Global Consensus Tracking Control of Nonlinear Multiagent Systems Under Quantitative Performance Constraints: A Low-Complexity Approach

This article explores global consensus tracking control for multiagent systems with unknown time-varying gains and nonlinearities, subject to quantitative performance constraints. The challenge lies in designing a controller that achieves global consensus without relying on the system's initial conditions while also ensuring the prescribed settling time and the convergence accuracy of synchronization errors. First, a concise, differentiable, piecewise continuous regulation function is proposed. The combination of this regulation function with error transformation addresses the singularity issue associated with initial conditions. Then, a piecewise performance function is also introduced to quantify both settling time and steady-state accuracy of synchronization errors. Integrating the regulation and performance functions yields a novel, low-complexity, robust method for enforcing quantitative performance constraints. This approach ensures global consensus under specified constraints and eliminates the need for nonlinear function approximation, parameter estimation, higher order derivative computation, or adaptive law design. Finally, the effectiveness of the proposed method is demonstrated through comparative simulations involving a planar robotic system.