Abstract
This paper addresses the tracking accuracy and robustness enhancement problems of fuzzy model based predictive control (MPC) for a class of nonlinear systems subjecting to lumped disturbances composed of bounded unknown disturbances and a model-plant mismatch. Main features of the proposed method are: 1) A fuzzy disturbance observer and an auxiliary controller are jointly developed to meet a certain control objective that minimizes the peak bound of the errors caused by the lumped disturbances, which eventually leads to desired offset-free tracking performance. 2) A pre-computed robust positively invariant set whose central is the nominal state is derived with the premise of input-to-state stability. 3) Tightened constraints for the guarantee of recursive feasibility of MPC is computed off-line and the quasi-min-max fuzzy MPC is elaborately designed according to a piecewise Lyapunov function. Furthermore, characteristics of robustness enhancement and low on-line computational burden are obtained as compared with the existing offset-free MPCs, and further the impacts of estimation error arising from sampling time and admissible target set on the system performance are also discussed. Two simulation examples verify the effectiveness of the proposed approach ensuring the satisfaction of constraints.
Highlights
Model predictive control (MPC), which can predict the future process behavior and optimize the control input with the consideration of various constraints, has been extensively studied in the past decades [1]
In light of tube-based MPC, this paper proposes a disturbance observer based fuzzy model-based predictive control (DOBFMPC) approach, which is shown in FIGURE 1 to address the aforementioned control challenges for industry process
DOBFMPC consisted of RMPC, discrete DOB and auxcontroller is proposed for the fuzzy system, which improves the tracking performance and robustness of the control system
Summary
Model predictive control (MPC), which can predict the future process behavior and optimize the control input with the consideration of various constraints, has been extensively studied in the past decades [1]. The tracking accuracy was further enhanced in [14], in which the modeled disturbances were estimated by a novel fuzzy disturbance observer and compensated in the compound control law, in term of the semiglobally input-tostate practically stability of the closed-loop system. T-S fuzzy model-based predictive control with the issues of stability and optimization has been designed on the basis of PLFs in [20]. In [29], a DOB based MPC for linear systems was proposed, in terms of ISS stability, to cancel out the disturbances effect from the control input via feedforward channel. In [30], the perturbations of a mobile robot treated as input addictive disturbances were estimated by an extended state observer (ESO) and compensated through the control law, the tracking performance of the distributed model predictive control was improved. The tracking offset is still an urgent problem to be solved when the disturbances are strong and there is still a lot of research to be done to meet the challenges of large nonlinear systems
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