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Nonlinear MPC, ( , ), , , 0 min ( , , ) . . ... NMPC Example with ISAT x32 Inputs x1 States RR x17 x31 x2 Feed Distillate Bottoms 32 state binary distillation column ...

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This paper proposes a Model Predictive Control (MPC) algorithm for the solution of a robust control problem for continuous-time systems. Discontinuous feedback strategies are allowed in the solution of the min-max problems to be solved. The use of such strategies allows MPC to address a large class of nonlinear systems, including among

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Approximate Explicit MPC One approach to alleviate complexity (will be used in the nonlinear case..) Solve iteratively 1-step optimization problems with varying terminal set constraint (P. Grieder and M. Morari 2003) Online: since the regions of the 1-step multiparametric programs may overlap apply

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In general, the explicit MPC ranges of different controllers may not match. For example, the controllers may have different constraints or state ranges. In such cases, create a separate explicit MPC range object for each controller. Validate Explicit MPC Controllers

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MPC) and the nonlinear optimization routine (for the nonlinear MPC). In order to address the discrete-valued nature of part of the considered actuators, the nonlinear MPC optimization routine is changed in two ways: either a naive additional post-processing is employed or the mid-processing iteration (which is another main

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Model Predictive Control (MPC) is a control strategy that is suitable for optimizing the performance of constrained systems. Constraints are present in all control sys-

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One approach to approximate a nonlinear MPC is convex multi-parametric nonlinear programming [5], [6]. Approximat-ing an MPC by NNs, as also done herein, has for example been proposed in [7], [8]. In contrast to the proposed framework, these approaches cannot guarantee stability and constraint satisfaction for the resulting AMPC. In[9], [10 ...