Cooperative Adaptive Control of Multiple High-speed Trains with Constrained Input via Learning Signal Trammeling

This paper develops a learning signal trammeling based cooperative adaptive control for multiple high-speed trains in the presence of constrained input nonlinearity. Using adaptive parameter identification to deal with unknown running resistances, an adaptive control is designed. Learning signal trammeling is investigated to guaranteed the closed-loop stability in the presence of constrained input. Under the proposed control, the closed-loop stability and boundedness of resulting closed-loop signals are ensured by Lyapunov stability theorem. Analytical boundaries of both position and speed tracking errors are obtained, characterizing by control parameters. Numerical simulation examples are given to verify the effectiveness of proposed control.