The control problem of T-S fuzzy system with actuator amplitude, rate and acceleration saturations isaddressed in this paper, where state augmented feedback controller with LMIs (Liner Matrix Inequalities) constraintconditions are proposed. Dynamic decoupling method is applied to fuzzify the input magnitude saturation nonlinearityinto several sub-linear systems with fuzzy rules, thus a new T-S fuzzy system with input rate and accelerationsaturations can be obtained. Then PDC (parallel distributed compensation) and NPDC (non-PDC) controller areboth designed for the new T-S fuzzy system. The first and second order derivatives of input variable are given toobtain an augmented fuzzy system. As the augmented system output variable, the input rate is represented by firstorder derivative of the input term, the rate saturation constraint is described through norm bounded method. Moreover,the polytopic approach is used to replace the second order derivative of the input term, and a state augmentedfeedback NPDC controller is designed, the domain of attraction optimization process is also given. Finally, twopractical examples are presented to show the effectiveness of proposed method.
The control problem of T-S fuzzy system with actuator amplitude, rate and acceleration saturations isaddressed in this paper, where state augmented feedback controller with LMIs (Liner Matrix Inequalities) constraintconditions are proposed. Dynamic decoupling method is applied to fuzzify the input magnitude saturation nonlinearityinto several sub-linear systems with fuzzy rules, thus a new T-S fuzzy system with input rate and accelerationsaturations can be obtained. Then PDC (parallel distributed compensation) and NPDC (non-PDC) controller areboth designed for the new T-S fuzzy system. The first and second order derivatives of input variable are given toobtain an augmented fuzzy system. As the augmented system output variable, the input rate is represented by firstorder derivative of the input term, the rate saturation constraint is described through norm bounded method. Moreover,the polytopic approach is used to replace the second order derivative of the input term, and a state augmentedfeedback NPDC controller is designed, the domain of attraction optimization process is also given. Finally, twopractical examples are presented to show the effectiveness of proposed method.