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The dynamic response of an adaptive fuzzy neural network (FNN) controlled quick-return mechanism, which is driven by a permanent magnet (PM) synchronous servo motor, is described in this study. 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, an adaptive controller is developed to control the position of a slider of the quick-return servomechanism. Moreover, since the selection of control gain of the adaptive controller has a significant effect on the system performance, an adaptive FNN controller is proposed to control the quick-return servomechanism. In the proposed adaptive FNN controller, an FNN is adopted to facilitate the adjustment of control gain on line. Simulated and experimental results due to periodic step and sinusoidal commands show that the dynamic behavior of the proposed adaptive FNN control system are robust with regard to parametric variations and external disturbances. [DOI: 10.1115/1.1368113]

The dynamic response of a sliding-mode-controlled slider-crank mechanism, which is driven by a permanent-magnet (PM) synchronous servo motor, is studied in this paper. First, a position controller is developed based on the principles of sliding-mode control. Moreover, to relax the requirement of the bound of uncertainties in the design of a sliding-mode controller, a fuzzy neural network (FNN) sliding-mode controller is investigated, in which an FNN is adopted to adjust the control gain in a switching control law on line to satisfy the sliding mode condition. In addition, to guarantee the convergence of tracking error, analytical methods based on a discrete-type Lyapunov function are proposed to determine the varied learning rates of the FNN. Numerical and experimental results show that the dynamic behaviors of the proposed controller-motor-mechanism system are robust with regard to parametric variations and external disturbances. Furthermore, compared with the sliding-mode controller, smaller control effort results and the chattering phenomenon is much reduced by the proposed FNN sliding-mode controller. Index Terms--Fuzzy neural network, permanent-magnet synchronous servo motor, slider-crank mechanism, sliding-mode control, varied learning rates.

A novel designed hybrid supervisory control system using a recurrent fuzzy neural network (RFNN) is proposed to control the mover of a permanent magnet linear synchronous motor (PMLSM) servo drive for the tracking of periodic reference inputs in this study. First, the field-oriented mechanism is applied to formulate the dynamic equation of the PMLSM. Then, a hybrid supervisory control system, which combines a supervisory control system and an intelligent control system, is proposed to control the mover of the PMLSM for periodic motion. The supervisory control law is designed based on the uncertainty bounds of the controlled system to stabilize the system states around a predefined bound region. Since the supervisory control law will induce excessive and chattering control effort, the intelligent control system is introduced to smooth and reduce the control effort when the system states are inside the predefined bound region. In the intelligent control system, the RFNN control is the main tracking controller which is used to mimic a idea control law, and a compensated control is proposed to compensate the difference between the idea control law and the RFNN control. The RFNN has the merits of fuzzy inference, dynamic mapping and fast convergence speed. In addition, an online parameter training methodology, which is derived using the Lyapunov stability theorem and the gradient descent method, is proposed to increase the learning capability of the RFNN. The proposed hybrid supervisory control system using RFNN can track various periodic reference inputs effectively with robust control performance. Index Terms--Field-oriented mechanism, hybrid supervisory control, intelligent control, permanent magnet linear synchronous motor (PMLSM), recurrent fuzzy neural network (RFNN).

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. Index Terms--Fuzzy neural network uncertainty observer, integral-proportional speed controller, rotor time-constant estimation, stator-flux-oriented control.

This paper investigates the problem of reachable set estimation and decentralized controller design for large-scale nonlinear systems, where time-varying delays and nonlinearities appear in interconnections connected to other subsystems. The Takagi–Sugeno model is used to describe each nonlinear subsystem. The aim of this paper is to design a decentralized state-feedback fuzzy controller such that the reachable set of the resulting closed-loop system with input constraint is bounded by an intersection of ellipsoids. First, a model transformation is proposed to reformulate the closed-loop system as several feedback interconnections with extra inputs and outputs. Then, based on a combined application of the Lyapunov–Krasovskii functional and the scaled small-gain theorem, the input–output approach is developed for the reachable set estimation and synthesis. Several conditions for the existence of a decentralized state-feedback fuzzy controller that ensures an ellipsoidal bound of reachable sets for the closed-loop system with input constraint are derived in terms of linear matrix inequalities. Finally, two numerical examples are given to validate the effectiveness of the proposed strategy. [ABSTRACT FROM AUTHOR]