Neurodynamics-based Model Predictive Control of Continuous-time Under-actuated Mechatronic Systems
Jiasen Wang, Jun Wang, Qing‐Long Han
Abstract
This article addresses neurodynamics-based model predictive control of continuous-time under-actuated mechatronic systems. The control problem is formulated as a global optimization problem based on sampled data, which is solved by using a collaborative neurodynamic approach. The closed-loop system is proven to be asymptotically stable. Specific applications on control of autonomous surface vehicles and unmanned wheeled vehicles are elaborated to substantiate the efficacy of the approach.
Topics & Concepts
MechatronicsModel predictive controlControl engineeringControl theory (sociology)Computer scienceControl (management)EngineeringArtificial intelligenceAdvanced Control Systems OptimizationAdaptive Control of Nonlinear SystemsControl Systems and Identification