Luy Tan Nguyen
@hcmut.edu.vn
Ho Chi Minh City University of Technology
RESEARCH INTERESTS
Distributed control; Adaptive dynamic programming; Robot control; Event-triggered control
Scopus Publications
Scopus Publications
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.
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.
Luy Nguyen Tan, Nishu Gupta, and Mohammad Derawi
Institute of Electrical and Electronics Engineers (IEEE)
Nguyen Le-Dung, Phan Huynh-Lam, Nguyen Hoang-Giap, and Nguyen Tan-Luy
Elsevier BV
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.
Luy Nguyen Tan and Duc Lam Gia
Institute of Electrical and Electronics Engineers (IEEE)
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.
N. D. Dien, N. T. Luy, and L. K. Lai
Springer International Publishing
Luy Nguyen Tan, Huu-Toan Tran, and Trong-Toan Tran
Institute of Electrical and Electronics Engineers (IEEE)
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.
Huu-Toan Tran, Luy Nguyen Tan, and Seung-Hun Han
Springer Science and Business Media LLC
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.
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.
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.
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.
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.
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.
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.
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.
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.
Luy Nguyen Tan
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.
Nguyen Tan Luy
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.
Tan Luy Nguyen
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.
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