Hongtao Liao

Distributed cooperative tracking control for heavy haul trains with event-triggered strategy

A cooperative control with distributed event-triggered strategy for heavy haul trains is proposed to track the desired velocity with minimum in-train forces under the consideration of operation efficiency and resource utilization. Event-triggered sampling mechanism is employed in determining the time instants for state sampling and transmission among locomotives to reduce system operation and resource consumption. A theorem for the triggering condition is developed to achieve the cooperative tracking under event triggering mechanism by exploiting the M-matrix, algebraic graph theory and the Lyapunov method. It is rigorously proved that the closed-loop system is uniformly asymptotically cooperative stable under proposed strategy. Simulations demonstrate the feasibility and effectiveness of the proposed strategy.

A distributed ESO based cooperative current-sharing strategy for parallel charging systems under disturbances

In this paper, a distributed extended state observer (ESO) based cooperative control is proposed to achieve current-sharing for parallel chargers under disturbances in energy storage type light rail vehicle systems. By treating the parallel charging system as a nonlinear and non-identical multiagent system, the current-sharing objective is converted as a distributed leader-follower consensus problem. A distributed ESO is designed to observe total disturbances with the known information from the established mathematical model of subchargers. A tracking differentiator (TD) is established to provide a transitional process for the reference signal to increase the dynamical performance and robustness. Based on ESO and TD, a distributed cooperative control protocol is proposed. Simulation and experimental results are provided to verify the disturbance rejection ability, feasibility and practicability of the proposed distributed current-sharing control method.

A generalized extended state observer for supercapacitor state of charge estimation under disturbances

In this paper, a generalized extended state observer (GESO) is proposed to accurately observe supercapacitors state of charge (SOC) by counteracting the modeling deviation and disturbances. With the consideration of dynamic modeling uncertainty and disturbances, a nonlinear mathematical model is established based on the three branch equivalent circuit model for supercapacitor (SC). A GESO is designed to observe SCs internal capacitance voltage and modeling error with the known information from the established mathematical model. With the estimated modeling error information, a compensation control law is designed to eliminate deviation, which results in an accurate internal capacitance voltage observation. An accurate SOC for SC is obtained with the estimated parameters. Experimental results from a synchronous buck circuit based test platform are provided to verify the feasibility and superiority for SOC observation by GESO.

Optimal constrained ensemble control for inhomogeneous robots

In this paper, an optimal ensemble control under dynamic constraints is designed based on multidimensional radau pseudospectral method (MRPM) for inhomogeneous robots. The optimal control scheme exploits the uniform control inputs to steer the robots to the desired distribution while compensating the inhomogeneity of robots. A controllable ensemble system is constructed, in which velocity and acceleration constraints are transformed into control input constraints. Its controllability is investigated. Then an optimal ensemble control policy is proposed based on MRPM, to satisfy control input constraints. The proof of convergence is provided for the MRPM. Simulation results are presented to verify the effectiveness of the proposed approach.