Luy Tan Nguyen

Verified email at iuh.edu.vn

Industrial University of Ho Chi Minh City



                                         

http://researchid.co/i-1326-2019
11

Scopus Publications

189

Scholar Citations

8

Scholar h-index

8

Scholar i10-index

Scopus Publications

  • Event-Triggered Distributed H<inf>∞</inf>Constrained Control of Physically Interconnected Large-Scale Partially Unknown Strict-Feedback Systems
    Luy Nguyen Tan

    IEEE Transactions on Systems, Man, and Cybernetics: Systems, ISSN: 21682216, eISSN: 21682232, Pages: 2444-2456, Published: April 2021 Institute of Electrical and Electronics Engineers (IEEE)
    In this paper, an event-triggered distributed <inline-formula> <tex-math notation="LaTeX">${ {\\mathcal {H}}_{\\infty }}$ </tex-math></inline-formula> constrained control algorithm is designed for physically interconnected large-scale partially unknown strict-feedback systems with constrained-input and external disturbance. The advantage of both physical interconnection and communication is synchronously exploited for the scheme. First, an event-triggered feedforward control policy is proposed to transform control of physically interconnected large-scale systems into equivalent event-triggered control of decoupled multiagent systems. Then, an event-triggering condition and an event-triggered feedback control algorithm are designed to learn the optimal control policy and the disturbance policy in the worst case. The algorithm eliminates identifier, actor, and disturber neural networks and also relaxes the persistent excitation condition. It guarantees that the closed-loop dynamics is stabilized and the cost function is converged to the bounded <inline-formula> <tex-math notation="LaTeX">${\\mathcal {L}}_{2}$ </tex-math></inline-formula>-gain optimal value while the Zeno phenomenon is excluded. Finally, the effectiveness of the proposed algorithm is verified through simulation results of event-triggered distributed control of a physically interconnected constrained-torques multimobile robot system.

  • Distributed H<inf>∞</inf>Optimal Tracking Control for Strict-Feedback Nonlinear Large-Scale Systems with Disturbances and Saturating Actuators
    Luy Nguyen Tan

    IEEE Transactions on Systems, Man, and Cybernetics: Systems, ISSN: 21682216, eISSN: 21682232, Pages: 4719-4731, Published: November 2020 Institute of Electrical and Electronics Engineers (IEEE)
    In this paper, a novel distributed <inline-formula> <tex-math notation="LaTeX">${{H}_{ \\infty }}$ </tex-math></inline-formula> optimal tracking control scheme is designed for a class of physically interconnected large-scale nonlinear systems in the presence of strict-feedback form, external disturbance and saturating actuators. First, by designing feedforward control, the distributed <inline-formula> <tex-math notation="LaTeX">${{H}_{\\infty }}$ </tex-math></inline-formula> optimal tracking control problem of a physically interconnected large-scale system is transformed into equivalent control of a decoupled multiagent system. Subsequently, a feedback control algorithm is designed to learn the optimal control input and the worst-case disturbance policy. The algorithm guarantees that the function approximation error and the distributed tracking error are uniformly ultimately bounded while the cost function converges to the bounded <inline-formula> <tex-math notation="LaTeX">$ {L}_{{2}}$ </tex-math></inline-formula>-gain optimal value. Finally, the effectiveness of the proposed scheme is demonstrated by simulation results of distributed control for the mobile multirobot system.

  • Event-triggered distributed H<inf>∞</inf> control of physically interconnected mobile Euler–Lagrange systems with slipping, skidding and dead zone
    Luy Nguyen Tan

    IET Control Theory and Applications, ISSN: 17518644, eISSN: 17518652, Pages: 438-451, Published: 12 February 2020 Institution of Engineering and Technology (IET)
    This study addresses an event-triggered distributed ℋ ∞ control method by extending traditional zero-sum differential games for physically interconnected non-holonomic mobile mechanical multi-agent systems with external disturbance and slipping, skidding and dead-zone disturbances. Initially, a problem of physically interconnected kinematic and dynamic control is transformed into an equivalent problem of event-triggered distributed ℋ ∞ control. Subsequently, the traditional two-player zerosum differential game is extended to a three-player zero-sum differential game, where a new player is included to approximate the worst dead-zone disturbance. To find player policies, an event-triggering condition and an event-triggered control law are proposed via neural networks (NNs). Although an NN weight-tuning law is designed on the basis of adaptive dynamic programming techniques, it can relax identification procedures for unknown drift dynamics and persistent excitation conditions. It also guarantees that the closed system is stable and the cost function converges to the bounded ℒ 2 -gain optimal value, while the Zeno behaviour is excluded. Finally, the effectiveness of the proposed method is verified by an application to a dead-zone torque multi-mobile robot system through numerical simulations.

  • Distributed optimal control for nonholonomic systems with input constraints and uncertain interconnections
    Luy Nguyen Tan

    Nonlinear Dynamics, ISSN: 0924090X, eISSN: 1573269X, Pages: 801-817, Published: 1 July 2018 Springer Science and Business Media LLC
    This paper studies a distributed optimal tracking control method for nonholonomic mobile mechanical multi-agent systems under complex conditions such as input constraints, the presence of both kinematic and dynamic disturbances, and uncertain interconnections. Initially, novel feed-forward control inputs are proposed to transform the inherently separate systems of kinematics and dynamics into an equivalent integrated system. Successively, an online distributed $$\\mathcal {L}_2$$L2-bounded optimal control algorithm is designed by utilizing adaptive dynamic programming and the theory of cooperative differential graphical games. In the algorithm, a single neural network instead of three for each agent is chosen, and the online weight-tuning laws for which are designed without identifying uncertain parameters directly or indirectly. Additionally, the optimal control and worst disturbance policies are synchronously updated in only one iterative loop. It is shown that during the convergence of the value functions to the approximate optimal values when the agents perform the algorithm, overall tracking and function approximation errors are uniformly ultimately bounded. Finally, as a successful application of the study, control of the wheeled mobile multi-robot system is discussed through simulations.

  • Omnidirectional-Vision-Based Distributed Optimal Tracking Control for Mobile Multirobot Systems With Kinematic and Dynamic Disturbance Rejection
    Luy Nguyen Tan

    IEEE Transactions on Industrial Electronics, ISSN: 02780046, Pages: 5693-5703, Published: July 2018 Institute of Electrical and Electronics Engineers (IEEE)
    Although various methods of controlling mobile robots have been studied, the distributed tracking control problem for uncertain nonholonomic mobile multirobot (NMMR) systems in an optimal manner with disturbance rejection for both kinematics and dynamics has not been thoroughly solved. This paper, therefore, devotes a novel method to solve the problem with application to real NMMR systems equipped with omnidirectional vision sensors, of which parameters are unknown or uncalibrated. First, the distributed optimal tracking control problem of a separate system in the presence of kinematic and dynamic disturbances is transformed into the equivalent optimal regulation with disturbance rejection of an integrated system. Then, the theory of differential games is utilized to formulate the integrated system into coupled Hamilton–Jacobi–Isaac equations, of which the solutions are approximated in real time by designed algorithms. By the Lyapunov theory, it is proven that the algorithms converge, and the closed-loop systems are stable. Finally, compared simulations and experiments for a group of three robots are provided to show the effectiveness of the proposed algorithms.

  • Distributed cooperative H<inf>∞</inf> optimal tracking control of MIMO nonlinear multi-agent systems in strict-feedback form via adaptive dynamic programming
    N. T. Luy

    International Journal of Control, ISSN: 00207179, eISSN: 13665820, Pages: 952-968, Published: 3 April 2018 Informa UK Limited
    ABSTRACT The design of distributed cooperative H∞ optimal controllers for multi-agent systems is a major challenge when the agents’ models are uncertain multi-input and multi-output nonlinear systems in strict-feedback form in the presence of external disturbances. In this paper, first, the distributed cooperative H∞ optimal tracking problem is transformed into controlling the cooperative tracking error dynamics in affine form. Second, control schemes and online algorithms are proposed via adaptive dynamic programming (ADP) and the theory of zero-sum differential graphical games. The schemes use only one neural network (NN) for each agent instead of three from ADP to reduce computational complexity as well as avoid choosing initial NN weights for stabilising controllers. It is shown that despite not using knowledge of cooperative internal dynamics, the proposed algorithms not only approximate values to Nash equilibrium but also guarantee all signals, such as the NN weight approximation errors and the cooperative tracking errors in the closed-loop system, to be uniformly ultimately bounded. Finally, the effectiveness of the proposed method is shown by simulation results of an application to wheeled mobile multi-robot systems.

  • Distributed optimal integrated tracking control for separate kinematic and dynamic uncertain non-holonomic mobile mechanical multi-agent systems
    Luy Nguyen Tan

    IET Control Theory and Applications, ISSN: 17518644, eISSN: 17518652, Pages: 3249-3260, Published: 15 December 2017 Institution of Engineering and Technology (IET)
    This study addres,ses a distributed optimal integrated tracking control method with disturbance rejection for separate kinematic and dynamic uncertain non-holonomic mobile mechanical multi-agent ( N M 3 ) systems. Initially, based on the graph theory, the overall tracking systems of agents are defined and the distributed optimal tracking problem of separate kinematics and dynamics is transformed into an equivalent distributed optimal regulation problem of the integrated affine system. Then, neural network (NN)-based adaptive dynamic programming and cooperative differential game theory is utilised for control, in which only one NN is required for each agent. The NN weight-tuning law and the online algorithm is developed to approximate the value function, and synchronously update both optimal control and worst disturbance laws in only one iterative loop. The tracking errors and function approximation errors are proven to be uniformly ultimately bounded using Lyapunov theory. Finally, as applications of the proposed method, control of the wheeled mobile multi-robot system is discussed. The effectiveness of the method is demonstrated by the results of the comparative numerical simulation.

  • Robust adaptive dynamic programming based online tracking control algorithm for real wheeled mobile robot with omni-directional vision system
    Nguyen Tan Luy

    Transactions of the Institute of Measurement and Control, ISSN: 01423312, Pages: 832-847, Published: 1 June 2017 SAGE Publications
    This paper proposes a new method to design an online robust adaptive dynamic programming algorithm (RADPA) for a wheeled mobile robot which is equipped with an omni-directional vision system. To integrate kinematic and dynamic controllers into the unique controller, we transform the strict feedback system dynamics into tracking error dynamics. Then, we propose a control scheme which uses only one neural network rather than three proposed in the actor-critic-based control schemes for the two-player zero-sum game problem. A neural network weight update law is designed for approximating the solution of the Hamilton–Jacobi–Isaacs equation without knowing knowledge of internal system dynamics. To implement the scheme, we propose the online RADPA, in which control and disturbance laws are updated simultaneously in an iterative loop. The convergence and stability of the online RADPA are proven by Lyapunov techniques. Simulations and experiments on a wheeled mobile robot testbed are carried out to verify the effectiveness of the proposed algorithm.

  • Adaptive dynamic programming-based design of integrated neural network structure for cooperative control of multiple MIMO nonlinear systems
    Tan Luy Nguyen

    Neurocomputing, ISSN: 09252312, eISSN: 18728286, Volume: 237, Pages: 12-24, Published: 10 May 2017 Elsevier BV
    Abstract Solving cooperative problems for multi-agent systems, in which the agent׳s artificial behaviors are similar to naturally biological behaviors of agents in practice, is a major challenge. The problems become more complex if the controlled agents are multi-input and multi-output (MIMO) nonlinear systems lacking knowledge of internal system dynamics and affected by external disturbances. In this paper, firstly, based on adaptive dynamic programming, three neural networks (NNs) (actor/disturber/critic) of control schemes for two-player games are integrated into the structure with only one NN, known as integrated NN (INN), with the aim of reducing computational complexity and waste of resources. Secondly, an INN weight update law and an online control algorithm, which updates parameters in one iterative step, are designed to find H ∞ optimal cooperative control solutions. With the aid of Lyapunov theory, we prove that the INN weight approximation errors and the cooperative tracking errors are uniformly ultimately bounded (UUB), and the system parameters converge to the approximately optimal values. Finally, two simulation studies, one of which is compared to three-NN structures in existing literature, are carried out to show the effectiveness of the proposed INN structure.

  • Reinforcement learning-based intelligent tracking control for wheeled mobile robot
    Nguyen Tan Luy, Nguyen Thien Thanh, and Hoang Minh Tri

    Transactions of the Institute of Measurement and Control, ISSN: 01423312, Pages: 868-877, Published: 1 October 2014 SAGE Publications
    This paper proposes a new method to design a reinforcement learning-based integrated kinematic and dynamic tracking control algorithm for a non-holonomic wheeled mobile robot without knowledge of the system’s drift tracking dynamics. The actor critic structure in the control scheme uses only one neural network to reduce computational cost and storage resources. A novel tuning law for a single neural network is designed to learn an online solution of a tracking Hamilton–Jacobi–Isaacs (HJI) equation. The HJI solution is used to approximate an H∞ optimal tracking performance index function and an intelligent tracking control law in the case of the worst disturbance. The laws guarantee closed-loop stability in real time. The convergence and stability of the overall system are proved by Lyapunov techniques. The simulation results on a non-linear system and wheeled mobile robot verify the effectiveness of the proposed controller.

  • Reinforecement learning-based optimal tracking control for wheeled mobile robot
    Nguyen Tan Luy

    Proceedings - 2012 IEEE International Conference on Cyber Technology in Automation, Control, and Intelligent Systems, CYBER 2012, Pages: 371-376, Published: 2012 IEEE
    This paper proposes a new method to design a reinforcement learning-based integrated kinematic and dynamic tracking control scheme for a nonholonomic wheeled mobile robot. The scheme uses just only one neural network to design an online adaptive synchronous policy iteration algorithm implemented as an actor critic structure. Our tuning law for the single neural network not only learns online a tracking-HJB equation to approximate both the optimal cost and the optimal control law but also guarantees closed-loop stability in real-time. The convergence and stability of the overall system are proven by Lyapunov theory. The simulation results for wheeled mobile robot verify the effectiveness of the proposed controller.

RECENT SCHOLAR PUBLICATIONS

  • Neural Network Observers and Sensorless Robust Optimal Control for Partially Unknown PMSM with Disturbances and Saturating Voltages
    LN Tan, CT Pham, DC Pham
    IEEE Transactions on Power Electronics 36 (10), 12045 -12056 2021

  • Optimal Tracking Control for PMSM with Partially-Unknown Dynamics, Saturation Voltages, Torque and Voltage Disturbances
    LN Tan, CT Pham
    IEEE Transactions on Industrial Electronics 2021

  • Event-triggered distributed H∞ constrained control of physically interconnected large-scale partially unknown strict-feedback systems
    LN Tan
    IEEE Transactions on Systems, Man, and Cybernetics: Systems 51 (4), 2444 - 2456 2021

  • Reinforcement learning-based event-triggered robust optimal control for mobile euler-lagrange systems with dead-zone and saturation actuators
    LN Tan, HT Tran, TT Tran, CT Pham
    International Journal of Mechanical Engineering and Robotics Research 10 (3 2021

  • Distributed H∞ optimal tracking control for strict-feedback nonlinear large-scale systems with disturbances and saturating actuators
    LN Tan
    IEEE Transactions on Systems, Man, and Cybernetics: Systems 50 (10), 4719-4731 2020

  • Event-triggered distributed H∞ control of physically interconnected mobile Euler–Lagrange systems with slipping, skidding and dead zone
    LN Tan
    IET Control Theory & Applications 14 (3), 438-451 2020

  • DISTRIBUTED FORMATION CONTROL AND OBSTACLE AVOIDANCE OF MULTI-ROBOT SYSTEM
    N Ha, K Tran, L Nguyen
    Journal of Science and Technology-IUH 40 (04) 2019

  • Event-triggered robust optimal control algorithm for strict-feedback nonlinear systems with dead-zone and external disturbance
    LN Tan, DQ Minh, TH Toan
    Special issue on Measurement, Control and Automation. Journal of Automation 2019

  • Omnidirectional-vision-based distributed optimal tracking control for mobile multirobot systems with kinematic and dynamic disturbance rejection
    LN Tan
    IEEE Transactions on Industrial Electronics 65 (7), 5693-5703 2018

  • Machine learning based-distributed optimal control algorithm for multiple nonlinear agents with input constraints
    NT Luy, NT Dang, DQ Minh, TH Vinh
    2018 5th NAFOSTED Conference on Information and Computer Science (NICS), 276-281 2018

  • Distributed optimal control for nonholonomic systems with input constraints and uncertain interconnections
    LN Tan
    Nonlinear Dynamics 93 (2), 801-817 2018

  • Distributed cooperative H∞ optimal tracking control of MIMO nonlinear multi-agent systems in strict-feedback form via adaptive dynamic programming
    LN Tan
    International Journal of Control 91 (4), 952-968 2018

  • Distributed optimal integrated tracking control for separate kinematic and dynamic uncertain non-holonomic mobile mechanical multi-agent systems
    LN Tan
    IET Control Theory & Applications 11 (18), 3249-3260 2017

  • Robust adaptive dynamic programming based online tracking control algorithm for real wheeled mobile robot with omni-directional vision system
    LN Tan
    Transactions of the Institute of Measurement and Control 39 (6), 832-847 2017

  • Adaptive dynamic programming-based design of integrated neural network structure for cooperative control of multiple MIMO nonlinear systems
    LN Tan
    Neurocomputing 237, 12-24 2017

  • Special issue on control and automation in cyber-physical systems
    S Tung, Y Liu, U Wejinwa
    Transactions of the Institute of Measurement and Control 36 (7), 867-867 2014

  • Reinforcement learning-based intelligent tracking control for wheeled mobile robot
    LN Tan, NT Thanh, HM Tri
    Transactions of the Institute of Measurement and Control 36 (7), 868-877 2014

  • Reinforcement learning-based robust adaptive tracking control for multi-wheeled mobile robots synchronization with optimality
    LN Tan, NT Thanh, HM Tri
    2013 IEEE Workshop on Robotic Intelligence in Informationally Structured 2013

  • Reinforcement learning-based tracking control for wheeled mobile robot
    LN Tan
    2012 IEEE International Conference on Systems, Man, and Cybernetics (SMC 2012

  • Reinforecement learning-based optimal tracking control for wheeled mobile robot
    LN Tan
    2012 IEEE International Conference on Cyber Technology in Automation 2012

MOST CITED SCHOLAR PUBLICATIONS

  • Reinforcement learning-based intelligent tracking control for wheeled mobile robot
    LN Tan, NT Thanh, HM Tri
    Transactions of the Institute of Measurement and Control 36 (7), 868-877 2014
    Citations: 24

  • Omnidirectional-vision-based distributed optimal tracking control for mobile multirobot systems with kinematic and dynamic disturbance rejection
    LN Tan
    IEEE Transactions on Industrial Electronics 65 (7), 5693-5703 2018
    Citations: 23

  • Adaptive dynamic programming-based design of integrated neural network structure for cooperative control of multiple MIMO nonlinear systems
    LN Tan
    Neurocomputing 237, 12-24 2017
    Citations: 23

  • Distributed cooperative H∞ optimal tracking control of MIMO nonlinear multi-agent systems in strict-feedback form via adaptive dynamic programming
    LN Tan
    International Journal of Control 91 (4), 952-968 2018
    Citations: 20

  • Distributed H∞ optimal tracking control for strict-feedback nonlinear large-scale systems with disturbances and saturating actuators
    LN Tan
    IEEE Transactions on Systems, Man, and Cybernetics: Systems 50 (10), 4719-4731 2020
    Citations: 19

  • Robust adaptive dynamic programming based online tracking control algorithm for real wheeled mobile robot with omni-directional vision system
    LN Tan
    Transactions of the Institute of Measurement and Control 39 (6), 832-847 2017
    Citations: 18

  • Event-triggered distributed H∞ constrained control of physically interconnected large-scale partially unknown strict-feedback systems
    LN Tan
    IEEE Transactions on Systems, Man, and Cybernetics: Systems 51 (4), 2444 - 2456 2021
    Citations: 13

  • Distributed optimal integrated tracking control for separate kinematic and dynamic uncertain non-holonomic mobile mechanical multi-agent systems
    LN Tan
    IET Control Theory & Applications 11 (18), 3249-3260 2017
    Citations: 11

  • Reinforecement learning-based optimal tracking control for wheeled mobile robot
    LN Tan
    2012 IEEE International Conference on Cyber Technology in Automation 2012
    Citations: 8

  • Event-triggered distributed H∞ control of physically interconnected mobile Euler–Lagrange systems with slipping, skidding and dead zone
    LN Tan
    IET Control Theory & Applications 14 (3), 438-451 2020
    Citations: 5

  • Distributed optimal control for nonholonomic systems with input constraints and uncertain interconnections
    LN Tan
    Nonlinear Dynamics 93 (2), 801-817 2018
    Citations: 5

  • Reinforcement learning-based robust adaptive tracking control for multi-wheeled mobile robots synchronization with optimality
    LN Tan, NT Thanh, HM Tri
    2013 IEEE Workshop on Robotic Intelligence in Informationally Structured 2013
    Citations: 5

  • Special issue on control and automation in cyber-physical systems
    S Tung, Y Liu, U Wejinwa
    Transactions of the Institute of Measurement and Control 36 (7), 867-867 2014
    Citations: 4

  • Robust reinforcement learning-based tracking control for wheeled mobile robot
    NT Luy, ND Thanh, NT Thanh, NTP Ha
    2010 The 2nd International Conference on Computer and Automation Engineering 2010
    Citations: 3

  • Facial Expression Recognition Using AAM Algorithm
    TN Duc, TN Huu, LN Tan
    Vietnam: Ho Chi Minh University of Technology 2009
    Citations: 3

  • Reinforcement learning-based tracking control for wheeled mobile robot
    LN Tan
    2012 IEEE International Conference on Systems, Man, and Cybernetics (SMC 2012
    Citations: 2

  • Robust adaptive control using reinforcement learning for nonlinear system with input constraints
    LT Nguyen, TT Nguyen, HTP Nguyen
    Science and Technology Development Journal 12 (16), 5-18 2009
    Citations: 2

  • Reinforcement learning-based event-triggered robust optimal control for mobile euler-lagrange systems with dead-zone and saturation actuators
    LN Tan, HT Tran, TT Tran, CT Pham
    International Journal of Mechanical Engineering and Robotics Research 10 (3 2021
    Citations: 1

Publications

Luy Nguyen Tan, Adaptive dynamic programming-based design of integrated neural network structure for cooperative control of multiple MIMO nonlinear systems, Neurocomputing, vol. 237, pp. 12-24, 2017