perturbations and external disturbance.     

A hybrid computed torque controller using fuzzy neural network formotor-quick-return servo mechanism

The dynamic response of a hybrid computed torque controlled quick-return mechanism, which is driven by a permanent magnet (PM) synchronous servo motor, is described in this paper. The crank and disk of the quick-return mechanism are assumed to be rigid. First, Hamilton's principle and Lagrange multiplier method are applied to formulate the mathematical model of motion. Then, based on the principle of computed torque control, a position controller is designed to control the position of a slider of the motor-quick-return servo mechanism. In addition, to relax the requirement of the lumped uncertainty in the design of a computed torque controller, a fuzzy neural network (FNN) uncertainty observer is utilized to adapt the lumped uncertainty online. Moreover, a hybrid control system, which combines the computed torque controller, the FNN uncertainty observer, and a compensated controller, is developed based on Lyapunov stability to control the motor-quick-return servo mechanism. The computed torque controller with FNN uncertainty observer is the main tracking controller, and the compensated controller is designed to compensate the minimum approximation error of the uncertainty observer instead of increasing the rule numbers of the FNN. Finally, simulated and experimental results due to periodic step and sinusoidal commands show that the dynamic behaviors of the proposed hybrid computed torque control system are robust with regard to parametric variations and external disturbances     

Decoupled stator-flux-oriented induction motor drive with fuzzyneural network uncertainty observer

A stator-flux-oriented induction motor drive using online rotor time-constant estimation with a robust speed controller is introduced in this paper. The estimation of the rotor time constant is made on the basis of the model reference adaptive system using an energy function. The estimated rotor time-constant is used in the current-decoupled controller, which is designed to decouple the torque and flux in the stator-flux-field-oriented control. Moreover, a robust speed controller, which is comprised of an integral-proportional speed controller and a fuzzy neural network uncertainty observer, is designed to increase the robustness of the speed control loop. The effectiveness of the proposed control scheme is demonstrated by simulation and experimental results     

Fuzzy channel-optimized vector quantization

A novel fuzzy clustering algorithm for the design of channel-optimized source coding systems is presented in this letter. The algorithm, termed fuzzy channel-optimized vector quantizer (FCOVQ) design algorithm, optimizes the vector quantizer (VQ) design using a fuzzy clustering process in which the index crossover probabilities imposed by a noisy channel are taken into account. The fuzzy clustering process effectively enhances the robustness of the performance of VQ to channel noise without reducing the quantization accuracy. Numerical results demonstrate that the FCOVQ algorithm outperforms existing VQ algorithms under noisy channel conditions for both Gauss-Markov sources and still image data     

Fuzzy neural networks for identification and control of ultrasonicmotor drive with LLCC resonant technique

This paper demonstrates the applications of fuzzy neural networks (FNNs) in the identification and control of the ultrasonic motor (USM). First, the USM is derived by a newly designed high-frequency two-phase voltage-source inverter using LLCC resonant technique. Then, two FNNs with varied learning rates are proposed to control the rotor position of the USM. The USM drive system is identified by a fuzzy neural network identifier (FNNI) to provide the sensitivity information of the drive system to a fuzzy neural network controller (FNNC). A backpropagation algorithm is used to train both the FNNI and FNNC on-line. Moreover, to guarantee the convergence of identification and tracking errors, analytical methods based on a discrete-type Lyapunov function are proposed to determine the varied learning rates of the FNNs. In addition, the effectiveness of the FNN-controlled USM drive system is demonstrated by experimental results. Accurate tracking response can be obtained due to the powerful on-line learning capability of the FNNs. Furthermore, the influence of parameter variations and external disturbances on the USM drive system can be reduced effectively     

Robust fuzzy neural network control for linear ceramic motor drivevia backstepping design technique

This study presents a robust fuzzy-neural-network (RFNN) control system for a linear ceramic motor (LCM) that is driven by an unipolar switching full-bridge voltage source inverter using LC resonant technique. The structure and operating principle of the LCM are introduced. Since the dynamic characteristics and motor parameters of the LCM are nonlinear and time varying, a RFNN control system is designed based on the hypothetical dynamic model to achieve high-precision position control via the backstepping design technique. In the RFNN control system a fuzzy neural network (FNN) controller is used to learn an ideal feedback linearization control law, and a robust controller is designed to compensate the shortcoming of the FNN controller. All adaptive learning algorithms in the RFNN control system are derived from the sense of Lyapunov stability analysis, so that system-tracking stability can be guaranteed in the closed-loop system. The effectiveness of the proposed RFNN control system is verified by experimental results in the presence of uncertainties. In addition, the advantages of the proposed control system are indicated in comparison with the traditional integral-proportional (IP) position control system     

Adaptive wavelet neural network control with hysteresis estimation for piezo-positioning mechanism

An adaptive wavelet neural network (AWNN) control with hysteresis estimation is proposed in this study to improve the control performance of a piezo-positioning mechanism, which is always severely deteriorated due to hysteresis effect. First, the control system configuration of the piezo-positioning mechanism is introduced. Then, a new hysteretic model by integrating a modified hysteresis friction force function is proposed to represent the dynamics of the overall piezo-positioning mechanism. According to this developed dynamics, an AWNN controller with hysteresis estimation is proposed. In the proposed AWNN controller, a wavelet neural network (WNN) with accurate approximation capability is employed to approximate the part of the unknown function in the proposed dynamics of the piezo-positioning mechanism, and a robust compensator is proposed to confront the lumped uncertainty that comprises the inevitable approximation errors due to finite number of wavelet basis functions and disturbances, optimal parameter vectors, and higher order terms in Taylor series. Moreover, adaptive learning algorithms for the online learning of the parameters of the WNN are derived based on the Lyapunov stability theorem. Finally, the command tracking performance and the robustness to external load disturbance of the proposed AWNN control system are illustrated by some experimental results.