Optimal Sampled-Data Control of a Nonlinear System
Optimal sampled-data control of a nonlinear system is considered with the stable-manifold approach and extensive use of numerical techniques. The idea is to notice the Hamiltonian system associated with the considered optimal control problem and to compute trajectories on its stable manifold. Since the control input accompanied with those trajectories is proved to be optimal, the optimal control law can be obtained through interpolation. The stable-manifold approach was originally proposed for continuous-time optimal control and here it is adapted for sampled-data control based on the works of Navasca. In the case of sampled-data control, the approach requires the state transition of the controlled plant during one sampling period together with its derivatives with respect to the state and the input. Their computation is achieved by numerical techniques. Moreover, a shooting method is proposed for systematic generation of the trajectories and extension is considered for the intersample behavior to be taken into account. The proposed method is applied to tracking control of a wheeled mobile robot. It works successfully with a rather long sampling period.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Neural Contraction Metrics for Robust Estimation and Control: A Convex Optimization Approach
This paper presents a new deep learning-based framework for robust nonlinear estimation and control using the concept of a Neural Contraction Metric (NCM). The NCM uses a deep long short-term memory recurrent neural netw…
Motion PlanningOptimal Motion PlanningState EstimationSafety of Sampled-Data Systems with Control Barrier Functions via Approximate Discrete Time Models
Control Barrier Functions (CBFs) have been demonstrated to be a powerful tool for safety-critical controller design for nonlinear systems. Existing design paradigms do not address the gap between theory (controller desig…
A PAC-Bayesian Framework for Optimal Control with Stability Guarantees
Stochastic Nonlinear Optimal Control (SNOC) involves minimizing a cost function that averages out the random uncertainties affecting the dynamics of nonlinear systems. For tractability reasons, this problem is typically …
Generalization BoundsNeural Stochastic Contraction Metrics for Learning-based Control and Estimation
We present Neural Stochastic Contraction Metrics (NSCM), a new design framework for provably-stable robust control and estimation for a class of stochastic nonlinear systems. It uses a spectrally-normalized deep neural n…
Aperiodic-sampled neural network controllers with closed-loop stability verifications (extended version)
In this paper, we synthesize two aperiodic-sampled deep neural network (DNN) control schemes, based on the closed-loop tracking stability guarantees. By means of the integral quadratic constraint coping with the input-ou…