# Dong ShenRenmin University of China | RUC · School of Mathematics

Dong Shen

PhD

PI, Distributed Artificial Intelligence Lab

## About

195

Publications

12,822

Reads

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2,248

Citations

Citations since 2016

Introduction

I am currently interested in iterative learning control under various special conditions such as data dropouts, sampling, quantization, iteration-varying reference etc. I also spend part time on distributed/decentralized estimation, control, and optimization for complex systems such as multi-agent systems, large scale systems, innerconnected systems, etc. The main focus is on the stochastic control and optimization.

Additional affiliations

January 2020 - present

July 2019 - August 2019

January 2018 - December 2019

Education

September 2005 - July 2010

September 2001 - July 2005

## Publications

Publications (195)

Accurate pattern transfer in wafer scanners necessitates the wafer stage and the reticle stage executing a coordinated motion with the synchronization error in terms of nanometers. In an attempt to cope with this challenging issue, a cross-coupling iterative learning control (ILC) with two inputs and two outputs is proposed and then decomposed into...

This paper investigates consensus control problem for a class of distributed parameter type multi-agent differential inclusion systems with state time-delay by utilizing iterative learning control (ILC). Unlike most ILC literature of nonlinear distributed parameter systems that require an identical virtual leader, the virtual leader is iteratively...

In this paper, we introduce a modified delayed perturbation of discrete matrix exponential for impulsive linear discrete delay systems with non-permutable matrices. Using the Z\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgre...

This study uses a multistage learning mechanism concept to investigate the accelerated learning control for stochastic systems. In this mechanism, the learning iterations are divided into successive stages, with each stage comprising several iterations. The learning gain is constant in each stage to accelerate the learning process and decreases it...

In this paper, we introduce a new kind of conformable stochastic impulsive differential systems (CSIDS) involving discrete distribution of Bernoulli. For random discontinuous trajectories, we modify the tracking error of piecewise continuous variables by a zero-order holder. First, the improved P -type and PD α -type learning laws of the random ite...

In this study, a quantized iterative learning control method with an encoding–decoding mechanism is investigated for networked control systems with constrained transmission bandwidths and random data dropouts at both the measurement and actuator sides. The intermittent update principle is used to address the problem of data asynchronism caused by t...

This article studies the conflicting goals of high-precision tracking and quick convergence speed, which is a longstanding problem in the learning control of stochastic systems. In such systems, a decreasing gain sequence is necessary to ensure the asymptotic convergence of the generated input sequence to a fixed limit. However, the convergence spe...

This article investigates the distributed iterative learning control for heating ventilation and air condition (HVAC) systems. Large‐scale building HVAC systems consisting of several subsystems are generally disturbed by the external environment and random human activities. The control objective is to achieve all units of the large‐scale building c...

In this study, we investigate the accelerated learning control schemes for point-to-point tracking systems (PTSs) with measurement noise. The asymptotic convergence of the generated input sequence has been a long-standing open issue for point-to-point tracking problems because there are infinite possible input candidates that can drive the system d...

In this paper, we introduce iterative learning control (ILC) schemes with varying trial lengths (VTL) to control impulsive multi-agent systems (I-MAS). We use domain alignment operator to characterize each tracking error to ensure that the error can completely update the control function during each iteration. Then we analyze the system’s uniform c...

This article considers the learning consensus problem of distributed parameter type multi‐agent differential inclusion systems including parabolic type and hyperbolic type. By imposing a Lipschitz condition on a set‐valued mapping and utilizing distributed P$$ P $$‐type iterative learning consensus control protocols, an iterative learning process i...

Kernels offer an effective alternative to implicitly embed the original data into a higher or infinite-dimensional space in support vector machines. Kernel learning, which attempts to determine optimal kernel functions to evaluate relationships between data, has garnered increasing interest. Employing multiple kernels to enhance optimality and gene...

In reality such as a rehabilitation training, a repetitive control is necessary but the operational lengths may be iteration varying due to health condition. For the issue, this article investigates an intermittent optimal learning control scheme that considers the partially available information for the learning processing. The performance index i...

In this paper, an indirect adaptive iterative learning control (iAILC) scheme is proposed for both linear and nonlinear systems to enhance the P-type controller by learning from set points. An adaptive mechanism is included in the iAILC method to regulate the learning gain using I/O measurements in real time. An iAILC method is first designed for l...

Due to electromagnet uncertainties, rail irregularities and other external disturbances, an excessively small levitation gap of electromagnetic levitation system (EMLS) may lead to serious deadlock, which would severely endanger the operational safety of high-speed maglev trains. A highly reliable levitation controller must be designed with the inh...

In this study, we consider the learning-tracking problem for stochastic systems through unreliable communication channels. The channels suffer from both multiplicative and additive randomness subject to unknown probability distributions. The statistics of this randomness, such as mean and covariance, are nonrepetitive in the iteration domain. This...

The networked structure has attracted significant attention due to high demand for industrial systems and rapid developments of network communication. Among various network randomness, fading is a common phenomenon, which can lead to signal attenuation, distortion, loss, and interference. This study concentrates on the point-to-point tracking probl...

This article addresses the batch-based learning consensus for linear and nonlinear multiagent systems (MASs) with faded neighborhood information. The motivation comes from the observation that agents exchange information via wireless networks, which inevitably introduces random fading effect and channel additive noise to the transmitted signals. It...

In this paper, a noisy output-based direct learning tracking control is proposed for stochastic linear systems with nonuniform trial lengths. The iteration-varying trial length is modeled using a Markov chain for demonstration of the iteration-dependence. The effect of the noisy output is asymptotically eliminated using a prior given decreasing gai...

This paper addresses convergence of iterative learning control for impulsive linear discrete delay systems with randomly varying trial lengths when coefficient matrices of systems are permutable. With the aid of the explicit representation of solutions expressed in discrete matrix delayed exponential, we provide two sufficient conditions of converg...

In this article, we consider quantized learning control for linear networked systems with additive channel noise. Our objective is to achieve high tracking performance while reducing the communication burden on the communication network. To address this problem, we propose an integrated framework consisting of two modules: a probabilistic quantizer...

The learning control strategy is studied for networked stochastic systems, where the output and input data are transmitted through multiple independent fading channels. The traditional P-type learning control scheme is revised according to the specific fading positions, where the constant learning gain is replaced by a variable one to suppress the...

This work presents a novel design framework of adaptive iterative learning control (ILC) approach for a class of uncertain nonlinear systems. By using the closed-loop reference model that can be viewed as an observer, the proposed adaptive ILC approach can be adapted to deal with the output tracking problem of nonlinear systems with unavailable sys...

Iterative learning control (ILC) has been well recognized for its output tracking ability in systems that perform repetitive tasks such as robot manipulators. In practice, however, the application of ILC remains challenging as it generally requires the repetition of the initial settings and such industrial manipulators do not provide measurements o...

This paper contributes to an efficiently computational algorithm of collaborative learning model predictive control for nonlinear systems and explores the potential of subsystems to accomplish the task collaboratively. The collaboration problem in the control field is usually to track a given reference over a finite time interval by using a set of...

In this paper, we adopt D-type and PD-type learning laws with the initial state of iteration to achieve uniform tracking problem of multi-agent systems subjected to impulsive input. For the multi-agent system with impulse, we show that all agents are driven to achieve a given asymptotical consensus as the iteration number increases via the proposed...

With fast developments in communication technologies, a large number of practical systems adopt the networked control structure. For this structure, the fading problem is an emerging issue among other network problems. It has not been extensively investigated how to guarantee superior control performance in the presence of unknown fading channels....

In this paper, we discuss the consensus tracking problem by introducing two iterative learning control (ILC) protocols (namely, Dα-type and PDα-type) with initial state error for fractional-order homogenous and heterogenous multi-agent systems (MASs), respectively. The initial state of each agent is fixed at the same position away from the desired...

This paper studies iterative learning control (ILC) using faded measurements without system information. The measurements are transmitted through fading channels, where the fading phenomenon is modelled by a multiplicative random variable. The system matrices are assumed unknown a priori and a random difference technique is applied to estimate the...

With the wide use of networks in repetitive systems, channels between a plant and controller may experience random fading, which is a common problem in long-distance wireless data communication. However, the control problem over fading channels is far from resolved. In this paper, we investigate learning control over fading channels to gradually im...

A novel data-driven learning control scheme is proposed for unknown systems with unknown fading sensor channels. The fading randomness is modeled by multiplicative and additive random variables subject to certain unknown distributions. In this scheme, we propose an error transmission mode and an iterative gradient estimation method. Unlike the conv...

This article considers the zero-error tracking problem of quantized iterative learning control for a general networked structure where the data are quantized and transmitted through limited communication channels at both measurement and actuator sides. An encoding and decoding mechanism is introduced into a simple uniform quantizer. The system outp...

In this paper, we design P-type and PIβ-type iterative learning control update laws to deal with the consensus tacking problem for nonlinear fractional-order multi-agent systems with both fixed and iteration-varying communicating graphs. The graphs for the multi-agent system are of the leader-follower type. The nonlinear function in each agent is r...

In this paper, we propose an iterative learning control strategy to track a desired trajectory for a class of uncertain systems governed by nonlinear differential inclusions. By imposing Lipschitz continuous condition on a set‐valued mapping described by a closure of the convex hull of a set and using D‐type and PD‐type updating laws with initial i...

In this paper, iterative learning control (ILC) is considered to solve the tracking problem of time-varying linear stochastic systems with randomly varying trial lengths. Using the two-dimensional Kalman filtering technique, the authors can establish a recursive framework for designing the learning gain matrix along both time and iteration axes by...

Background and Objectives
: Glycemic control with unannounced meals is the major challenge for artificial pancreas. In this study, we described the performance and safety of learning-type model predictive control (L-MPC) for artificial pancreas challenged by an unannounced meal in type 1 diabetes (T1D).
Methods
: This closed-loop (CL) system was t...

In this work, a new design framework of adaptive iterative learning control (ILC) approach for a class of uncertain nonlinear systems is presented. By making use of the closed-loop reference model which works as an observer, the developed adaptive ILC method is able to be adopted to deal with the output tracking problem of nonlinear systems without...

Learning control is investigated to solve the tracking problem for linear systems via unreliable networks with random data dropouts. By using an encoding-decoding mechanism-based finite-level uniform quantizer, the communication burden is remarkably reduced while retaining a precise tracking performance. An intermittent update principle is adopted...

Epilepsy is one of the most common neurological disorders. Neuro-modulation becomes a promising way to address it. For an effective modulation, closed-loop mode is necessary but difficult. A control algorithm, which can adjust itself to get desired suppression of epileptic activity, is in great need. In this paper, active disturbance rejection cont...

Multisensor systems are widely applied to realize the comprehensive monitoring and control as they feature multiple individual sensors/outputs. In such systems, different sensors can receive different types of operation signals, such as pressure, temperature, and volume. The desired references for different sensors may conflict in that an input sig...

The nonuniform trial length problem, which causes information dropout in learning, is very common in various control systems such as robotics and motion control systems. This paper presents a comprehensive survey of recent progress on iterative learning control with randomly varying trial lengths. Related works are reviewed in three dimensions: mod...

This paper considers the consensus tacking problem for nonlinear fractional‐order multiagent systems by presenting a PDα‐type iterative learning control update law with initial learning mechanisms. The asymptotical convergence of the proposed distributed learning algorithm is strictly proved by using the properties of fractional calculus. A suffici...

Stochastic iterative learning control (ILC) is designed for solving the tracking problem of stochastic linear systems through fading channels. Consequently, the signals used in learning control algorithms are faded in the sense that a random variable is multiplied by the original signal. To achieve the tracking objective, a two-dimensional Kalman f...

The present work aims to develop a novel adaptive iterative learning control(AILC) method for nonlinear multiple input multiple output (MIMO) systems that execute various control missions with iteration-varying magnitude-time scales. In order to reduce the variations of the systems, this work proposes a series of time scaling transformations to nor...

This paper studies the iteration varying trail lengths problem for high‐order continuous‐time nonlinear systems, where the initial state may deviate from the desired value and the sign of input gain is unknown. First, to deal with the general nonlinear systems, a fuzzy approximation technique is applied for each dimension of the nonlinear function...

This paper proposes adaptive iterative learning control schemes for robot manipulator systems with iteration-varying lengths. To prove the asymptotical convergence of the joint position tracking error along the iteration axis, this paper develops a new composite energy function based on the newly introduced auxiliary variables for the analysis. Mor...

This paper presents a novel off-line iterative learning control algorithm for multiple-input-multiple-output time-varying discrete stochastic systems. Using the steady-state Kalman filtering method, we provide a novel framework for the selection of optimal/sub-optimal fixed learning gain matrices in real applications, which is convenient for engine...

A variable gain feedback PDα-type iterative learning control (ILC) update rate is proposed for the fractional-order nonlinear systems with time-delay. The learning update rate combines the open loop and closed loop strategy, in which the system’s current tracking error and the previous iterative control of the tracking error is simultaneously used...

This paper deals with iterative Jacobian-based recursion technique for the root-finding problem of the vector-valued function, whose evaluations are contaminated by noise. Instead of a scalar step size, we use an iterate-dependent matrix gain to effectively weigh the different elements associated with the noisy observations. The analytical developm...

This paper proposes an iterative learning control (ILC) algorithm with gain adaptation for discrete-time stochastic systems. The algorithm is based on Kesten's accelerated stochastic approximation algorithm. The gain adaptation uses only tracking error information, and, hence, is a data-driven adaptation approach. If stochastic noises account for a...

In this paper, we apply iterative learning control to both linear and nonlinear fractional-order multi-agent systems to solve consensus tacking problem. Both fixed and iteration-varying communicating graphs are addressed in this paper. For linear systems, a PD α-type update law with initial state learning mechanism is introduced by virtue of the me...

This paper applies learning control to repetitive systems over fading channels at both output and input sides to improve tracking performance without applying restrictive fading conditions. Both multiplicative and additive randomness of the fading channel are addressed, and the effects of fading communication on the data are carefully analyzed. A d...

This paper considers a novel distributed iterative learning consensus control algorithm based on neural networks for the control of heterogeneous nonlinear multiagent systems. The system's unknown nonlinear function is approximated by suitable neural networks; the approximation error is countered by a robust term in the control. Two types of contro...

In this paper, we present a numerical solution for a finite time complete tracking problem based on the iterative learning control technique for dynamical systems governed by partial differential inclusions of parabolic type with noninstantaneous impulses. By imposing a standard Lipschitz condition on a set-valued mapping and applying conventional...

This paper investigates an iterative learning control for single-input, single-output, and linear time-invariant discrete system. The special design of the learning gain matrix is introduced, where a finite uniform quantizer is incorporated with an encoding and decoding mechanism to realize the zeroerror convergence of a tracking problem. Furthermo...

In this paper, the iterative learning control is introduced to solve the consensus tracking problem of a multi-agent system with random noises and measurement range limitation. A distributed learning control algorithm is proposed for all agents by utilising its nearest neighbour measured information from previous iterations. With the help of the st...

This chapter presents the two-dimensional (2D) techniques for addressing the tracking problem of linear discrete-time stochastic systems with varying trial lengths. The Kalman filtering technique is applied to derive the recursive learning gain matrix which guarantees the mean square convergence of the input error to zero. As a consequence, the tra...

In this chapter, we will extend the idea on ILC design with randomly varying trial lengths to nonlinear dynamic systems. Different from Chaps. 2 and 3, this chapter will employ an iteratively moving average operator into the ILC scheme.

This chapter considers the adaptive iterative learning control (ILC) for continuous-time parametric nonlinear systems with partial structure information under iteration-varying trial length environments. In particular, two types of partial structure information are taken into account. The first type is that the parametric system uncertainty can be...

In this chapter, sampled-data iterative learning control (ILC) method is extended to a class of continuous-time nonlinear systems with iteration-varying trial lengths. In order to propose a unified ILC algorithm, the tracking errors will be redefined when the trial length is shorter or longer than the desired one. Based on the modified tracking err...

This chapter proposes robust iterative learning control schemes for continuous-time nonlinear systems with various nonparametric uncertainties under nonuniform trial length circumstances. The nonuniform trial length is described by a random variable, which causes a random data missing problem while designing and analyzing algorithms for the precise...

This chapter proposes ILC for discrete-time affine nonlinear systems with randomly iteration-varying lengths. No prior information on the probability distribution of random iteration length is required prior to controller design. The conventional P-type update law is used with a modified tracking error because of randomly iteration-varying lengths....

To further improve the learning performance, this chapter will propose two novel ILC schemes for discrete-time linear systems with randomly varying trial lengths. In contrast to Chaps. 2 and 3 that advocate to replace the missing control information by zero, the proposed learning algorithms in this chapter are equipped with a random searching mecha...

This chapter proposes adaptive iterative learning control (ILC) schemes for continuous-time parametric nonlinear systems with iteration lengths that randomly vary. As opposed to the existing ILC works that feature nonuniform trial lengths, this chapter is applicable to nonlinear systems that do not satisfy the globally Lipschitz continuous conditio...

Similar to Chap. 2, this chapter also considers a class of discrete-time linear systems with randomly varying trial lengths. However, in contrast to Chap. 2, this chapter aims to avoid using the traditional \(\lambda \)-norm in convergence analysis which may lead to a non-monotonic convergence. Compared to Chap. 2, the main contributions of the cha...

This chapter presents the novel formulation and idea to address the tracking control problem for discrete-time linear systems with randomly varying trial lengths. An ILC scheme with an iteration-average operator is introduced for tracking tasks with nonuniform trial lengths, which thus mitigates the requirement on classic ILC that all trial lengths...

This book presents a comprehensive and detailed study on iterative learning control (ILC) for systems with iteration-varying trial lengths. Instead of traditional ILC, which requires systems to repeat on a fixed time interval, this book focuses on a more practical case where the trial length might randomly vary from iteration to iteration. The iter...

This chapter proposes a convergence analysis of ILC for discrete-time linear systems with randomly iteration-varying lengths. No prior information is required on the probability distribution of randomly iteration-varying lengths. The conventional P-type update law is adopted with Arimoto-like gains and causal gains. The convergence both in almost s...