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We present an indirect adaptive model predictive control algorithm for output tracking of linear systems with polytopic uncertainty. The proposed approach is based on the velocity form of the system model, and achieves input-to-state stable output tracking with respect to the parameter estimation error and the rate of change of time-varying references. For the constrained case, recursive feasibility...
We propose a control design for a constrained linear system to track reference signals within a given bounded error. The admissible reference signals are generated as output trajectories of a reference generator, which is a constrained linear system driven by unknown bounded inputs. The controller has to track the reference signals and to never violate a given tracking error bound, while satisfying...
We develop an indirect adaptive model predictive control algorithm for uncertain linear systems subject to constraints. The system is modeled as a polytopic linear parameter varying system where the convex combination vector is constant but unknown. The terminal cost and set are constructed from a parameter-dependent Lyapunov function and the associated control law, and robust control invariant set...
We extend a recently developed design for indirect adaptive model predictive control (IAMPC) and presents additional results on its stability properties. The IAMPC guarantees constraints satisfaction including during the learning transient, is input-to-state stable (ISS) with respect to the parameter estimation error, and has computational burden comparable to that of non-adaptive MPC. In this paper...
For stabilizing model predictive control adjusting the prediction model requires the adjustment of the terminal set and terminal cost. However, the conventional methods to design these are not practical, and often impossible, to implement in microcontrollers. In this paper, we pre-compute the terminal cost and terminal set in a form that allows to adjust them with minimal computational effort, following...
This paper develops further the network reference governor, which is a predictive algorithm for modifying commands sent to the remote system to satisfy state and control constraints. Due to the network communication, the governor must account for a delay that can be time-varying and unknown. The paper summarizes the results on network reference governor theory, and demonstrate its operation on a case...
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