Youqing Wang

ILC for networked nonlinear systems with unknown control direction through random Lossy channel

Abstract The iterative learning control is constructed for the discrete-time networked nonlinear systems with random measurement losses and unknown control direction, which have not been studied simultaneously in literature. Differing from the conventional Bernoulli random variable model, the random packet loss is modeled by an arbitrary stochastic sequence with bounded length requirement, which is a new model of realistic packet losses. A novel regulating approach based on truncations is introduced to make the proposed algorithm find the correct control direction adaptively, and then guarantee the almost sure convergence property. Therefore, this paper has three major innovations compared with reported studies, namely, the stochastic sequence model of packet loss, the novel control direction regulation method, and the almost sure convergence property of the proposed algorithm. An illustrative example shows the effectiveness of the proposed approach.

Unbiased Minimum Variance Fault and State Estimation for Linear Discrete Time-Varying Two-Dimensional Systems

The fault and state estimation problem is addressed for a class of linear discrete time-varying two-dimensional systems subject to state and measurement noises. Two estimators are proposed to compute the estimation of the system state and/or fault recursively, both of which are unbiased with minimum variance. Through formulating the estimation problem as the solvability problem of the corresponding matrix equations of estimator gains and system constraint, the necessary and sufficient condition of the existence and the solution for the proposed estimators are given. An example is used to demonstrate the effectiveness of the proposed estimators.

Control Performance Assessment for ILC-Controlled Batch Processes in a 2-D System Framework

In this paper, control performance assessment (CPA) is studied for batch processes controlled by iterative learning control (ILC). A 2-D linear quadratic Gaussian (LQG) benchmark is proposed to assess the performance of ILC in a 2-D framework. Based on the 2-D theory, an ILC-controlled batch process is first converted into a 2-D Roesser model. Subsequently, in order to assess the control performance of the converted 2-D system, the conventional LQG tradeoff curve is upgraded to the LQG performance assessment tradeoff surface. However, the complete knowledge of the system model is required to obtain the LQG tradeoff surface. For system without accurate model knowledge, a novel data-driven CPA method is further proposed. In this case, a novel 2-D closed-loop subspace identification method is proposed to identify the converted 2-D Roesser system. Based on the identified model, the LQG tradeoff surface can be obtained and utilized to assess the control performance. Overall, several simulation examples verified the feasibility and effectiveness of the proposed method.

Stochastic Point-to-Point Iterative Learning Tracking Without Prior Information on System Matrices

This paper contributes to a point-to-point iterative learning control problem for stochastic systems without prior information on system matrices. The stochastic approximation technique with gradient estimation by random difference is introduced to design the update law for input. It is strictly proved that the input sequence would converge almost surely to the optimal one, which minimizes the averaged tracking performance index. An illustrative simulation shows the effectiveness of the proposed algorithm.

Simultaneous estimation of multiple channel faults for two-dimensional linear systems

ABSTRACT This study focuses on the problem of simultaneous estimation of multiple channel faults for two-dimensional linear systems, which are described by Fornasini–Marchesini second (FM-II) model, and the faults that exist in state equation and measurement equation. By transforming the fault in the measurement equation as augmented state, the FM-II model with faults in the state equation and measurement equation can be rewritten into a singular system. Hence, several observers are proposed for the singular systems, and then the estimation of the faults in the state equation and measurement equation can be obtained. Using Lyapunov stability theory, sufficient conditions for the existence of the asymptotically stable observer and uniformly ultimately bounded observer are derived in the context of time domain. For the bounded observer, the upper bound of estimation error can be provided referring to the fault bound. Numerical and practical examples are given to demonstrate the effectiveness of the proposed method.

Krein-space based robust H∞ fault estimation for two-dimensional uncertain linear discrete time-varying systems

Abstract In this study, the robust H ∞ fault estimation problem for two-dimensional linear time-varying systems with norm-bounded unknown input, measurement noise, and time-varying process uncertainty is investigated. By introducing an equivalent auxiliary system and a new certain indefinite quadratic form performance function, the system uncertainty can be appropriately considered into the new performance function and the fault estimator design is converted to the minimization problem of a quadratic form. Based on the partially equivalence property between the deterministic quadratic form problem and the Krein space estimation theory, the two-dimensional H ∞ fault estimation problem can be solved via signal deconvolution in Krein space. Through employing projection operation and Riccati-like difference equation in two dimensions, both the recursive form fault estimator and the explicit condition for existence of the estimator are derived. One Darboux equation example is provided to illustrate the effectiveness of the proposed fault estimator.

Convergence analysis of ILC input sequence for underdetermined linear systems

Dear editor, Iterative learning control (ILC) is a kind of intelligent control strategy, applied to those systems that could complete a given task over a finite time interval and repeat it again and again. Since introduced in 1984, it has gained fast developments during the past three decades [1]. In order to analyze the tracking performance of ILC, it is conventional to prove that the actual input sequence converges to the desired input along the iteration axis in most literature. This idea is intuitive as the output is driven by the input, thus better input convergence to the desired input leads to better output tracking performance. However, to ensure the convergence of the input sequence, it is usually assumed that the correlation matrix from the input to the output, i.e., the coupling matrix CB, provided that the system is (A,B,C), is of full-column rank [2,3]. This condition means that a unique desired input could be solved according to the desired trajectory. Therefore, algorithms could be designed to find the unique solution. As a consequence of this requirement, the dimension of the outputs should be not less than the dimension of the inputs when a general multi-input-multi-output (MIMO) system is taken into account. However, in many practical problems, the dimension condition is not satisfied. The system is referred to as a underdetermined system if the dimension of the inputs is larger than that of the outputs in this letter. For such kind of system, few results have been reported on the convergence of the input sequence along the iteration axis. In [4], it was proved that the state error converges to zero for the underdetermined system. In [5, 6], convergence of the modified tracking errors, i.e., tracking errors at selected output positions, was given. In short, the reported results mainly focused on the convergence of the tracking error itself or on the state error rather than on the input error.

Fault Diagnosis and Application to Modern Systems

1Department of Automation, TNList, Tsinghua University, Beijing 100084, China 2Department of Computer Science, Brunel University London, London UB8 3PH, UK 3School of Instrument Science and Opto-Electronics Engineering, Beihang University, Beijing 100191, China 4Xi’an High-Tech Institute, Xi’an 710025, China 5College of Electrical Engineering and Automation, Shandong University of Science and Technology, Qingdao 266590, China