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International Joint Conference on Neural Networks

作者： Xiangnan Zhong Zhen Ni Haibo He Department of Electrical Computer and Biomedical Engineering University of Rhode Island Kingston RI USA

ISBN: (纸本)9781479919611

In this paper, we propose a novel event-triggered adaptive dynamic programming (ADP) method using only the input-output data. Event-triggered method is widely used for its computational efficiency capacity. Comparing with the traditional method which updates the controller periodically, the event-triggered method only updates the controller when it is necessary and therefore the computation is reduced. Generally, the triggered condition is based on the system current and sampled states. In this paper, we consider a neural-network-based observer to recover the system dynamics using the measured input-output data. The triggered instants are calculated according to the recovered state. Stability analysis of the proposed approach is presented. We verify our proposed method through a robot-arm example.

关键词： Event-Triggered Adaptive Dynamic Programming continuous-time nonlinear system Measured Input-Output Data

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Design of a Stable Discrete-time Output-feedback Decentralized Controller for Uncertain continuous-time Large-scale nonlinear systems

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INTERNATIONAL JOURNAL OF CONTROL AUTOMATION AND systemS 2025年第5期23卷 1402-1410页

作者： Park, Jang-Hyun Kim, Seong-Hwan Mokpo Natl Univ Dept Elect Engn 1666 Yeongsan Ro Muan Gun 58554 Jeollanam Do South Korea

This paper addresses a novel decentralized digital control strategy that ensures the Lyapunov stability of interconnected large-scale systems encompassing a broad class of uncertain, nonautonomous nonlinear subsystems. Addressing the challenges posed by interconnected uncertainties and nonlinearities, the proposed approach adapts and simplifies a sampled-output feedback controller, building upon the foundation laid in our previous work. The key innovation lies in the integration of a discrete-time higher-order sliding-mode differentiator, which relies solely on sampled data from continuous-time signals. This enables practical implementation in digital control systems. Rigorous Lyapunov stability analysis is provided to guarantee the stability of the overall closed-loop system. The effectiveness and performance of the proposed decentralized digital controller are validated through numerical simulations, demonstrating its potential for real-world applications in complex interconnected systems.

关键词： continuous-time nonlinear system digital controller Lyapunov stability sampled-output feedback

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Robust Controller Design of continuous-time nonlinear system Using Neural Network

Robust Controller Design of Continuous-time Nonlinear System...

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International Joint Conference on Neural Networks

作者： Xiangnan Zhong Haibo He Danil V. Prokhorov Department of Electrical Computer and Biomedical Engineering University of Rhode Island Kingston RI 02881 USA. Toyota Research Institute NA TTC Ann Arbor MI 48105 USA.

ISBN: (纸本)9781467361279

In this paper, we propose an optimal control method based on the solution of Hamilton-Jacobi-Bellman (HJB) equation for the continuous-time nonlinear system with bounded unknown perturbation. The robust control system is converted into the corresponding optimal control system with appropriate performance index and the equivalence of the transformation is proved, i.e., the solution of the optimal control problem can globally asymptotically stabilize the robust control system. Adaptive dynamic programming (ADP) based approach is presented to iteratively approximate the optimal performance index and obtain the optimal control policy. A neural network with adaptive weights is applied to implement this approach. An example is given to illustrate the proposed method.

关键词： HJB continuous-time nonlinear system appropriate performance index performance indicators robust controller design Neural network nonlinear systems Optimal control continuous time systems Robust control Optimal control systems controller design Solutions control systems

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Two-loop reinforcement learning algorithm for finite-horizon optimal control of continuous-time affine nonlinear systems

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INTERNATIONAL JOURNAL OF ROBUST AND nonlinear CONTROL 2022年第1期32卷 393-420页

作者： Chen, Zhe Xue, Wenqian Li, Ning Lewis, Frank L. Shanghai Jiao Tong Univ Dept Automat Shanghai 200240 Peoples R China Minist Educ China Key Lab Syst Control & Informat Proc Shanghai Peoples R China Shanghai Engn Res Ctr Intelligent Control & Manag Shanghai Peoples R China Northeastern Univ State Key Lab Synthet Automat Proc Ind Shenyang Peoples R China Northeastern Univ Int Joint Res Lab Integrated Automat Shenyang Peoples R China Univ Texas Arlington UTA Res Inst Arlington TX 76019 USA

This article proposes three novel time-varying policy iteration algorithms for finite-horizon optimal control problem of continuous-time affine nonlinear systems. We first propose a model-based time-varying policy iteration algorithm. The method considers time-varying solutions to the Hamiltonian-Jacobi-Bellman equation for finite-horizon optimal control. Based on this algorithm, value function approximation is applied to the Bellman equation by establishing neural networks with time-varying weights. A novel update law for time-varying weights is put forward based on the idea of iterative learning control, which obtains optimal solutions more efficiently compared to previous works. Considering that system models may be unknown in real applications, we propose a partially model-free time-varying policy iteration algorithm that applies integral reinforcement learning to acquiring the time-varying value function. Moreover, analysis of convergence, stability, and optimality is provided for every algorithm. Finally, simulations for different cases are given to verify the convenience and effectiveness of the proposed algorithms.

关键词： continuous-time nonlinear system finite-horizon optimal control iterative learning control policy iteration reinforcement learning value function approximation

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Integral-Type Event-Triggered Model Predictive Control of nonlinear systems With Additive Disturbance

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IEEE TRANSACTIONS ON CYBERNETICS 2021年第12期51卷 5921-5929页

作者： Sun, Qi Chen, Jicheng Shi, Yang Univ Victoria Dept Mech Engn Victoria BC V8W 2Y2 Canada

This article studies integral-type event-triggered model predictive control (MPC) of continuous-time nonlinear systems. An integral-type event-triggered mechanism is proposed by incorporating the integral of errors between the actual and predicted state sequences, leading to reduced average sampling frequency. Besides, a new and improved robustness constraint is introduced to handle the additive disturbance, rendering the MPC problem with a potentially enlarged initial feasible region. Furthermore, the feasibility of the designed MPC and the stability of the closed-loop system are rigorously investigated. Several sufficient conditions to guarantee these properties are established, which is related to factors, such as the prediction horizon, the disturbance bound, the triggering level, and the contraction rate for the robustness constraint. The effectiveness of the proposed algorithm is illustrated by numerical examples and comparisons.

关键词： nonlinear systems Optimization Additives Robustness Predictive control Closed loop systems Prediction algorithms continuous-time nonlinear system integral-type event-triggered mechanism (ETM) nonlinear model predictive control (MPC) robust control

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Robust Optimal Control Scheme for Unknown Constrained-Input nonlinear systems via a Plug-n-Play Event-Sampled Critic-Only Algorithm

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IEEE TRANSACTIONS ON systemS MAN CYBERNETICS-systemS 2020年第9期50卷 3169-3180页

作者： Zhang, Huaguang Zhang, Kun Xiao, Geyang Jiang, He Northeastern Univ State Key Lab Synthet Automat Proc Ind Shenyang 110004 Peoples R China Northeastern Univ Sch Informat Sci & Engn Shenyang 110004 Peoples R China

In this paper, a novel event-sampled robust optimal controller is proposed for a class of continuous-time constrained-input nonlinear systems with unknown dynamics. In order to solve the robust optimal control problem, an online data-driven identifier is established to construct the system dynamics, and an event-sampled critic-only adaptive dynamic programming method is developed to replace the conventional time-driven actor-critic structure. The designed online identification method runs during the solving process and is not applied as a priori part for the solutions, which simplifies the architecture and reduces computational load. The proposed robust optimal control algorithm tunes the parameters of critic-only neural network (NN) by event-triggering condition and runs in a plug-n-play framework without system functions, where fewer transmissions and less computation are required as all the measurements received simultaneously. Based on the novel design, the stability of system and the convergence of critic NN are demonstrated by Lyapunov theory, where the state is asymptotically stable and weight error is guaranteed to be uniformly ultimately bounded. Finally, the applications in a basic nonlinear system and the complex rotational-translational actuator problem demonstrate the effectiveness of the proposed method.

关键词： Optimal control Artificial neural networks Heuristic algorithms Approximation algorithms Robust control nonlinear dynamical systems Adaptive dynamic programming (ADP) continuous-time nonlinear system input constraint neural network (NN) optimal control robust control

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When sex matters: a complete model of X-linked diseases

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INTERNATIONAL JOURNAL OF GENERAL systemS 2018年第6期47卷 549-568页

作者： Del Vecchio, C. Verrilli, F. Glielmo, L. Univ Sannio Dept Engn Benevento Italy

X-linked recessive diseases are genetic disorders caused by gene's abnormalities placed on the X chromosome. Due to differences between males and females in sex chromosomes, the transmission mechanisms of these diseases vary in the two sexes. Other than the results of the well-known patterns of inheritance, the current spread of genetic disorders is influenced by spontaneous genetic mutations and individual's reduced reproduction capacities (fertility) conditioned by the disease severity. We developed a structured, continuous-time mathematical model describing how the disease spreads along time;the model accounts for a different fertility of affected individuals and for spontaneous mutations. Through Lyapunov analysis, we gained insights into system's asymptotic behaviour, that is how individual's fitness or spontaneous genetic mutations affect disease's diffusion. To the best of our knowledge, our model is the first one specifically designed to describe X-linked recessive diseases diffusion.

关键词： continuous-time nonlinear system equilibrium and stability epidemiology genetic diseases

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Unified Approach to the Problem of Fault Diagnosis

Unified Approach to the Problem of Fault Diagnosis

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Conference on Control and Fault-Tolerant systems (SysTol)

作者： Shumsky, Alexey Ye. Zhirabok, Alexey N. Inst Marine Technol Problems Vladivostok Russia

ISBN: (纸本)9781424481545

A unified approach to the problem of fault diagnosis is considered for the cases of continuous-time and discrete-time nonlinear and linear system models as well as for discrete-event model. The feature of this approach is that it allows obtaining the unique on the algorithmic level solution of the problem for all above types of the models. Conventional differential geometric and pair algebra of partitions tools are applied as auxiliary ones for providing the calculations in special model cases.

关键词： continuous time systems continuous-time nonlinear system differential algebraic equations differential geometric algebra differential geometry discrete event systems discrete time systems discrete-event model discrete-time nonlinear system fault diagnosis fault diagnosis linear system model linear systems nonlinear systems pair algebra

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Estimation of Rotary Inverted Pendulum by using the Unscented Kalman Filter - Estimation of the initial state

Estimation of Rotary Inverted Pendulum by using the Unscente...

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Annual Conference on the Society-of-Instrument-and-Control-Engineers

作者： Zheng, Min Ikeda, Kenji Shimomura, Takao Univ Tokushima Grad Sch Adv Technol & Sci Tokushima 770 Japan

ISBN: (纸本)9784907764289

This paper proposes a continuous-time model estimation method by using the Unscented Kalman Filter (UKF) from the sampled I/O data, in which the initial state as well as the plant parameter are estimated. The initial state is estimated based on a backward system of the plant. The effectiveness is demonstrated by using the Rotary Inverted Pendulum (RIP) to estimate the initial state and the parameter of a continuous-time nonlinear system.

关键词： continuous-time nonlinear system Unscented Kalman Filter backward system Rotary Inverted Pendulum parameter estimation

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Parameter estimation of rotary inverted pendulum based on unscented kalman filter

Parameter estimation of rotary inverted pendulum based on un...

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SICE-ICASE International Joint Conference

作者： Zheng, Min Ikeda, Kenji Shimomura, Takao Univ Tokushima Dept Informat Sci & Intelligent Syst Tokushima 770 Japan

ISBN: (纸本)9788995003848

The Unscented Kalman Filter (UKF) is a nonlinear estimation method, and has been widely applied in nonlinear control applications. However, if covariance of the initial value is unknown, the estimated value does not converge to the true value in general. To improve the result of estimation, the iterated UKF is proposed as a method to repeat the estimation by redefining the initial value of the filter. Its effectiveness is demonstrated by using the Rotary Inverted Pendulum (RIP) to estimate the parameters of a continuous-time nonlinear system.

关键词： continuous-time nonlinear system unscented kalman filter rotary inverted pendulum estimation

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