LAUSR

A robust fuzzy predictive control (RFPC) based on Takagi–Sugeno (T-S) fuzzy model is proposed for systems with uncertainties, time-varying delays, unknown disturbances, as well as strong nonlinearity. First, the T-S fuzzy model is built by a number of linear submodels and nonlinear membership functions. Then, by introducing the output tracking error to this fuzzy model, the novel augmented state space model is presented to independently regulate the process state variables and output tracking error. The control law of the proposed RFPC is further designed based on this extended model, which can guarantee the process state to be fast convergent and make the process output track the set-point well. Moreover, it increases the ability of adjustment for the proposed controller. Utilizing Lyapunov–Krasovskii method, optimized control theory, and control method, the stable sufficient conditions are given for the designed control law to make sure the asymptotical stability of the nonlinear uncertain system with the time-varying delay and unknown disturbances. The gains of the controller can be obtained by solving these stabilized conditions in form of linear matrix inequality constraints. At last, a case study of continuous stirred tank reactor manifests that the proposed RFPC method can bear a larger range of time delay, overcome the uncertainties and unknown disturbances well, and have better tracking performance.