Ping He

Bio: Ping He is an academic researcher from Harbin Institute of Technology. The author has contributed to research in topics: Iterative learning control & Time domain. The author has an hindex of 1, co-authored 2 publications receiving 26 citations.

Iterative Learning Identification and Compensation of Space-Periodic Disturbance in PMLSM Systems With Time Delay

Abstract: This paper aims to solve a closed-loop identification problem for the space-periodic force ripple in permanent-magnet linear synchronous motor (PMLSM) systems. Conventional identification schemes use the overall error signal to update estimates. However, the error caused by mechanical vibration and measurement noise could affect and even deteriorate the identification performance. In this paper, a novel iterative learning identification method that utilizes the partial but most pertinent information in the error signal is proposed to identify the force ripple. First, the effective error signal caused by the reference trajectory and the force ripple are extracted by projecting the overall error signal to a subspace. The subspace is spanned by some basis functions selected on the basis of the physical model of the PMLSM and the sinusoidal model of the force ripple. The time delay of the PMLSM system is compensated in these basis functions. Then, a norm-optimal approach is proposed to design the learning gain. The monotonic convergence of the iterative learning identification is further analyzed. Numerical simulation and experiments are provided to validate the proposed method and confirm its feasibility and effectiveness in force ripple identification, as well as its compensation.

Iterative Learning Identification with Bias Compensation for Stochastic Linear Time-Varying Systems

Abstract: A novel iterative learning algorithm is proposed for the identification of linear time-varying (LTV) output-error (OE) systems that perform tasks repetitively over a finite-time interval. Conventional LTV system identification normally relies on recursion algorithms in time domain, which are unable to follow fast changing parameters because of an inevitable estimation lag. To overcome this problem, an extra iteration axis is introduced besides the time axis in the parameter estimation process, and identification algorithm performed in iteration domain is proposed. Firstly, a norm-optimal identification approach is presented to balance the tradeoff between convergence speed and noise robustness. Then a bias compensation algorithm is further proposed to improve the estimation accuracy. Finally, numerical examples are provided to validate the algorithm and confirm its effectiveness. The algorithm is effective to estimate both slow and abrupt parameter changes with high accuracy without estimation lags.

Extended State Observer-Based Data-Driven Iterative Learning Control for Permanent Magnet Linear Motor With Initial Shifts and Disturbances

Abstract: In this paper, an extended state observer-based data-driven iterative learning control [extended state observer (ESO)-based DDILC] is developed for a permanent magnet linear motor (PMLM). The PMLM is formulated mathematically by using a general nonlinear discrete-time system with consideration of exogenous disturbances. Then, a new iterative dynamic linearization (IDL) is proposed to equivalently reformulate the nonlinear PMLM system with a linear input–output incremental form involving iteration-varying initial states and disturbances. The concept of ESO is introduced into iteration direction to iteratively estimate the random initial states and disturbances as well as their corresponding partial derivatives by considering all of them as a whole extended state. The proposed ESO-based DDILC scheme contains a learning control algorithm and a gradient parameter updating algorithm obtained from two distinct objective functions, respectively. Moreover, the proposed method is data-driven and no explicit model is involved. Theoretical analysis shows the robustness of the proposed method in the presence of iteration-varying initial shifts and disturbances. The simulation on PMLM is conducted to confirm the validity and applicability of the ESO-based DDILC.

Robust Predictive Current Control of PMLSM With Extended State Modeling Based Kalman Filter: For Time-Varying Disturbance Rejection

Abstract: This paper proposed a robust deadbeat predictive current control (PCC) of the permanent magnet linear synchronous machine (PMLSM) with an extended state modeling (ESM) based Kalman filter (KF) for both the state and disturbance estimation. First, the disturbance dynamics of the PMLSM electrical subsystem is analyzed in detail and then the ESM is constructed as considering the disturbance as a higher order integrator motivated by the main idea of the extended state observer. Second, the KF for the current prediction with reduced noises and the disturbance estimation due to the parameter variation is designed combining with the ESM. Furtherly, the robust PCC is introduced with the ESM-based KF. Finally, the parameter tuning for the ESM-based KF is discussed with the discrete simulation and then the experimental results are given under the single current closed loop and the double cascade position-current loop with linear-varying parameter. Both the simulation and experimental results verify the effectiveness of the proposed scheme.

Online Iterative Learning Compensation Method Based on Model Prediction for Trajectory Tracking Control Systems

Abstract: In this article, to guarantee the good tracking performance of the precision motion system for various tracking tasks, an online iterative learning compensation method is proposed for closed-loop motion control systems. The prediction model is based on the closed-loop model of the linear second-order system with a proportional-integral-derivative controller, and an estimation term is added to deal with the influence of slow-varying uncertain disturbances. On the basis of the accurate state prediction, the dynamical feedforward compensation can be obtained, which suppresses the tracking error caused by the dynamical lag. Furthermore, in order to simultaneously compensate the errors caused by nonlinear factors such as uncertain disturbances and to guarantee the smoothness of the compensated trajectory, the optimal compensation gain is determined through online iterative calculation. The online iterative approach is similar to iterative learning control, but does not require several offline iterations of a repeating trajectory. Comparative experiments are carried out on an industrial motion stage. Various experimental results consistently demonstrate that the proposed compensation scheme can achieve the tracking accuracy comparable to iterative learning, while maintaining the robustness to trajectory changes and uncertain disturbances without reoffline iteration.

Data-Driven Iterative Feedforward Tuning for a Wafer Stage: A High-Order Approach Based on Instrumental Variables

Abstract: The feedforward controller plays an important role in the achievement of high servo performance of wafer scanning. In this paper, a novel data-driven feedforward tuning method is developed in the presence of noise. Three distinguished features make it different from the existing methods: first, high extrapolation capability to tasks; second, low requirement on the system model; and especially, third, high noise tolerant capability. These superiorities are achieved by a high-order iterative feedforward tuning algorithm based on instrumental variables. It utilizes error data from all past iterations via an integrator in the learning law, yet without the need of the plant model or the sensitivity function. Furthermore, $H_2$ optimization with specified convergence speed constraint is proposed to design the learning gain. Connections and differences between the proposed algorithm and the existing ones are discussed. Experimental results validate the proposed method and confirm its effectiveness and superiority.

Kalman-Filtering-Based Iterative Feedforward Tuning in Presence of Stochastic Noise: With Application to a Wafer Stage

Abstract: Iterative feedforward tuning (IFFT) enables high performance for motion systems that perform varying tasks without the need for system models. In this paper, IFFT is employed for a wafer stage to achieve good trajectory tracking performance and excellent disturbance compensation ability. Recently, the instrumental variable (IV) approach has been introduced into IFFT algorithms (IV-IFFT), enabling unbiased estimates for the parameters of a feedforward controller in the presence of stochastic noise. However, the estimation variances achievable with IV-IFFT are larger than zero. The aim of this paper is to develop an IFFT algorithm that enables unbiased estimates with zero asymptotic variances, which can be achieved by the simultaneous use of the Kalman filtering (KF) approach and the IV approach in IFFT, yielding the KF-IV-IFFT algorithm. The different roles of KF and IV approaches to improve the noise-tolerant capability of IFFT are also revealed. Experimental results obtained on a wafer stage confirm the practical relevance of the proposed KF-IV-IFFT algorithm.