Stochastic receding horizon control of nonlinear stochastic systems with probabilistic state constraints
This work addresses the challenge of real-time stochastic control with state constraints for nonlinear systems, which is important for autonomous systems operating under uncertainty.
The paper presents a receding horizon control framework for nonlinear stochastic systems with probabilistic state constraints, enabling real-time implementation on mobile processors. The approach decomposes the problem into drift-based reference path planning and stochastic optimal tracking, with closed-form or pre-computed solutions, and demonstrates convergence and simulation results.
The paper describes a receding horizon control design framework for continuous-time stochastic nonlinear systems subject to probabilistic state constraints. The intention is to derive solutions that are implementable in real-time on currently available mobile processors. The approach consists of decomposing the problem into designing receding horizon reference paths based on the drift component of the system dynamics, and then implementing a stochastic optimal controller to allow the system to stay close and follow the reference path. In some cases, the stochastic optimal controller can be obtained in closed form; in more general cases, pre-computed numerical solutions can be implemented in real-time without the need for on-line computation. The convergence of the closed loop system is established assuming no constraints on control inputs, and simulation results are provided to corroborate the theoretical predictions.