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Titlebook: Constrained Optimal Control of Linear and Hybrid Systems; Francesco Borrelli Book 2003 Springer-Verlag Berlin Heidelberg 2003 Constrained

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Receding Horizon Control process only during the following sampling interval. At the next time step a new optimal control problem based on new measurements of the state is solved over a shifted horizon. The resultant controller will be referred to as Receding Horizon Controller (RHC).
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Constrained Robust Optimal Controltinuous piecewise affine function of the state vector. Thus, when the optimal control law is implemented in a moving horizon scheme, the on-line computation of the resulting controller requires the evaluation of a piecewise affine function only.
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Constrained Finite Time Optimal Controldback control law. We give insight into the structure of the optimal control law and of the value function for optimal control problems with performance criteria based on quadratic and linear norms. We describe how the optimal control law can be efficiently computed by means of multiparametric linea
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Receding Horizon Controltroller can be designed by repeatedly solving finite time optimal control problems in a receding horizon fashion as described next. At each sampling time, starting at the current state, an open-loop optimal control problem is solved over a finite horizon. The optimal command signal is applied to the
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Constrained Robust Optimal Controlsed on a min-max optimal control formulation based on a linear performance index. Robustness is achieved against additive norm-bounded input disturbances and/or polyhedral parametric uncertainties in the state-space matrices. We show that the robust optimal control law over a finite horizon is a con
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Reducing On-line Complexityblem, we propose two new algorithms that avoid storing the polyhedral regions. The new algorithms significantly reduce the on-line storage demands and computational complexity during evaluation of the PPWA feedback control law resulting from the CFTOC problem.
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