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Event-triggered optimal tracking control for strict-feedback nonlinear systems with non-affine nonlinear faults

nonlinear DYNAMICS 2024年第17期112卷 15413-15426页

作者： Wang, Ling Wang, Xin Wang, Ziming Southwest Univ Coll Elect & Informat Engn Chongqing Key Lab Nonlinear Circuits & Intelligent Chongqing 400044 Peoples R China Hong Kong Univ Sci & Technol Guangzhou Guangzhou 511458 Peoples R China

This article studies the control ideas of the optimal backstepping technique, proposing an event-triggered optimal tracking control scheme for a class of strict-feedback nonlinear systems with non-affine and nonlinear faults. A simplified identifier-critic-actor framework is employed in the reinforcement learning algorithm to achieve optimal control. The identifier estimates the unknown dynamic functions, the critic evaluates the system performance, and the actor implements control actions, enabling modeling and control of anonymous systems for achieving optimal control performance. In this paper, a simplified reinforcement learning algorithm is designed by deriving update rules from the negative gradient of a simple positive function related to the Hamilton-Jacobi-Bellman equation, and it also releases the stringent persistent excitation condition. Then, a fault-tolerant control method is developed by applying filtered signals for controller design. Additionally, to address communication resource reduction, an event-triggered mechanism is employed for designing the actual controller. Finally, the proposed scheme's feasibility is validated through theoretical analysis and simulation.

关键词： Identifier-critic-actor architecture Optimized backstepping Fault-tolerant control Event-triggered control Strict-feedback systems

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Neural Network-Based Event-Triggered Adaptive Control Algorithms for Uncertain nonlinear Systems with Actuator Failures

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COGNITIVE COMPUTATION 2020年第6期12卷 1370-1380页

作者： Tan, Lihua Li, Chuandong Huang, Junjian Southwest Univ Coll Elect & Informat Engn Chongqing Key Lab Nonlinear Circuit & Intelligent Chongqing 400715 Peoples R China

The adaptive control for strict-feedback nonlinear systems has drawn a lot of attention in various communities. Since neural network is a useful universal-approximator to approximate unknown plant model, the neural network-based adaptive control for nonlinear systems has attracted substantial interest over decades. Furthermore, to reduce the controller updating and save the control resource, the event-triggered mechanism has been widely applied. In this paper, the RBF neural network is applied to construct the state and composite disturbance observers and the back-stepping and Lyapunov-like method are applied to design the event-triggered adaptive controller. The theoretical framework of adaptive fault-tolerant control issue for strict-feedback nonlinear system that suffer from both unknown mismatched disturbance and actuator failures is formulated. This paper comes up with a novel event-triggered control strategy to guarantee that the tracking issue is resolved with better desired performance. In this study, a unified theoretical mechanism is developed to tackle the case where some factors consisting of unknown state variables, unknown mismatched disturbance, and actuator failures as well as event-triggered effects are merged together. We expect to extend the proposed method for the self-triggered case.

关键词： Neural network Event-triggered Fault-tolerant control Observer

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Diffusion least logarithmic absolute difference algorithm for distributed estimation

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SIGNAL PROCESSING 2018年 142卷 423-430页

作者： Chen, Feng Shi, Tao Duan, Shukai Wang, Lidan Wu, Jiagui Southwest Univ Coll Elect & Informat Engn Chongqing 400715 Peoples R China Southwest Univ Sch Math & Stat Chongqing 400715 Peoples R China Key Lab Nonlinear Circuits & Intelligent Informat Chongqing 400715 Peoples R China

The popular distributed estimation algorithms based on the mean-square error criterion is not robust against impulsive noise in the adaptive networks. To address the problem, the diffusion least logarithmic absolute difference (LLAD) algorithm is proposed in this article, which adopts both the logarithm operation and sign operation to the error. The algorithm can elegantly and gradually adjust the conventional cost functions in its optimization based on the error variation. Compared with centralized LLAD algorithm, the diffusion LLAD algorithm performs a good balance between communications and performance. The theoretical stability of mean and mean-square performance of the algorithm is analyzed. Simulation results indicate that the algorithm achieves a better performance, compared with diffusion LMS and diffusion sign-error LMS algorithms, even in the impulsive noise environment. (C) 2017 Elsevier B.V. All rights reserved.

关键词： Impulsive interference Distributed estimation Least logarithmic absolute difference

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Anti-control of periodic firing in HR model in the aspects of position, amplitude and frequency

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COGNITIVE NEURODYNAMICS 2021年第3期15卷 533-545页

作者： Dong, Tao Zhu, Huiyun Southwest Univ Coll Elect & Informat Engn Chongqing Key Lab Nonlinear Circuits & Intelligen Chongqing 400715 Peoples R China

This paper proposes a novel controller to control position, amplitude and frequency of periodic firing activity in Hindmarsh-Rose model based on Hopf bifurcation theory which is composed of linear control gain and nonlinear control gain. First, we select the activation of the fast ion channel as control parameter. Based on explicit criterion of Hopf bifurcation, a series of conditions are obtained to derive the linear gains of controller responsible for control of the location where the periodic firing activity occurs. Then, based on the control parameter, a series of conditions are obtained to derive the nonlinear gains of controller responsible for controlling the amplitude and frequency of periodic firing activity by using center manifold and normal form. Finally, the numerical experiments show that our controller can make the periodic firing activity occur at designed value and control the amplitude and frequency of periodic firing activity by adjusting nonlinear control gain of controller.

关键词： HR model Periodic firing Anti-control Hopf bifurcation

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Robust least mean logarithmic square adaptive filtering algorithms

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JOURNAL OF THE FRANKLIN INSTITUTE 2019年第1期356卷 654-674页

作者： Xiong, Kui Wang, Shiyuan Southwest Univ Coll Elect & Informat Engn Chongqing Peoples R China Chongqing Key Lab Nonlinear Circuits & Intelligen Chongqing Peoples R China

The conventional logarithm cost-based adaptive filters, e.g., the least mean logarithmic square (LMLS) algorithm, cannot combat impulsive noises at the filtering process. To address this issue, we present a novel robust least mean logarithmic square (RLMLS) algorithm by using a generalized logarithmic cost function. The proposed RLMLS algorithm can provide robustness against impulsive noises with the improvement of filtering accuracy. For theoretical analysis, the mean square analysis of RLMLS is provided in terms of the mean square deviation (MSD) and excess mean-square error (EMSE) with a white Gaussian reference. For further performance improvement in different noises, the variable step-size RLMLS (VSSRLMLS) based on the statistics of error is proposed to improve the convergence rate and steady-state mean square error, simultaneously. Analytical results and superiorities of RLMLS and VSSRLMLS in the context of system identification are supported by simulations from the aspects of filtering accuracy and robustness in Gaussian and impulse noises. (C) 2018 The Franklin Institute. Published by Elsevier Ltd. All rights reserved.

关键词： Impulse noise

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Reinforcement Learning-Based Event-Triggered Constrained Containment Control for Perturbed Multiagent Systems

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IEEE TRANSACTIONS ON SIGNAL AND informatION PROCESSING OVER NETWORKS 2024年 10卷 820-832页

作者： Tang, Daocheng Pang, Ning Wang, Xin Southwest Univ Coll Westa Chongqing Key Lab Nonlinear Circuitsand Intelligen Chongqing 400715 Peoples R China Shanghai Jiao Tong Univ Dept Automat Shanghai 200240 Peoples R China Southwest Univ Coll Elect & Informat Engn Chongqing Key Lab Nonlinear Circuits & Intelligent Chongqing 400715 Peoples R China

This article investigates the full-state-constrained optimal containment control problem of perturbed nonlinear multiagent systems (MASs). Initially, to balance control accuracy and cost while maintaining the states of MASs within confined regions, an enhanced constrained optimized backstepping (OB) framework is first developed for the multiagent control scenario by adopting an identifier-actor-critic-based reinforcement learning (RL) algorithm, where a novel performance index based on the barrier Lyapunov function (BLF) is integrated into the classic OB framework. Then, to enhance the robustness of the systems, the proposed framework employs disturbance observers to mitigate the effects of unknown external disturbances. Moreover, sufficient conditions are established to ensure that systems maintain stability and expected performance under denial-of-service (DoS) attacks. Subsequently, the controller implements a novel dynamic event-triggered mechanism (DETM), adaptively adjusting the triggering conditions by the estimated neural network (NN) weights in the proposed framework for substantial communication burden reduction. Finally, the stability of the systems is demonstrated using the Lyapunov theory, and a simulation example confirms the feasibility of the proposed scheme.

关键词： Constrained optimized backstepping (OB) containment control denial-of-service (DoS) attacks. dynamic event-triggered mechanism (DETM) reinforcement learning (RL)

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Long-term and short-term memory networks based on forgetting memristors

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SOFT COMPUTING 2023年第23期27卷 18403-18418页

作者： Liu, Yi Chen, Ling Li, Chuandong Liu, Xin Zhou, Wenhao Li, Ke Southwest Univ Chongqing Key Lab Nonlinear Circuits & Intelligent Elect Informat & Engn Chongqing 400715 Peoples R China Lappeenranta Lahti Univ Technol LUT Sch Engn Sci Comp Vis & Pattern Recognit Lab Lappeenranta Finland

The hardware circuit of neural network based on forgetting memristors not only has the characteristics of high computational efficiency and low power consumption, but also has the advantage that a memristor can store the weight of long-term memory and short-term memory. Neural networks based on forgetting memristors can process two different data sets;however, the number of data sets processed is determined by the conversion rate of short-term memory to long-term memory neural network. In this paper, a forgetting memristor model with controllable decay rate is proposed, the short-term memory and long-term memory of the long-term and short-term memory (LSTM) network based on forgetting memristor is proposed, and the conversion speed from short-term memory network to long-term memory network is controllable. In the process of transformation from short-term memory to long-term memory of LSTM network based on forgetting memristor, the decay rate of forgetting memristor can be controlled, and the duration of short-term memory of LSTM network can be set. A reset signal mechanism is proposed so that the state of short-term memory of LSTM network with high recognition rate can be controlled. Based on the proposed controllable decay rate and reset signal, the state of the short-term memory network with high recognition rate can be set, so the LSTM network with two states can realize the recognition of different number of images under different data sets. Finally, two kinds of data sets are tested on the LSTM network based on the forgetting memristor, and the recognition rate is good, which shows the effectiveness of the proposed algorithm.

关键词： Image classification Forgetting memristor Long- and short-term memory network Fixed decay Synchronous decay

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A Proximal Dynamic Approach to Equilibrium Problems With Finite-Time Convergence

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IEEE TRANSACTIONS ON AUTOMATIC CONTROL 2024年第3期69卷 1773-1780页

作者： Ju, Xingxing Li, Chuandong He, Xing Feng, Gang Southwest Univ Coll Elect & Informat Engn Chongqing Key Lab Nonlinear Circuits & Intelligent Chongqing 400715 Peoples R China City Univ Hong Kong Dept Biomed Engn Hong Kong Peoples R China

This article proposes a finite-time converging proximal dynamic model (FPD) to deal with equilibrium problems. A distinctive feature of the FPD is its fast and finite-time convergence, in contrast to conventional proximal dynamic methods. It is shown that the solution of the proposed FPD converges to the solution of the corresponding equilibrium problems in finite-time under some mild conditions. Then the proposed FPD is applied to solve problems of nonsmooth composite optimization and absolute value equations. It is further shown in the case of solving composite optimization problems that the equilibrium point of the proposed proximal gradient dynamic model is globally finite-time stable under the so-called proximal Polyak-Lojasiewicz condition, which is weaker than strong convexity. Finally, numerical examples are presented to illustrate the effectiveness of the proposed methods.

关键词： Convergence Optimization Mathematical models Dynamical systems Integrated circuit modeling Heuristic algorithms Robustness Absolute value equations composite optimization equilibrium problems finite-time convergence proximal dynamics

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Secure multi-channel information encryption based on integrated optical device

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EXPERT SYSTEMS WITH APPLICATIONS 2024年第PartC期249卷

作者： Chai, Junxiong Xie, Yiyuan Jiang, Xiao Su, Ye Li, Lili Southwest Univ Coll Elect & Informat Engn Chongqing 400715 Peoples R China Univ Chongqing Key Lab Networks & Cloud Comp Secur Chongqing 400715 Peoples R China Chongqing Key Lab Nonlinear Circuits & Intelligent Chongqing 400715 Peoples R China

In allusion to the privacy and security problems, a secure multi -channel information protection scheme based on an integrated optical device is proposed in this paper. The information to be encrypted and shared is converted into a QR code. For supporting the multi -channel encryption, a 1- n w encryption algorithm is employed, which possesses the capability to map the original image information into n w seemingly random cipher images. In terms of hardware, an ultra -compact optical encryption device is designed, which consists of two rows of ring resonators coupled to the waveguide. This novel encryption device can output ten cipher images, each transmitted on a separate channel. Meanwhile, the vertical -cavity surface -emitting laser subject to optical injection and optical feedback is researched to generate optical chaos sequences, which are introduced for key generation and QR code encryption. Ultimately, combining the 1- n w encryption algorithm and optical chaos, information protection is implemented based on integrated optics. The simulation results demonstrate the practical feasibility and remarkable effectiveness of the proposed scheme. Furthermore, various security and robustness analyses are performed, and the results reveal that the proposed information protection scheme has a high level of security and robustness. This work is of great significance for the development of optical encryption chips and provides potential applications for secure data sharing.

关键词： information security Optical encryption Plasmonic encryption device Multi-wavelength Optics chaos

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SRMC: A Multibit Memristor Crossbar for Self-Renewing Image Mask

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IEEE TRANSACTIONS ON VERY LARGE SCALE INTEGRATION (VLSI) SYSTEMS 2018年第12期26卷 2830-2841页

作者： Shang, Liuting Duan, Shukai Wang, Lidan Huang, Tingwen Southwest Univ Coll Elect & Informat Engn Chongqing Key Lab Nonlinear Circuits & Intelligen Chongqing 400715 Peoples R China Southwest Univ Brain Inspired Comp & Intelligent Control Key Lab Chongqing Key Lab Nonlinear Circuits & Intelligen Coll Elect & Informat EngnChongqing Collaborat I Chongqing 400715 Peoples R China Southwest Univ Chongqing Collaborat Innovat Ctr Brain Sci Natl & Local Joint Engn Lab Intelligent Transmiss Coll Elect & Informat Engn Chongqing 400715 Peoples R China Texas A&M Univ Qatar Dept Math Doha 23874 Qatar

A recent surge of research on deep convolutional neural network (DCNN) has given a challenge on existing computation architecture which has the flows in speed and memory bottleneck. In the pretreatment of deep learning, mask operation is frequently used to remove noise or fetch information. However, under data-intensive conditions, applying mask frequently can put an extremely heavy memory/communication burden on computing system. An efficient substrate of DCNN for ameliorating mask operation is urgently needed. In this paper, we present a self-renewing mask circuit (SRMC) to alleviate/solve earlier problems. First, a new approach to apply mask is presented based on computation-in-memory architecture that implements high-performance processor and high-density memory in the same physical location. Second, we designed the peripheral circuit which can provide self-renewing function to further avoid the data exchange with an external space. As opposed to most other computational element, which calculates two 1-bit data, the proposed SRMC storage multibit value and calculate several of them parallel. The calculating ability of SRMC is significantly superior to those of computational element in general architectures. Moreover, mean filter and edge detector are implemented to illustrate the effectiveness and scalability of the proposed circuit. Finally, we discussed two possible methods to enhance the practicability of our scheme.

关键词： Computation-in-memory (CIM) image processing mask operation memristor crossbar

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