Optimal Guaranteed-Cost Composite Nonlinear Feedback Cruise Control for Heavy-Haul Trains

To enhance transient control performance and achieve multi-objective optimization in terms of speed tracking accuracy, safety, and energy consumption, this paper explores a novel fuzzy optimal guaranteed-cost composite nonlinear feedback (CNF) cruise control strategy for heavy-haul trains, taking into account parameter uncertainties and asymmetric input saturation. A high-order nonlinear multiple-mass-point dynamics model is first established and then simplified into a lower-dimensional uncertain Takagi-Sugeno fuzzy error dynamics model through the application of the fencing concept and sector nonlinearity technique, which facilitates the controller design process. Employing the non-parallel distributed compensation approach along with Finsler’s lemma, a convex optimization problem is formulated to minimize an upper bound on train performance and stabilize the system. The corresponding sufficient conditions are deduced in terms of linear matrix inequalities. The proposed control strategy effectively integrates the benefits of CNF control with those of guaranteed-cost control methodologies. Numerical simulations comparing with different control methods are carried out using MATLAB/Simulink. The results of these comparisons demonstrate both the effectiveness and superiority of the proposed approach.