Luy Tan Nguyen

@hcmut.edu.vn

Ho Chi Minh City University of Technology



                 

https://researchid.co/i-1326-2019

RESEARCH INTERESTS

Distributed control; Adaptive dynamic programming; Robot control; Event-triggered control

33

Scopus Publications

Scopus Publications



  • Event-triggered distributed H<inf>∞</inf> secure control for nonholonomic agents with dead-zone inputs under attacks on sensors and actuators
    Luy Nguyen Tan
    Wiley
    SummaryDistributed optimal control has been extensively investigated for nonholonomic mechanical agents (NMA). However, the existing methods have not yet taken into account vulnerabilities caused by malicious attacks on sensors and dead‐zone actuator links. The aim of this paper is therefore to propose a novel control scheme that not only achieves bounded ‐gain consensus performance and rejects dead‐zone disturbance but also mitigates the burden of communication resources and the effects of sensor and actuator attacks. Firstly, to estimate unmeasurable states and the unknown model of the attacks, event‐triggered (ET) observers are designed. Secondly, ET‐augmented control is proposed to transform Euler‐Lagrange dynamics into consensus tracking dynamics, from which the ET‐robust optimal control problem is formulated. Thirdly, the ET‐distributed secure control strategies are approximated synchronously via adaptive dynamic programming (ADP) and multi‐player differential game theory. Finally, a numerical example illustrates, on a networked multi‐robot system, the effectiveness of the proposed method.

  • ADP-Based Decentralized Adaptive Optimal Control for Two Cooperating Robots
    Do Hoang Thai Duong, Quoc Phan Nguyen Phuc, Giap Nguyen Hoang, Dung Nguyen Le, and Luy Nguyen Tan
    Springer Nature Switzerland

  • ADP-Based &lt;italic&gt;H&lt;/italic&gt;&lt;sub&gt;&amp;#x221E;&lt;/sub&gt; Optimal Control of Robot Manipulators with Asymmetric Input Constraints and Disturbances
    Dien Nguyen Duc, Lai Lai Khac, and Luy Nguyen Tan
    Institute of Electrical and Electronics Engineers (IEEE)
    Trajectory tracking control for robot manipulators is an attractive topic in the research community. This is a challenging problem because robot manipulators are complex nonlinear systems. Furthermore, the tracking control performance for robot manipulators is greatly affected by input constraints and external disturbances. This paper proposes a novel $ H_{\\infty } $ optimal controller for robot manipulators with asymmetric input constraints and external disturbances based on adaptive dynamic programming (ADP). Firstly, a strict feedback nonlinear system is used to represent the robot manipulator dynamics, and then a feedforward controller is designed to construct the tracking error dynamics. Secondly, a value function is introduced, and the Hamilton-Jacobi-Isaacs equation is made and approximated online by the principle of adaptive dynamic programming. Thirdly, the optimal control law and disturbance compensation law are determined. The stability and convergence of the proposed algorithm are analyzed by the Lyapunov technique. Finally, the controller performance is verified through simulation and experimental results with STM32F407 of STMicroelectronics.

  • Event-triggered Robust Optimal Control for PMSM with Unknown Internal Dynamics, Disturbances, and Constrained Inputs
    Luy Nguyen Tan, Thanh Pham Cong, and Duy Pham Cong
    Institute of Electrical and Electronics Engineers (IEEE)
    In industry, for driving a permanent magnet synchronous motor (PMSM), it is more favorable to optimize control performances and reduce computational complexity and communication waste from a controller to actuators. For that reason, this paper employs an event-triggering mechanism to design a robust optimal control strategy for PMSM. Firstly, the PMSM model is presented as a strict-feedback nonlinear system with unknown internal dynamics, disturbances, and constrained inputs. Then, an event-triggered (ET) feedforward control strategy is introduced to convert the separated speed and current dynamics into an augmented system. Secondly, an ET-robust optimal feedback control strategy and an ET disturbance compensation strategy are designed using adaptive dynamic programming (ADP) and zero-sum game theory. All controller parameters are tuned online without identifying unknown dynamics or using a persistent excitation condition. It is shown that system stability and the exclusion of Zeno’s behavior are fulfilled. Finally, compared with the existing time-triggering control strategies in simulation and experiments with TMS320F28335 of Texas Instruments, the proposed strategy is more effective in reducing the burden of computation bandwidth and communication load.

  • Adaptive Dynamic Programming and Zero-Sum Game-Based Distributed Control for Energy Management Systems With Internet of Things
    Luy Nguyen Tan, Nishu Gupta, and Mohammad Derawi
    Institute of Electrical and Electronics Engineers (IEEE)


  • Event-Triggered ℋ<inf>∞</inf>Optimal Control for Euler-Lagrange Dynamics with Dead Zone Inputs under Attacks on Sensors and Actuators
    Luy Nguyen Tan, Dung Nguyen Le, Quoc Phan Nguyen Phuc, Lam Phan Huynh, and Thanh Pham Cong
    IEEE
    Optimal control has been investigated for Euler-Lagrange (EL) systems. However, the existing methods have not yet considered the input constraints due to dead zones and vulnerabilities caused by malicious attackers. In this paper, we propose a novel control algorithm to gain the bounded ℒ2-gain tracking performance with deadzone disturbance rejection and to reduce not only the effects of sensor and actuator attacks but also the burden of communication load. Firstly, an event-triggered (ET) observer is studied to conjecture the unmeasurable states and compensate for the adverse attack effects. Secondly, ET augmented control is proposed to transform Euler-Lagrange dynamics into tracking error dynamics. Thirdly, an ET ${{\\mathcal{H}}_\\infty }$ optimal control law is approximated via adaptive dynamic programming (ADP). Finally, the simulation of mobile nonholonomic robots illustrates the effectiveness of the proposed method.

  • &lt;italic&gt;H&lt;/italic&gt;&lt;sub&gt;&amp;#x221E;&lt;/sub&gt; Control for Oscillator Systems with Event-Triggering Signal Transmission of Internet of Things
    Luy Nguyen Tan, Nishu Gupta, and Mohammad Derawi
    Institute of Electrical and Electronics Engineers (IEEE)
    This article proposes to design a distributed <inline-formula> <tex-math notation="LaTeX">$H_{\\infty} $ </tex-math></inline-formula> optimal control algorithm for Van der Pol oscillators with unknown internal dynamics, input constraints and external disturbances, via event-triggering signal transmission of the Internet of Things (IoT). First, the graph theory for the IoT is introduced. Second, the dynamics of Van der Pol oscillators are transformed into the tracking dynamics which cooperate via the IoT network. Third, unlike the existing online optimal control algorithms using adaptive dynamic programming, we design an <inline-formula> <tex-math notation="LaTeX">$H_{\\infty} $ </tex-math></inline-formula> optimal control algorithm employing an event-triggering signal transmission mechanism to reduce the burden of communication resource and computation bandwidth of the IoT network. As the triggering condition and approximation parameter update policies are appropriately designed, the algorithm guarantees that the Zeno phenomenon is free, the consensus errors are uniformly ultimately bounded, and the external disturbance is compensated. Finally, numerical simulation results with comparison to the time-triggering algorithms confirm the effectiveness of the proposed algorithm.

  • H<inf>∞</inf> Optimal Tracking Control for Robot Manipulators with Input Constraint and Disturbances
    N. D. Dien, N. T. Luy, and L. K. Lai
    Springer International Publishing


  • LiDAR-Based Online Navigation Algorithm For An Autonomous Agricultural Robot


  • Optimal Tracking Control for PMSM with Partially Unknown Dynamics, Saturation Voltages, Torque, and Voltage Disturbances
    Luy Nguyen and Thanh Cong Pham
    Institute of Electrical and Electronics Engineers (IEEE)
    This article proposes a novel optimal tracking control scheme for permanent magnet synchronous motors (PMSMs) with partially unknown dynamics, saturation voltages, and disturbances in both speed and current dynamics. The strict-feedback nonlinear system is employed to present the PMSM model. Augmented feedforward control inputs are proposed to transform a speed and current tracking control problem of conventional cascade structures to an optimal control problem of a new structure. Consequently, the saturated adaptive optimal control law is designed for the problem. The optimal solution of Hamilton–Jacobi–Issac equation, which provides the value to the control law, is approximated by a simple online approximator. An integral reinforcement learning technique is used to tune the approximator without observing unknown dynamics. It is proven that the optimal value function, the control law, and the worst disturbance law converge to the near-optimal values. The simulation and experiment on a PMSM prototype model of a load drive application with a digital signal processing board TMS320F28379D of Texas Instrument are conducted to justify the effectiveness of the proposed scheme.

  • Q-Learning Algorithm and CMAC Approximation Based Robust Optimal Control for Renewable Energy Management Systems


  • Model-Learning-Based Partitioned Control of a Human-Powered Augmentation Lower Exoskeleton
    Huu-Toan Tran, Luy Nguyen Tan, and Seung-Hun Han
    Springer Science and Business Media LLC

  • Neural Network Observers and Sensorless Robust Optimal Control for Partially Unknown PMSM with Disturbances and Saturating Voltages
    Luy Nguyen Tan, Thanh Pham Cong, and Duy Pham Cong
    Institute of Electrical and Electronics Engineers (IEEE)
    This article proposes neural network (NN) based observer schemes and a sensorless robust optimal control scheme for partially unknown permanent magnet synchronous motors with disturbances and saturating voltages. First, an NN-observer scheme is designed to estimate back-electromotive force (EMF), for which the mathematical model in rotary or stationary reference frames is relaxed. The NN weight tuning law is designed via Lyapunov theory to guarantee that EMF is ultimately uniformly bounded. Second, to compensate the inexact extraction of the estimated back-EMF at any speed conditions, disturbances, and NN approximation errors, another NN-observer scheme is designed to estimate the tracking errors of rotor position and speed, for which low-pass filters and/or phase-locked loops are not needed. Third, a sensorless saturated robust optimal control scheme dealing with general disturbances and saturating voltages is designed. The scheme includes the augmented feedforward controller to transform the speed and current tracking problem into an optimal control problem. Finally, the feedback control law and worst disturbance law are obtained without estimating unknown internal dynamics. The effectiveness of the proposed schemes is tested through simulations and comparative experiments on a load drive application with a DSP board TMS320F28379D.

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

  • Reinforcement Learning-Based Event-Triggered Robust Optimal Control for Mobile Euler-Lagrange Systems with Dead-Zone and Saturation Actuators
    Tan-Luy Nguyen, Huu-Toan Tran, Trong-Toan Tran, and Cong-Thanh Pham
    EJournal Publishing
    This paper proposes a reinforcement learning (RL)-based event-triggered robust optimal control method for mobile Euler-Lagrange systems with both dead-zone and saturation from actuators. Firstly, kinematics and dynamics of the system are integrated into the equivalent system, where both of the dead-zone and saturation inputs are treated. Secondly, event-triggered robust optimal control and dead-zone disturbance laws are designed, where their parameters are only updated when a triggering condition occurs. Via RL techniques, the new triggering condition is introduced. The method not only guarantees the stability of the closed system and the convergence of the cost function to the bounded 2 -gain optimal value but also relaxes identification procedures for unknown nonlinear functions. Additionally, it maintains the minimum inter-event time between two sequent triggering instants greater than zero, thus the Zeno’s behavior is avoided. Finally, the simulation of a nonholonomic wheeled mobile robot system with deadzone and saturated torques is implemented to verify the effectiveness of the proposed method. 

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

  • Machine learning based-distributed optimal control algorithm for multiple nonlinear agents with input constraints
    Nguyen Tan Luy, Nguyen Thanh Dang, Dang Quang Minh, and Tran Hong Vinh
    IEEE
    This paper utilizes the machine learning theory to propose an algorithm for solving the distributed optimal control of multiple nonlinear agents with saturating actuators. Unlike the existing algorithm based on an critic/actor/disturber framework with three neural networks (NNs) to approximate Hamilton-Jacobi-Isaac solution for each nonlinear agent, the algorithm in the paper is proposed with only one NN. It is shown that when the algorithm is executed online, the NN weight approximation errors and states are uniformly ultimately bounded (UUB) as well as the NN weights and optimal control policies are guaranteed to be converged to the approximately optimal values concurrently, and nonquadratic cost functions with constrained-inputs are minimized. To show the effectiveness of the proposed algorithm, simulations for multiple controlled Van der Pol oscillators are carried out and compared.

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

RECENT SCHOLAR PUBLICATIONS

    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