Braking penalized receding horizon control of heavy haul trains

Incorporated with a receding horizon control (RHC) approach, a penalty method is proposed to reduce energy wasted by braking in a heavy haul train’s operation. The train’s practical nonlinear model is linearized to design the RHC controller. This controller is then applied to the train practical nonlinear dynamics and its performances are analyzed. In particular, the main focus in this study is on the brake penalty’s impact on the train performances. Meantime, a fence method is presented to tackle two issues. The first one is that all the cars in a train cannot be controlled individually due to limit of available transmission channels for control systems in a long train. The other one is that the RHC approach suffers from heavy computation and memory load. Simulations verified that the brake penalty presented in the design can reduce a train’s energy consumption and intrain forces remarkably without sacrificing the train’s velocity tracking performance. Simulations also verified that the fence method is essential to reduce the related computation load when the RHC approach is applied to a long heavy haul train. Further, it is demonstrated that the fence method can effectively shorten computation time and reduce memoryhttp://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6979hb2014ai201

Development of an optimal operation approach in the MPC framework for heavy-haul trains

An operation control approach for heavy haul trains to optimize their performance, including operation safety, service quality and energy consumption, is proposed. Following a model predictive control method, the controller is capable of scheduling a train to operate optimally during a long section of the rail track. In the cost function, two penalty factors are presented, one for the braking forces and one for coupler damping effects. The penalty for braking forces is employed to reduce energy waste incurred by braking. The penalty for coupler damping is introduced to alleviate the cyclic vibration of couplers, which link adjacent cars in the train. The damping penalty is also expected to reduce energy wasted by coupler damping and corresponding maintenance/replacement cost of the dampers. In addition, the weight of the velocity tracking term in the objective function is modified to vary dynamically according to the train’s velocity to improve the train’s overall performance. Simulations verify the effectiveness of the proposed control approach. Discussions over the impacts of the two penalty factors and dynamic weight method are provided together with some suggestions on their applications.http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6979hb201

Fuzzy Constrained Predictive Optimal Control of High Speed Train with Actuator Dynamics

We investigate the problem of fuzzy constrained predictive optimal control of high speed train considering the effect of actuator dynamics. The dynamics feature of the high speed train is modeled as a cascade of cars connected by flexible couplers, and the formulation is mathematically transformed into a Takagi-Sugeno (T-S) fuzzy model. The goal of this study is to design a state feedback control law at each decision step to enhance safety, comfort, and energy efficiency of high speed train subject to safety constraints on the control input. Based on Lyapunov stability theory, the problem of optimizing an upper bound on the cruise control cost function subject to input constraints is reduced to a convex optimization problem involving linear matrix inequalities (LMIs). Furthermore, we analyze the influences of second-order actuator dynamics on the fuzzy constrained predictive controller, which shows risk of potentially deteriorating the overall system. Employing backstepping method, an actuator compensator is proposed to accommodate for the influence of the actuator dynamics. The experimental results show that with the proposed approach high speed train can track the desired speed, the relative coupler displacement between the neighbouring cars is stable at the equilibrium state, and the influence of actuator dynamics is reduced, which demonstrate the validity and effectiveness of the proposed approaches

Leveraging Connected Highway Vehicle Platooning Technology to Improve the Efficiency and Effectiveness of Train Fleeting Under Moving Blocks

Future advanced Positive Train Control systems may allow North American railroads to introduce moving blocks with shorter train headways. This research examines how closely following trains respond to different throttle and brake inputs. Using insights from connected automobile and truck platooning technology, six different following train control algorithms were developed, analyzed for stability, and evaluated with simulated fleets of freight trains. While moving blocks require additional train spacing beyond minimum safe braking distance to account for train control actions, certain following train algorithms can help minimize this distance and balance fuel efficiency and train headway by changing control parameters

Adaptive Fault-Tolerant Control of A Two-car High-speed Train Model with Inter-car Flexible Link and Traction Actuator Failures

This paper studies the adaptive fault-tolerant tracking control problem for the high-speed trains with intercar flexible link and traction actuator failures. This study is focused on a benchmark model which, as a main dynamic unit of the CRH (China Railway High-speed) train, is a two-car dynamic system with a flexible link between two cars, for which the input acts on the second car and the output is the speed of the first car. This model is under parameter uncertainties and subject to uncertain actuator failures. For such an underactuated system, to ensure the first car tracking a desired speed trajectory, a coordinate transformation method is employed to decompose the system model into a control dynamics subsystem and a zero dynamics subsystem. Stability analysis is conducted to show that such a zero dynamic system is Lyapunov stable and is partially input-to-state stable. An adaptive fault-tolerant control scheme is developed which is able to ensure the closedloop system signal boundedness and desired speed tracking, in the presence of the unknown system parameters and actuator failures. Simulation results from a realistic train dynamic model are presented to verify the desired adaptive control system performance

Adaptive Control Design and Evaluation for Multibody High-speed Train Dynamic Models

In this paper, the adaptive tracking control problem is investigated for multibody high-speed train dynamic model in the presence of unknown parameters, which is an open adaptive control problem. A 4-car train unit model with input signals acting on the 2nd and 3rd cars and output signals being the speeds of the 1st and 3rd cars is chosen as a benchmark model, in which the aerodynamic resistance force is also considered. To handel the nonlinear term, the feedback linearization method is employed to decompose the system into a control dynamics subsystem and a zero dynamics subsystem. A new and detailed stability analysis is conducted to show that such a zero dynamic system is Lyapunov stable and is also partially input-to-state stable under the condition that the speed error between the 1st and 3rd cars is exponentially convergent (to be ensured by a nominal controller) or belongs to the L1 signal space (to be achieved by a properly designed adaptive controller). The system configuration leads to a relative degree 1 subsystem and a relative degree 2 subsystem, for which different feedback linearization-based adaptive controllers and their nominal versions are developed to ensure the needed stabilization condition, the desired closed-loop system signal boundedness and asymptotic output speed tracking. Detailed closed-loop system stability and tracking performance analysis are given for the new control schemes. Simulation results from a realistic train dynamic model are presented to verify the desired adaptive control system performance

Adaptive position tracking control of high-speed trains with piecewise dynamics

This paper addresses the adaptive position track-ing control problem for high-speed trains with time-varying resistances and mass in the motion dynamics. To handel these time-varying parameters with piecewise constant characteris-tics, a piecewise constant model with unknown parameters is in-troduced for different train operation conditions. An integrated adaptive controller structure is constructed to have the capacity to achieve plant-model matching with known parameters and complete system parametrization with unknown parameters, which is desirable for adaptive tracking control. For the train position tracking requirement, the reference model system is speci?cally chosen. Stable adaptive laws are designed to update the adaptive controller parameters in the presence of the unknown piecewise constant system parameters. Closed-loop stability and asymptotic state tracking are proved. Simulation results on a high-speed train model are presented to illustrate the desired adaptive position tracking control performance

Pneumatic Positioning Control System Using Constrained Model Predictive Controller: Experimental Repeatability Test

Most of the controllers that were proposed to control the pneumatic positioning system did not consider the limitations or constraints of the system in their algorithms. Non-compliance with the prescribed system constraints may damage the pneumatic components and adversely affect its positioning accuracy, especially when the system is controlled in real-time environment. Model predictive controller (MPC) is one of the predictive controllers that is able to consider the constraint of the system in its algorithm. Therefore, constrained MPC (CMPC) was proposed in this study to improve the accuracy of pneumatic positioning system while considering the constraints of the system. The mathematical model of pneumatic system was determined by system identification technique and the control signal to the valves were considered as the constraints of the pneumatic system when developing the controller. In order to verify the accuracy and reliability of CMPC, repetitive experiments on the CMPC strategy was implemented. The existing predictive controller, that was used to control the pneumatic system such as predictive functional control (PFC), was also compared. The experimental results revealed that CMPC effectively improved the position accuracy of the pneumatic system compared to PFC strategy. However, CMPC not capable to provide a fast response as PF

Bibliography on heavy vehicle dynamics

http://deepblue.lib.umich.edu/bitstream/2027.42/108243/1/103019.pdfDescription of 103019.pdf : Bibliograph