Online Regulation of Dynamical Systems to Solutions of Constrained Optimization Problems
This paper considers the problem of regulating a dynamical system to equilibria that are defined as solutions of an input- and state-constrained optimization problem. To solve this regulation task, we design a state feedback controller based on a continuous approximation of the projected gradient flow. We first show that the equilibria of the interconnection between the plant and the proposed controller correspond to critical points of the constrained optimization problem. We then derive sufficient conditions to ensure that, for the closed-loop system, isolated locally optimal solutions of the optimization problem are locally exponentially stable and show that input constraints are satisfied at all times by identifying an appropriate forward-invariant set.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Utility of the Koopman operator in output regulation of disturbed nonlinear systems
This paper studies the problem of output regulation for a class of nonlinear systems experiencing matched input disturbances. It is assumed that the disturbance signal is generated by an external autonomous dynamical sys…
State Constrained Stochastic Optimal Control for Continuous and Hybrid Dynamical Systems Using DFBSDE
We develop a computationally efficient learning-based forward-backward stochastic differential equations (FBSDE) controller for both continuous and hybrid dynamical (HD) systems subject to stochastic noise and state cons…
Driving by the Rules: A Benchmark for Integrating Traffic Sign Regulations into Vectorized HD Map
Ensuring adherence to traffic sign regulations is essential for both human and autonomous vehicle navigation. While current online mapping solutions often prioritize the construction of the geometric and connectivity lay…
Autonomous DrivingAutonomous NavigationTraffic Sign RecognitionPhysics constrained nonlinear regression models for time series
A central issue in contemporary science is the development of data driven statistical nonlinear dynamical models for time series of partial observations of nature or a complex physical model. It has been established rece…
regressionTime SeriesConstrained nonlinear output regulation using model predictive control -- extended version
We present a model predictive control (MPC) framework to solve the constrained nonlinear output regulation problem. The main feature of the proposed framework is that the application does not require the solution to clas…
Model Predictive Control