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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 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...
This paper provides a tutorial overview of reference governors and command governors, which are add-on control schemes for reference supervision and constraint enforcement in closed-loop feedback control systems. The main approaches to the development of such schemes for linear and nonlinear systems are described. The treatment of unmeasured disturbances and parametric uncertainties is addressed....
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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