Safety-Certified Self-Triggered Cooperative Path Following Control via Data-Driven Learning and Neurodynamic Optimization

This paper investigates the safety-certified cooperative path following problem for second-order nonlinear systems with multiple-input multiple-output strict-feedback form subject to safety, state, and input constraints. A safety-certified learning control approach is proposed to achieve collision-free cooperative path following based on command optimization, online learning, and self-triggered communication. Specifically, an extended-state-observer-aided learning neural predictor is developed to simultaneously identify nonlinear functions and unknown input gains without measuring state derivatives. Control barrier functions are then employed to ensure safety through forward invariant sets. Next, command optimization is utilized to generate the optimal virtual control signals that satisfy safety constraints, state constraints, and input constraints. A neurodynamic optimization technique is employed to solve the quadratic optimization problem in real time. Additionally, a self-triggered mechanism is introduced in path variable coordination to reduce the listening and triggering times. By using the proposed safety-certified cooperative path following approach, a safe formation is guaranteed for input-to-state safety. Simulation results are provided to illustrate the effectiveness of the proposed method.
Note to Practitioners
—This paper addresses the safety-certified cooperative path following problem of multi-agent systems, which has practical implications in various applications. These applications include formation patrol, cargo transportation, search and rescue missions, swarm robotics, and agricultural tasks. By coordinating the movements of multiple agents, cooperative path following enhances efficiency in these scenarios. The challenges posed by safety, state, and input constraints are commonly encountered in practical. To address these challenges, the command optimization approach is proposed in this paper. The optimization problem is efficiently solved in real-time using neurodynamic optimization technique. Furthermore, to enhance feasibility in a limited communication environment, a self-triggered mechanism is introduced to reduce the communication burden. Therefore, the aforementioned effective scheme is suitable for implementation in industrial applications.