System identification: theory for the user
System identification: theory for the user
Adaptive control: stability, convergence, and robustness
Adaptive control: stability, convergence, and robustness
Verification of Polyhedral-Invariant Hybrid Automata Using Polygonal Flow Pipe Approximations
HSCC '99 Proceedings of the Second International Workshop on Hybrid Systems: Computation and Control
Approximate Reachability Analysis of Piecewise-Linear Dynamical Systems
HSCC '00 Proceedings of the Third International Workshop on Hybrid Systems: Computation and Control
A tutorial on support vector regression
Statistics and Computing
Brief paper: MPC for tracking piecewise constant references for constrained linear systems
Automatica (Journal of IFAC)
Statistics for sparse, high-dimensional, and nonparametric system identification
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
Reachability algorithm for biological piecewise-affine hybrid systems
HSCC'07 Proceedings of the 10th international conference on Hybrid systems: computation and control
Reachability analysis of nonlinear systems using conservative approximation
HSCC'03 Proceedings of the 6th international conference on Hybrid systems: computation and control
Efficient representation and computation of reachable sets for hybrid systems
HSCC'03 Proceedings of the 6th international conference on Hybrid systems: computation and control
Autonomous Helicopter Aerobatics through Apprenticeship Learning
International Journal of Robotics Research
Survey Constrained model predictive control: Stability and optimality
Automatica (Journal of IFAC)
Systems with persistent disturbances: predictive control with restricted constraints
Automatica (Journal of IFAC)
Input-to-state stability for discrete-time nonlinear systems
Automatica (Journal of IFAC)
Examples when nonlinear model predictive control is nonrobust
Automatica (Journal of IFAC)
Robust model predictive control using tubes
Automatica (Journal of IFAC)
An overview of statistical learning theory
IEEE Transactions on Neural Networks
Identification and control of dynamical systems using neural networks
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
Hi-index | 22.14 |
Controller design faces a trade-off between robustness and performance, and the reliability of linear controllers has caused many practitioners to focus on the former. However, there is renewed interest in improving system performance to deal with growing energy constraints. This paper describes a learning-based model predictive control (LBMPC) scheme that provides deterministic guarantees on robustness, while statistical identification tools are used to identify richer models of the system in order to improve performance; the benefits of this framework are that it handles state and input constraints, optimizes system performance with respect to a cost function, and can be designed to use a wide variety of parametric or nonparametric statistical tools. The main insight of LBMPC is that safety and performance can be decoupled under reasonable conditions in an optimization framework by maintaining two models of the system. The first is an approximate model with bounds on its uncertainty, and the second model is updated by statistical methods. LBMPC improves performance by choosing inputs that minimize a cost subject to the learned dynamics, and it ensures safety and robustness by checking whether these same inputs keep the approximate model stable when it is subject to uncertainty. Furthermore, we show that if the system is sufficiently excited, then the LBMPC control action probabilistically converges to that of an MPC computed using the true dynamics.