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In this paper, an adaptive controller is developed to suppress chaos and track the desired speed in an uncertain chaotic permanent magnet synchronous motor (PMSM) drive system. The controller consists of computational and supervisory control schemes. The computational controller, based on fuzzy neural networks, is used to approximate the unknown nonlinear control signal, while the supervisory controller is employed to attenuate the approximation error effects of the neural network and ensure the system is robust. Simulation results demonstrate that the proposed controller can successfully quash chaotic oscillation in a PMSM and allow speeds to follow the desired trajectory despite the existence of uncertainties. [ABSTRACT FROM PUBLISHER]

In this paper, a new concept of quasi sliding mode control (QSMC) is introduced for the tracking control of chaotic symmetric gyros with linear-plus-cubic damping. In contrast to previous works on sliding mode control, based on the proposed QSMC, the chattering phenomenon can be eliminated. Furthermore, the controlled gyro system can track desired trajectories and tracking errors can be driven into a predictable neighborhood of zero, even when the input nonlinearity is present. Numerical simulations are provided to demonstrate the effectiveness of the proposed QSMC design method. [ABSTRACT FROM PUBLISHER]

The problem of generalized projective synchronization for unified chaotic systems coupled with sector nonlinear inputs is investigated. Using the sliding mode control technique, a control scheme containing a new proportional–integral switching surface is developed to guarantee generalized projective synchronization even when the uncertainties and sector nonlinearity are present. Numerical simulations are included to demonstrate the effectiveness of the proposed synchronization scheme. [ABSTRACT FROM PUBLISHER]