Neurodynamics-Based Model Predictive Control of Continuous-Time Under-Actuated Mechatronic Systems

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.

Automated summary

This article discusses neurodynamics-based model predictive control of continuous-time under-actuated mechatronic systems, formulated as a global optimization problem and solved using a collaborative neurodynamic approach. The closed-loop system is proven to be asymptotically stable, with specific applications on control of autonomous surface vehicles and unmanned wheeled vehicles elaborated to demonstrate the efficacy of the approach.