Titlebook: Handbook of Model Predictive Control; Sa?a V. Rakovi?,William S. Levine Book 2019 Springer International Publishing AG, part of Springer N

決定性

灌輸

Moving Horizon EstimationPC performance. Moving horizon estimation (MHE) is an optimization-based state estimation algorithm. Similar to MPC, it relies on the minimization of a sum of stage costs subject to a dynamic model. Unlike MPC, however, conditions under which MHE is robustly stable have been slow to emerge. Recently

表示問

Probing and Duality in Stochastic Model Predictive Controlt signal and output measurements. This density is known as the . in control circles and as the . in artificial intelligence and robotics squares. The choice of control signal affects the information state so that state observability becomes control-dependent. Thus, the feedback control law needs to

Malfunction

Economic Model Predictive Control: Some Design Tools and Analysis Techniquesity by directly minimizing, over a receding prediction horizon, the costs incurred in system’s operation. Several design alternatives have been proposed in the literature, as well as suitable tools for the analysis of stability and performance of the different design methods. This note provides the

Jejune

Nonlinear Predictive Control for Trajectory Tracking and Path Following: An Introduction and Perspec changing references are given, formulations for tracking of a time-dependent trajectory or for following a geometric path are more suitable. In other cases, no clear reference is given, but only optimal economic behavior is desired. We outline how these control goals can be captured and embedded in

Detoxification

Hybrid Model Predictive Controlthe term ., span a wide range of control settings. These include discrete-time systems with discontinuous right-hand sides, with states that include both continuous-valued and discrete-valued variables. It also includes systems controlled by MPC strategies using memory variables and logic states, co