An adaptive discrete-time controller is developed for a class of practical plants when the mathematical model is unknown and the sampling time is nonconstant or unfixed. The data-driven model is established by the set of plant's input–output data under the pseudo-partial derivative (PPD) which represents the change of output with respect to the change of control effort. The multi-input fuzzy rule emulated network (MiFREN) is utilized to estimate PPD with an online-learning phase to tune all adjustable parameters of MiFREN with the convergence analysis. The proposed control law is developed by the discrete-time sliding mode control (DSMC), and the time-varying band is established according to the unfixed sampling time and unknown boundaries of disturbances and uncertainties. The prototype of direct current-motor current control with uncontrolled-sampling time is constructed to validate the performance of the proposed controller.
Issue Section:
Research Papers
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