Abstract
On the basis of the fuzzy T-S (Takagi-Sugeno) model, we propose a robust model predictive control for a class of nonlinear systems with constraint inputs. The upper bound of predictive cost is derived; the constraints on stability and inputs are transformed into linear matrix inequalities (LMIs), which can be easily solved. Thus we adopt a parallel distributed compensation (PDC) controller in the scheme. Sufficient conditions of moving horizon optimization are derived based on LMIs and Lyapunov function, and consequently the stability of closed-loop systems is proved. The simulation results verify the effectiveness of the proposed method.
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Li, Y., Qiu, Y., Zhang, J. (2011). Robust Model Predictive Control for Nonlinear Systems. In: Jin, D., Lin, S. (eds) Advances in Computer Science, Intelligent System and Environment. Advances in Intelligent and Soft Computing, vol 105. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23756-0_38
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DOI: https://doi.org/10.1007/978-3-642-23756-0_38
Publisher Name: Springer, Berlin, Heidelberg
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