A self-tuning fuzzy inference sliding mode control method is presented for single inverted pendulum position tracking control. Sliding mode control is a special nonlinear control method which has a quick response, is insensitive to parameters’ variation and disturbance; and is very suitable for nonlinear system control. Neuro-fuzzy logic systems are used to directly generate the "equivalent control term". In this case, a neuro-fuzzy system was described as a self-tuning fuzzy inference system optimized online using Takagi-Sygeno type of rules and a back-propagation algorithm to minimize a cost function. The cost function is made up of a quadratic error term and a weight decay term that prevents an excessive growth of parameters. The definition of sliding mode control was presented, and on the basis of the inverted pendulum system the sliding mode controller was designed. Stability of the proposed control scheme is proved by the Lyapunov theorem and the control scheme is applied to an inverted pendulum system. Simulation studies show that the method is effective and can be applied to a nonlinear control system.
Recent developments in electrical machines, power electronics, and control theories help the variable speed drive technology to offer precise speeds at every level of operation. In that same context, this study investigated on improving the performance of variable speed drive based on a permanent magnet synchronous motor by rejecting the external disturbance in speed control using the passivity-based control and sliding mode control approach. The sliding mode control approach is implemented with an exponential reaching law to improve the robustness of the system. Furthermore, a passivity-based q-axis current loop and d-axis current loop controllers are detailed. The system stability with the proposed scheme is mathematically proved using Lyapunov stability criteria. Simulation results are provided to demonstrate the superior properties of the proposed control method using MATLAB Simulink.
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