Task Optimization for Fixed-Time Control of Intermittent Human-Robot Interaction With Time-Varying Exponents and...

In this article, we investigate the task optimization for fixed-time control of intermittent human-robot interaction, where a human operator assists the robot intermittently in selecting the most appropriate Pareto solution. First, as for the Lyapunov fixed-time stability criterion inequality with and without the constant term, we all derive the Lyapunov stability conditions with time-varying exponents and coefficients, providing us with more flexibility and freedom to shape the contour of the convergence near the Lyapunov stable equilibrium. We then use them to propose a hierarchical fixed-time event-triggered optimization (HFTEO) algorithm based on human-oriented scheme, where the so-called human-oriented scheme means that the components constituting task information are known only to the human operator, but not to the robot, which is beneficial to ensure the confidentiality and security of the task. Simulation results are given to show the effectiveness of the proposed Lyapunov stability conditions and algorithm.