• Coordinated iterative learning control schemes for train trajectory tracking with overspeed protection

      Sun, Heqing; Hou, Zhongsheng; Li, Dayou (IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2012)
      This work embodies the overspeed protection and safe headway control into an iterative learning control (ILC) based train trajectory tracking algorithm to satisfy the high safety requirement of high-speed railways. First, a D-type ILC scheme with overspeed protection is proposed. Then, a corresponding coordinated ILC scheme with multiple trains is studied to keep the safe headway. Finally, the control scheme under traction/braking force constraint is also considered for this proposed ILC-based train trajectory tracking strategy. Rigorous theoretical analysis has shown that the proposed control schemes can guarantee the asymptotic convergence of train speed and position to its desired profiles without requirement of the physical model aside from some mild assumptions on the system. Effectiveness is further evaluated through simulations.
    • Data-driven model-free adaptive control for a class of MIMO nonlinear discrete-time systems

      Hou, Zhongsheng; Jin, Shangtai; Beijing Jiaotong University, Beijing, China (IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2011)
      In this paper, a data-driven model-free adaptive control (MFAC) approach is proposed based on a new dynamic linearization technique (DLT) with a novel concept called pseudo-partial derivative for a class of general multiple-input and multiple-output nonlinear discrete-time systems. The DLT includes compact form dynamic linearization, partial form dynamic linearization, and full form dynamic linearization. The main feature of the approach is that the controller design depends only on the measured input/output data of the controlled plant. Analysis and extensive simulations have shown that MFAC guarantees the bounded-input bounded-output stability and the tracking error convergence.
    • A high-order internal model based iterative learning control scheme for nonlinear systems with time-iteration-varying parameters

      Yin, Chenkun; Xu, Jian-Xin; Hou, Zhongsheng (IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2010)
      In this technical note, we propose a new iterative learning control (ILC) scheme for nonlinear systems with parametric uncertainties that are temporally and iteratively varying. The time-varying characteristics of the parameters are described by a set of unknown basis functions that can be any continuous functions. The iteratively varying characteristics of the parameters are described by a high-order internal model (HOIM) that is essentially an auto-regression model in the iteration domain. The new parametric learning law with HOIM is designed to effectively handle the unknown basis functions. The method of composite energy function is used to derive convergence properties of the HOIM-based ILC, namely the pointwise convergence along the time axis and asymptotic convergence along the iteration axis. Comparing with existing ILC schemes, the HOIM-based ILC can deal with nonlinear systems with more generic parametric uncertainties that may not be repeatable along the iteration axis. The validity of the HOIM-based ILC under identical initialization condition (i.i.c.) and the alignment condition is also explored.
    • Iterative learning control with unknown control direction: a novel data-based approach

      Shen, Dong; Hou, Zhongsheng; Chinese Academy of Sciences (IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2011)
      Iterative learning control (ILC) is considered for both deterministic and stochastic systems with unknown control direction. To deal with the unknown control direction, a novel switching mechanism, based only on available system tracking error data, is first proposed. Then two ILC algorithms combined with the novel switching mechanism are designed for both deterministic and stochastic systems. It is proved that the ILC algorithms would switch to the right control direction and stick to it after a finite number of cycles. Moreover, the input sequence converges to the desired one under the deterministic case. The input sequence converges to the optimal one with probability 1 under stochastic case and the resulting tracking error tends to its minimal value.
    • Modified iterative-learning-control-based ramp metering strategies for freeway traffic control with iteration-dependent fctors

      Hou, Zhongsheng; Yan, Jingwen; Xu, Jian-Xin; Li, Zhenjiang; Beijing Jiaotong University, Beijing, China (IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2012)
      For a freeway traffic system with strict repeatable pattern, iterative learning control (ILC) has been successfully applied to local ramp metering for a macroscopic freeway environment by formulating the original ramp metering problem as an output tracking, disturbance rejection, and error compensation problem. In this paper, we address the freeway traffic ramp-metering system under a nonstrict repeatable pattern. ILC-based ramp metering and ILC add-on to ALINEA strategies are modified to deal with the presence of iteration-dependent parameters, iteration-dependent desired trajectory, and input constraints. Theoretical analysis and extensive simulations are used to verify the effectiveness of the proposed approaches.
    • A norm optimal iterative learning control based train trajectory tracking approach

      Sun, Heqing; Hou, Zhongsheng; Li, Dayou (IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2012)
      A norm optimal iterative learning control (NOILC) is proposed and applied in train trajectory tracking problem, and it then is extended to the cases with traction/braking constraint. Rigorous theoretical analysis has shown that the proposed approach can guarantee the asymptotic convergence of train speed and position to desired profiles as iteration number goes infinity. Simulation results further demonstrate the effectiveness of the proposed NOILC approach.
    • A novel data-driven control approach for a class of discrete-time nonlinear systems

      Hou, Zhongsheng; Jin, Shangtai; Beijing Jiaotong University, Beijing, China (IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2011)
      In this work, a novel data-driven control approach, model-free adaptive control, is presented based on a new dynamic linearization technique for a class of discrete-time single-input and single-output nonlinear systems. The main feature of the approach is that the controller design depends merely on the input and the output measurement data of the controlled plant. The theoretical analysis shows that the approach guarantees the bounded input and bounded output stability and tracking error monotonic convergence. The comparison experiments verify the effectiveness of the proposed approach.