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In a previous work we developed a convex infinite dimensional linear programming (LP) approach to approximating the region of attraction (ROA) of polynomial dynamical systems subject to compact basic semialgebraic state constraints. Finite dimensional rela ...
This paper addresses the design of Model Predictive Control (MPC) laws to solve the trajectory-tracking problem and the path-following problem for constrained under-actuated vehicles. By allowing an arbitrarily small asymptotic tracking error, we derive MP ...
The Multi-Parametric Toolbox is a col- lection of algorithms for modeling, control, analysis, and deployment of constrained optimal controllers developed under Matlab. It features a powerful ge- ometric library that extends the application of the toolbox b ...
Model Predictive Control (MPC) for buildings has gained a lot of attention recently. It has been shown that MPC can achieve significant energy savings in the range between 15-30 % compared to a conventional control strategy, e.g., to a rule-based controlle ...
We characterize the maximum controlled invariant (MCI) set for discrete-time systems as the solution of an infinite-dimensional linear programming problem. In the case of systems with polynomial dynamics and semialgebraic state and control constraints, we ...
State-feedback model predictive control (MPC) of constrained discrete-time periodic affine systems is considered. The periodic systems’ states and inputs are subject to periodically time-dependent, hard, polyhedral constraints. Disturbances are additive, b ...
This work presents an approach for both dis- tributed synthesis and control for a network of discrete-time constrained linear systems without central coordinator. Every system in the network is dynamically coupled to a number of neighboring systems and it ...
This work presents an approach for both distributed synthesis and control for a network of discrete-time constrained linear systems without central coordinator. Every system in the network is dynamically coupled to a number of neighboring systems and it is ...
This paper presents a systematic computational study on the performance of distributed optimization in model predictive control (MPC). We consider networks of dynamically coupled systems, which are subject to input and state con- straints. The resulting MP ...
This paper presents a method for plug-and-play distributed MPC of a network of interacting linear systems. The previously introduced idea of plug and play control addresses the challenge of performing network changes in the form of subsystems that are join ...
