Dynamic programming for constrained optimal control of discrete-time linear hybrid systems

By Francesco Borrelli, Mato Baotic, Alberto Bemporad, Manfred Morari. at - Automatisierungstechnik, 2005

384 citations recorded by the source corpus.

Subfields: Control and Systems Engineering

View Paper: https://www.semanticscholar.org/paper/34a9d3d1845b6265fdd6862d8d4f883e96597ab5

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In Control and Systems Engineering

Citations in the corpus

Linear offset-free Model Predictive Control (2009, methodology): “Methods have been developed [3,10,11,17] to solve (15), (16) explicitly to obtain a state feedback control law u(t) = c0(x̂(t), d̂(t), r(t)) in the form of a look-up table.”
Hybrid Model Predictive Control Application Towards Optimal Semi-Active Suspension (2005, methodology): “( 2005 ), it is possible to compute an explicit representation u ( t )= f ( x ( t )) of the receding horizon control law ( 31 ) as a collection of affine gains over (possibly overlapping) polyhedral partitions of the set of states <inline-graphic xmlns:xlink="http://www.”
Predictive Control for Linear and Hybrid Systems (2017, background): “Model Predictive Control for discrete-time PWA systems and their stability and robustness properties have been studied in [68, 60, 32, 188, 185, 186].”
Thinking fast and slow: Optimization decomposition across timescales (2017, methodology): “Typical methods for solving optimal control problem involve Pontryagin’s principle [39], [40] and Hamilton-Jacobi-Bellman equation [4], [5].”
Model Predictive Control of Power Electronic Systems: Methods, Results, and Challenges (2020, methodology): “As a result, the state-feedback control law is obtained and stored in look-up tables [87], [88], while methods, such as branch-”
Decentralized receding horizon control for large scale dynamically decoupled systems (2009, methodology): “Using algorithms described in [1, 2], the decentralized receding horizon framework proposed in this paper has been applied in simulation to a number of large scale control problems with success.”
MPC-Based Approach to Active Steering for Autonomous Vehicle Systems (2005, methodology): “…phase, if the solution of a MILP or MIQP is prohibitive, the explicit piecewise affine form of the MPC law can be computed off line by using the multiparametric mixed integer programming (mp-MILP or mp-MIQP) solvers (Dua and Pistikopoulos, 2000; Bemporad et al., 2002; Borrelli et al., 2005a).”
Distributed Model Predictive Control of Switched Nonlinear Systems with Scheduled Mode Transitions (2013, neutral): “Vkðx̂ðtkout r ÞÞ Vkðx̂ðtkin r ÞÞe 2q k Nkr Dkr (10)”
A survey on explicit model predictive control (2009, background): “It is easy to show that the explicit hybrid MPC controller has the form (21), but the control law u(x) may be discontinuous across neighboring polyhedra [20].”