In this paper, a novel model-free adaptive control (MFAC) algorithm based on dual successive projection (DuSP)-MFAC method is proposed, and it analyzed using the introduced DuSP symmetrically similar structures of controller its parameter estimator MFAC. Then, proposed DuSP-MFAC scheme successfully implemented in an autonomous car "Ruilong" for lateral tracking problem via converting trajectory into stabilization by preview-deviation-yaw angle. This MFAC-based was tested demonstrated...

This paper proposes a constrain spatial adaptive iterative learning controller (CSAILC) for the displacement-speed trajectory tracking of automatic train control system with unknown parametric/nonparametric uncertainties and speed constraints. First, nonlinear dynamic model operation is transformed from temporal domain into utilizing state differentiator. Besides, displacement-related are updated in iteration axis. Furthermore, barrier function involved to satisfy constraint, corresponding...

An iterative learning control based on Automatic Train Operation is proposed for repetitively running train to deal with trajectory tracking problem under iteration-varying operation condition and certain speed constrains. Iteration-varying considered in this paper focuses the air resistance coefficient of train, which may be completely different at any two consecutive processes due weather conditions. To eliminate influence caused by conditions, data measured temperature sensors used. In...

This paper proposes a novel model-free adaptive control (MFAC) strategy for urban road traffic network via perimeter based on dynamic linearization technique and predictive control. The accurate flow model of the is replaced by equivalent data model. Based idea control, current action obtained solving online, at each sampling coordinate, finite horizon closed-loop optimal problem. robustness MFAC to time-varying desired vehicle accumulation, random demand macroscopic fundamental diagram...

Abstract In this paper, a point‐to‐point iterative learning control strategy for cascaded multibody high‐speed train (HST) system with model uncertainty and external disturbance is designed to address specified given desired points tracking problem. The proposed method, which only used point information rather than whole trajectory information, improve the multiple‐point accuracy by enjoying repetitiveness of an HST. A norm‐optimal method employed in ILC operating framework analyze HST...

In this study, a data-driven adaptive iterative learning Kalman consensus filtering (DD-AILKCF) method is designed for high-speed trains to address the parameter identification and speed consistent optimal estimation problem. The nonlinear train dynamics model transformed into linear-like state-space by using Full Form Dynamic Linearization (FFDL) technique. Meanwhile, four types of sensors are used obtain different kinds datasets implement multi-sensor system. proposed in paper consists two...

An iterative learning control (ILC) based on Automatic Train Operation (ATO) is proposed to address train speed tracking problem under iteration-varying operation condition, that is, the air resistance coefficients of at any two consecutive iterations are completely different. Using data measured by sensors, repetitive requirement traditional ILC partially relaxed in method. The effectiveness method verified theoretical analysis and numerical simulation.

This work investigates the problem of random successive data dropout at output side stochastic linear systems and presents a novel updating scheme (SUS) based on iterative learning control (ILC) to avoid failures due loss. In particular, successively lost in latest iteration is compensated via predictive information estimated successfully with same time instant label previous by multi-step model. Mathematical induction used demonstrate convergence proposed ILC scheme. Lastly, simulation...

In this paper, a novel data-driven iterative learning control (ILC) scheme is proposed for class of constrained linear time-invariant (LTI) discrete-time systems with unknown system matrix. First, we only use the input and output information from previous iterations to estimate Markov matrix obtained by lifted technique. Second, three types constraints, which include constraint, constraint change rate between two iteration index, are transferred into inequality in unified manner. And then,...

This paper develops spatial adaptive iterative learning control (SAILC) method for the Automatic Train Operation (ATO) of high speed train in order to drive follow desired displacement-speed trajectory. By utilizing state differentiator, temporal nonlinear system is transformed a system. The uncertainties which are space dependent learned by parametric updating law. adopting composite energy function, convergence SAILC obtained. Furthermore, error domain under iteration-varying initial...

In this work, a novel model free adaptive control method based on mathematical algorithm named double successive projection (DSP-MFAC) is proposed, from which it can be inferred that conventional MFAC only special case of DSP-MFAC with the Cartesian product output and input Hilbert space. For general unknown nonlinear system, an innovative partial form dynamic linearization (PFDL) technique first presented new concept pseudo gradient (PG). Then, controller for system designed PG aid DSP...

An iterative learning control scheme for linear dynamic systems with iteration-varying pass length is proposed. The lost positions information filled the estimated system outputs. Using lifted-system framework, both asymptotic stability and monotonic convergence criterion can be obtained maximum (MPL) error. Finally, a simulation which based on of an electrical stimulation gait assistance studied.