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Further results on quantized stabilization of nonlinear cascaded systems with dynamic uncertainties

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Science China(Information Sciences) 2016年第7期59卷 202-213页

作者： Tengfei LIU zhong-ping jiang State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Department of Electrical and Computer Engineering Polytechnic School of Engineering New York University

This article studies the quantized partial-state feedback stabilization of a class of nonlinear cascaded systems with dynamic uncertainties. Under the assumption that the dynamic uncertainties are input-to-state practically stable, a novel recursive design method is developed for quantized stabilization by taking into account the influence of quantization and using the small-gain theorem. When the dynamic uncertainty is input-to-state stable, asymptotic stabilization can be achieved with the proposed quantized control law.

关键词： quantized control nonlinear systems dynamic uncertainties asymptotic stabilization

Data-driven cooperative output regulation of multi-agent systems under distributed denial of service attacks

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Science China(Information Sciences) 2023年第9期66卷 5-20页

作者： Weinan GAO zhong-ping jiang State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Department of Electrical and Computer Engineering Tandon School of Engineering New York University

This paper addresses an optimal, cooperative output regulation problem for multi-agent systems with distributed denial of service attacks and unknown system dynamics. Unlike existing studies, the proposed solution is essentially a learning-based control strategy such that one can obtain a distributed control policy with internal models through online data and analyze the resilience of closed-loop systems, both without the precise knowledge of system dynamics in the state-space model. The efficiency of the proposed methodology is validated using computer simulations.

关键词： cooperative output regulation adaptive dynamic programming learning-based control multi-agent systems cybersecurity

Learning-based adaptive optimal output regulation of linear and nonlinear systems:an overview

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Control Theory and Technology 2022年第1期20卷 1-19页

作者： Weinan Gao zhong-ping jiang Department of Mechanical and Civil Engineering College of Engineering and Science Florida Institute of Technology150 W.University Blvd.MelbourneFL32901USA Department of Electrical and Computer Engineering Tandon School of EngineeringNew York UniversitySix MetroTech CenterBrooklynNY11201USA

This paper reviews recent developments in learning-based adaptive optimal output regulation that aims to solve the problem of adaptive and optimal asymptotic tracking with disturbance *** proposed framework aims to bring together two separate topics—output regulation and adaptive dynamic programming—that have been under extensive investigation due to their broad applications in modern control *** this framework,one can solve optimal output regulation problems of linear,partially linear,nonlinear,and multi-agent systems in a data-driven *** will also review some practical applications based on this framework,such as semi-autonomous vehicles,connected and autonomous vehicles,and nonlinear oscillators.

关键词： Adaptive optimal output regulation Adaptive dynamic programming Reinforcement learning Learning-based control

A Lyapunov characterization of robust policy optimization

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Control Theory and Technology 2023年第3期21卷 374-389页

作者： Leilei Cui zhong-ping jiang Department of Electrical and Computer Engineering New York UniversityBrooklynNY 11201USA

In this paper,we study the robustness property of policy optimization(particularly Gauss-Newton gradient descent algorithm which is equivalent to the policy iteration in reinforcement learning)subject to noise at each *** invoking the concept of input-to-state stability and utilizing Lyapunov's direct method,it is shown that,if the noise is sufficiently small,the policy iteration algorithm converges to a small neighborhood of the optimal solution even in the presence of noise at each *** expressions of the upperbound on the noise and the size of the neighborhood to which the policies ultimately converge are *** on Willems'fundamental lemma,a learning-based policy iteration algorithm is *** persistent excitation condition can be readily guaranteed by checking the rank of the Hankel matrix related to an exploration *** robustness of the learning-based policy iteration to measurement noise and unknown system disturbances is theoretically demonstrated by the input-to-state stability of the policy *** numerical simulations are conducted to demonstrate the efficacy of the proposed method.

关键词： Policy optimization Policy iteration(PI)-Input-to-state stability(ISS) Lyapunov's direct method

New results in global stabilization for stochastic nonlinear systems

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Control Theory and Technology 2016年第1期14卷 57-67页

作者： Tao BIAN zhong-ping jiang Control and Networks Lab Department of Electrical and Computer Engineering Tandon School of Engineering New York University 5 Metrotech Center Brooklyn NY 11201 U.S.A.

This paper presents new results on the robust global stabilization and the gain assignment problems for stochastic nonlinear systems. Three stochastic nonlinear control design schemes are developed. Furthermore, a new stochastic gain assignment method is developed for a class of uncertain interconnected stochastic nonlinear systems. This method can be combined with the nonlinear small-gain theorem to design partial-state feedback controllers for stochastic nonlinear systems. Two numerical examples are given to illustrate the effectiveness of the proposed methodology.

关键词： Stochastic nonlinear systems stochastic input-to-output stability stabilization gain assignment small-gain

A junction-by-junction feedback-based strategy with convergence analysis for dynamic traffic assignment

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Science China(Information Sciences) 2016年第1期59卷 30-46页

作者： Tengfei LIU Xuesong LU zhong-ping jiang State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Department of Electrical and Computer Engineering University of Connecticut Department of Electrical and Computer Engineering Polytechnic School of Engineering New York University

By considering the traffic assignment problem as a control problem, this paper develops a new realtime route guidance strategy for accurate convergence of the traffic flows to user equilibrium(UE) or system optimum(SO), in the presence of drivers’ response uncertainties. With the new guidance strategy, the drivers make routing decisions based on the route guidance information from junction to junction. Specifically, instead of total travel cost of every route from origin to destination, the travel cost of every alternative link plus the average cost to destination from the next junction corresponding to the alternative link is sent to the drivers at each specific junction. The drivers’ response to the route guidance information is directly modeled by the splitting rates at the junctions, which are simply negatively correlated with the comparison of related cost information and are able to take into account the drivers’ response uncertainties. With the proposed route guidance strategy, in the case of fixed travel demands, the accurate convergence of the traffic flows to a UE is guaranteed in the presence of drivers’ response uncertainties by using La Salle’s invariance principle. When marginal travel cost information, instead of travel cost information, is sent to the drivers, a system optimum can be achieved under a mild condition on the marginal cost function.

关键词： dynamic traffic assignment user equilibrium(UE) system optimum(SO) convergence La Salle’s invariance principle

Measurement feedback control of nonlinear systems:a small-gain approach

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Journal of Control and Decision 2020年第1期7卷 64-89页

作者： Tengfei Liu zhong-ping jiang State Key Laboratory of Synthetical Automation for Process Industries Northeastern UniversityShenyang 110004People’s Republic of China Department of Electrical and Computer Engineering New York UniversityBrooklynNYUSA

This paper presents a new tool for feedback control design of nonlinear systems in the presence of non-smooth measurement *** introduce a small-gain design approach to robust control of nonlinear uncertain systems with disturbed *** a design ingredient,a modified gain assignment technique for measurement feedback control of nonlinear uncertain systems is *** a recursive control design approach,the closed-loop system is transformed into a network of input-to-state stable(ISS)systems and the influences of the measurement errors are represented by ISS *** feedback control objective is achieved by applying the cyclic-small-gain theorem to the closed-loop ***,event-triggered control of nonlinear systems is studied in a unified framework of measurement feedback control.

关键词： Nonlinear systems measurement error nonlinear small-gain theorem input-to-state stability

A Survey of Recent Results in Quantized and Event-based Nonlinear Control

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International Journal of Automation and computing 2015年第5期12卷 455-466页

作者： zhong-ping jiang Teng-Fei Liu Polytechnic School of Engineering New York University State Key Laboratory of Synthetical Automation for Process Industries Northeastern University

Constructive nonlinear control design has undergone rapid and significant progress over the last three decades. In this paper, a review of recent results in this important field is presented with a focus on interdisciplinary topics at the interface of control,computing and communications. In particular, it is shown that the nonlinear small-gain theory provides a unified framework for solving problems of quantized feedback stabilization and event-triggered control for nonlinear systems. Some open questions in quantized and networked nonlinear control systems are discussed.

关键词： Nonlinear systems nonlinear control quantized cont

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Adaptive dynamic programming for finite-horizon optimal control of linear time-varying discrete-time systems

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Control Theory and Technology 2019年第1期17卷 73-84页

作者： Bo PANG Tao BIAN zhong-ping jiang Control and Networks (CAN) Lab Department of Electrical and Computer Engineering Tandon School of Engineering New York University Brooklyn NY 11201 U.S.A. Bank of America Merrill Lynch One Bryant Park New York NY 70036 U.S.A.

This paper studies data-driven learning-based methods for the finite-horizon optimal control of linear time-varying discretetime systems. First, a novel finite-horizon Policy Iteration (PI) method for linear time-varying discrete-time systems is presented. Its connections with existing in finite-horizon PI methods are discussed. Then, both data-drive n off-policy PI and Value Iteration (VI) algorithms are derived to find approximate optimal controllers when the system dynamics is completely unknown. Under mild conditions, the proposed data-driven off-policy algorithms converge to the optimal solution. Finally, the effectiveness and feasibility of the developed methods are validated by a practical example of spacecraft attitude control.

关键词： Optimal control time-varying system adaptive dynamic programming policy iteration (PI) value iteration(VI)