Shigen Gao

Approximation-Based Robust Adaptive Automatic Train Control: An Approach for Actuator Saturation

This paper addresses an on-line approximation-based robust adaptive control problem for the automatic train operation (ATO) system under actuator saturation caused by constraints from serving motors. A robust adaptive control law is proposed, which is proved capable of on-line estimating of the unknown system parameters and stabilizing the closed-loop system. To cope with actuator saturation, another robust adaptive control is proposed for the ATO system, by explicitly considering the actuator saturation nonlinearity other than unknown system parameters, which is also proved capable of stabilizing the closed-loop system. Simulation results are presented to verify the effectiveness of the two proposed control laws.

An Introduction to Parallel Control and Management for High-Speed Railway Systems

This paper introduces a framework of parallel control and management for high-speed railway systems (HRSs). First, based on multiagent modeling, an artificial HRS that is consistent with realistic operations of the actual HRS is constructed. Then, different kinds of computational experiments are performed on the artificial HRS, followed by analysis and synthesis with a case. Finally, through an interactive and parallel operation between the actual and artificial HRSs, a set of practical control and management strategies can be achieved for the actual HRS. With the primary objective of ensuring reliability and safety of HRSs, this study could enhance the quality of services and the integrated transportability with other existing modes of transportation systems to provide appropriate recommendations and strategies for forming an overall effective comprehensive transportation system.

An Integrated Control Model for Headway Regulation and Energy Saving in Urban Rail Transit

In an urban rail transit system, issues regarding headway regulation have aroused wide attention. The assurance of headway regularity can decrease train delay times and average passenger waiting times. An integrated control method is proposed to optimize train headway by adjusting the train arrival time at stations. The adjustment of train arrival time is achieved by using an analytical method, and then the speed profile for each train is calculated by a suboptimal method, which has been applied in a practical system. Through simulation, the CPU time for calculating optimal train arrival time and speed profile is analyzed, respectively. The analysis demonstrates that the proposed method satisfies the real-time requirements for solving the headway regulation problem. By adopting the proposed method, the average passenger waiting time and the energy consumption can be decreased. In particular, the proposed method has better performance when the dispatch headway is large.

Truncated adaptation design for decentralised neural dynamic surface control of interconnected nonlinear systems under input saturation

ABSTRACT This paper studies decentralised neural adaptive control of a class of interconnected nonlinear systems, each subsystem is in the presence of input saturation and external disturbance and has independent system order. Using a novel truncated adaptation design, dynamic surface control technique and minimal-learning-parameters algorithm, the proposed method circumvents the problems of ‘explosion of complexity’ and ‘dimension curse’ that exist in the traditional backstepping design. Comparing to the methodology that neural weights are online updated in the controllers, only one scalar needs to be updated in the controllers of each subsystem when dealing with unknown systematic dynamics. Radial basis function neural networks (NNs) are used in the online approximation of unknown systematic dynamics. It is proved using Lyapunov stability theory that all the signals in the closed-loop system are semi-globally uniformly ultimately bounded. The tracking errors of each subsystems, the amplitude of NN approximation residuals and external disturbances can be attenuated to arbitrarily small by tuning proper design parameters. Simulation results are given to demonstrate the effectiveness of the proposed method.

Extended fuzzy logic controller for high speed train

In this paper, two dynamic models of high-speed train are presented, namely a single-mass (SM) model and an unit-displacement multi-particle (UDMP) model. Based on the former, a direct fuzzy logic controller is designed, and on the latter, a new fuzzy controller incorporating the implication logic is designed. Three sets of relevant numerical simulation are provided to demonstrate the effectiveness of the proposed control schemes through comparison.

Adaptive neural control with intercepted adaptation for time-delay saturated nonlinear systems

In this paper, the adaptive neural control is proposed for a class of single-input-single-output nonlinear systems with state delay and input saturation. An intercepted adaptation approach is designed to attenuate the effect caused by the input saturation based on a constructed auxiliary system, and radial basis function neural networks are used in the online learning of unknown dynamics. Lyapunov–Krasovskii function is introduced to deal with the state delay. The proposed control scheme can guarantee semi-globally uniformly boundedness of the closed-loop system as rigorously proved by Lyapunov stability theorem. The ultimate and transient tracking errors will be confined in compact regions. The diameters of these regions can be adjusted to be arbitrarily small by tuning proper design parameters. Illustrative examples are used to demonstrate the effectiveness of the proposed control method.

Single-parameter-learning-based fuzzy fault-tolerant output feedback dynamic surface control of constrained-input nonlinear systems ☆

Abstract This paper addresses the problem of adaptive fuzzy fault-tolerant dynamic surface control for a class of constrained-input nonlinear systems. To resolve this problem, the design of an observer-based single-parameter-learning (SPL) control method using output feedback is proposed. The Takagi-Sugeno (T-S) fuzzy system is used to identify and approximate online the uncertain nonlinear dynamics, requiring no knowledge. The barriers that restrict the applications of the traditional backstepping and approximation-based approach, including the explosion of complexity and the dimension curse problems, are circumvented via dynamic surface control and SPL techniques. The merit of the proposed method lies in that only one parameter in the entire control scheme requires online adjustment, regardless of the number of parameters in the T-S fuzzy system that characterizes the fuzzy rules; the calculation burden, in this sense, is reduced to the extent of the minimum value. The truncated adaptation method is used to avoid the chattering and instability caused by constrained input. It is shown with rigorous proof using the Lyapunov and invariant set theorems that all the closed-loop signals are guaranteed semi-globally uniformly ultimately bounded. The output tracking error is adjustable by means of design parameters in an explicit form, and can be adjusted to an arbitrarily small value around zero by appropriately chosen control parameters, even under faulty and constrained actuators. Simulation and comparative results are provided to demonstrate the effectiveness of the proposed control approach.

Adaptive fault-tolerant automatic train operation using RBF neural networks

In order to accommodate actuator failures which are unknown in amplitude and time, adaptive fault-tolerant control schemes are proposed for automatic train operation system. Firstly a basic design scheme on the basis of direct adaptive control is considered. It is demonstrated that, when actuator failures occur, asymptotical speed and position tracking are guaranteed. Then a new user-friendly control scheme is proposed which can eliminate the undesirable chattering phenomenon, which is the defect of the previous method. Simulation results verify the effectiveness of established theoretical results that satisfactory speed tracking and position tracking can be guaranteed in the presence of uncertain actuator failures in automatic train operation systems.

Characteristic model-based all-coefficient adaptive control for automatic train control systems

Safe and reliable automatic train control is a primary consideration for any advanced rail transit system. This paper introduces the characteristic model-based modeling method into ATC system and develops an all-coefficient adaptive control. Two characteristic models, namely speed characteristic model and position characteristic model, are established for analyzing both train traction and cruising dynamical characteristics. Control strategies are proposed using single speed feedback and speed/position bi-feedback. Numerical simulations are carried out to verify the effectiveness of the proposed strategies, showing that control objective and required dynamic performance are well satisfied. Moreover, the system shows robustness against time-varying model uncertainties and unknown operational environment dynamics.

Neural adaptive coordination control of multiple trains under bidirectional communication topology

This paper investigates the problem of coordination control for a group of trains by neural adaptive approach. The communication structure among trains is a bidirectional one, i.e., necessary information of neighboring trains is used in the control design for a train. Two control schemes are developed, with the first one requiring the information of position, speed, and acceleration of neighboring trains, while the second requiring the information of position of neighboring trains only by virtue of high-order sliding mode observer technique. Based on the universal approximation capacity of radial basis function neural networks, there are no requirements of the precise parameters describing operational resistance and other kinds of extra resistances in the controller design, which are reconstructed by radial basis function neural networks online. The stability of single train and multiple trains are guaranteed by Lyapunov stability theorem. Numerical simulations are presented to demonstrate the effectiveness and performance of the proposed controllers.