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This paper expands a sliding mode fuzzy controller which sliding surface gain is online tuned by minimum fuzzy inference algorithm. The main goal is to guarantee acceptable trajectories tracking between the second order nonlinear system (robot manipulator) actual and the desired trajectory. The fuzzy controller in proposed sliding mode fuzzy controller is based on Mamdani’s fuzzy inference system (FIS) and it has one input and one output. The input represents the function between sliding function, error and the rate of error. The outputs represent torque, respectively. The fuzzy inference system methodology is online tune the sliding surface gain based on errorbased fuzzy tuning methodology. Pure sliding mode fuzzy controller has difficulty in handling unstructured model uncertainties. To solve this problem applied fuzzybased tuning method to sliding mode fuzzy controller for adjusting the sliding surface gain (ë ). Since the sliding surface gain (ë) is adjusted by fuzzybased tuning method, it is nonlinear and continuous. Fuzzybased tuning sliding mode fuzzy controller is stable modelfree controller which eliminates the chattering phenomenon without to use the boundary layer saturation function. Lyapunov stability is proved in fuzzybased tuning sliding mode fuzzy controller based on switching (sign) function. This controller has acceptable performance in presence of uncertainty (e.g., overshoot=0%, rise time=0.8 second, steady state error = 1e9 and RMS error=1.8e12).
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