Article
Automation & Control Systems
Hanfeng Li, Xianfu Zhang, Gang Feng
Summary: This article addresses the event-triggered output feedback control problem for switched nonlinear strict-feedback systems under asymmetric input saturation, proposing a new design procedure based on a reduced-order observer to construct an output feedback controller. By using hyperbolic tangent function and indicator function, the scheme ensures global boundedness of signals and convergence of output to a small region around the origin, without employing the conventional backstepping method. The effectiveness of the proposed control scheme is demonstrated using a continuous stirred tank reactor.
IEEE TRANSACTIONS ON CYBERNETICS
(2021)
Article
Engineering, Electrical & Electronic
Liandi Fang, Shihong Ding, Ju H. Park, Li Ma
Summary: This paper addresses the control problem of p-norm stochastic nonlinear systems with output constraints, proposing an adaptive fuzzy output-feedback control strategy. By constructing a nonlinear observer and utilizing a barrier Lyapunov function, the proposed scheme ensures all signals of the closed-loop systems are bounded and do not violate output constraints.
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS I-REGULAR PAPERS
(2021)
Article
Automation & Control Systems
Yize Mi, Jianyong Yao
Summary: This article addresses the tracking control problem of a class of control-affine nonlinear systems subject to input saturation, parametric uncertainties, and unmodeled uncertainties. A nested-saturation-function-based controller integrated with feedforward model compensation is proposed. A saturated linear extended state observer (SLESO) and parameter adaptation law are used to compensate for unmodeled uncertainties and parametric uncertainties respectively. The proposed scheme guarantees steady-state tracking performance through uncertainty compensation and effectively addresses the conservativeness issue in the input saturation problem by online-updating the available unsaturated region, improving transient performance. The proposed scheme ensures asymptotic stability under constant unmodeled uncertainties and ultimate boundedness under time-varying unmodeled uncertainties. Simulation studies are presented to demonstrate the effectiveness of the proposed approach.
INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL
(2023)
Article
Automation & Control Systems
Ines Righi, Sabrina Aouaouda, Mohammed Chadli, Khaled Khelil
Summary: This article proposes a method for designing robust controller laws for a class of uncertain nonlinear parameter varying (NLPV) descriptor systems under input saturation and external disturbances. The stability conditions are derived using polytopic parameter-dependent (PD) nonquadratic Lyapunov functions and L2 gain performance is used to attenuate the effect of the external disturbance signals. The largest domain of attraction (DoA) for the system is estimated and solved as an optimization problem, demonstrating the effectiveness of the proposed design methods through two examples.
INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL
(2021)
Article
Automation & Control Systems
Fei Shen, Xinjun Wang, Xinghui Yin
Summary: This paper proposes an adaptive neural output-feedback control for a class of stochastic nonlinear systems with unknown control directions and hysteresis input, utilizing radial basis function neural networks and adaptive backstepping method. A state observer is used to estimate unmeasurable system state signals, with Nussbaum gain technique for unknown control directions, and consideration of backlash-like hysteresis input control. The designed adaptive controller ensures convergence of output tracking error to a small region around the origin, with all signals in the closed-loop systems being semi-global uniformly ultimately bounded. Simulation results validate the effectiveness of the theoretical analysis.
INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE
(2021)
Article
Acoustics
Majid Shahbazzadeh, Homa Salehifar, S. Jalil Sadati
Summary: This study investigates the problem of optimal guaranteed cost control for nonlinear systems under input saturation. The goal is to design a dynamic output feedback controller that ensures asymptotic stability of the closed-loop system and minimizes the upper bound of the cost function. The designed controller also guarantees that the control signals stay within their permissible range. The paper formulates the problem as an optimization problem with bilinear matrix inequality constraints, which are converted into linear matrix inequality conditions using technical lemmas. Simulation results demonstrate the effectiveness and advantages of the proposed theoretical results.
JOURNAL OF VIBRATION AND CONTROL
(2023)
Article
Automation & Control Systems
Ruiming Xie, Shengyuan Xu
Summary: This article investigates the adaptive state-feedback control problem of output-constrained stochastic high-order nonlinear systems with stochastic integral input-to-state stability (SiISS) inverse dynamics. Two new control design and analysis methods are proposed based on a key nonlinear transformation function and the use of SiISS small-gain condition. The simulation result demonstrates the effectiveness of this control method in guaranteeing system stability without violating output constraint.
INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL
(2023)
Article
Automation & Control Systems
Muhammad Hamad Zaheer, Khalid M. Arthur, Se Young (Pablo) Yoon
Summary: This paper investigates the local stabilization of nonlinear systems with uncertain equilibrium states and actuator constraints. A derivative feedback control scheme is proposed to stabilize the system and drive it to its true equilibrium state even in the presence of uncertainty. Stability conditions are derived in the form of matrix inequalities for cases with bounded actuator output energy and saturation, and numerical methods are discussed for synthesizing feasible control solutions. The effectiveness of the proposed method is demonstrated through a numerical example and a magnetic levitation test rig.
Article
Engineering, Marine
Zhipeng Shen, Ang Li, Li Li, Haomiao Yu
Summary: This paper focuses on adaptive robust output feedback tracking control of an underactuated ship considering input saturation and unavailable velocities. A nonlinear observer is designed for the estimation of velocities, and the backstepping method is combined with the dynamic surface control (DSC) technique for stabilization of tracking errors and resolution of complexity explosion problem. An adaptive algorithm is also designed to estimate the upper bound of external disturbances. According to Lyapunov stability theory, all closed-loop signals are uniformly ultimately bounded (UUB).
JOURNAL OF MARINE SCIENCE AND TECHNOLOGY
(2022)
Article
Automation & Control Systems
Chunxiao Wang, Zhongcai Zhang, Yuqiang Wu
Summary: This article presents the controller design and stability analysis for stochastic cascade nonlinear systems with external disturbances. A new extended state observer based on the Riccati differential equation is constructed to estimate the unmeasurable system states and the external disturbance. An adaptive output feedback controller is proposed based on stochastic input-to-state stability and backstepping design technique. The designed controller guarantees that all closed-loop system signals are bounded and the original system states are globally asymptotically stable in probability.
INTERNATIONAL JOURNAL OF ADAPTIVE CONTROL AND SIGNAL PROCESSING
(2022)
Article
Automation & Control Systems
Jun Mao, Wencheng Zou, Jian Guo, Zhengrong Xiang
Summary: The contribution of this research is to solve a sampled-data output feedback stabilization problem for a class of upper-triangular stochastic nonlinear systems in p- normal structure with unavailable states and uncertain time-varying input delay. By developing a reduced-order observer and constructing a sampled-data output feedback stabilizer, the adverse influences caused by uncertain time-varying delays can be effectively restricted. Furthermore, the global stability of the closed-loop system can be analyzed using suitable design parameters and allowable sampling period.
INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL
(2022)
Article
Automation & Control Systems
Liang Liu, Jiaming Lu, Mengru Kong
Summary: This paper discusses exponentially stable problem for a class of stochastic strict feedforward nonlinear systems, presenting a parameter-dependent controller to handle nonlinearities. Through coordinate transformation and parameter selection, the proposed controller ensures stability of the closed-loop system as demonstrated by stochastic Lyapunov stability theory. Simulation results validate the efficiency of the proposed controller.
JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS
(2021)
Article
Automation & Control Systems
Rong-Heng Cui, Xue-Jun Xie
Summary: This paper focuses on the problem of output feedback stabilization of stochastic planar nonlinear systems with output constraint. By introducing new nonlinear mappings, adding a power integrator and homogeneous domination techniques, two new design and analysis frameworks are established in this paper to achieve stochastic asymptotic stability and stochastic finite-time stability of the trivial solution of the closed-loop system, while ensuring the requirements of almost sure boundedness of the closed-loop signals and the symmetric output constraint.
Article
Automation & Control Systems
Wonseok Ha, Juhoon Back
Summary: This article presents a robust output feedback stabilizer for multi-input-multi-output nonlinear systems subject to external disturbances and system uncertainties. The stabilizer is designed based on the assumption that there exists a state feedback controller that makes the origin of the nominal closed-loop system asymptotically stable. The proposed controller can handle the nonlinearity of the input gain matrix of the nominal system.
IEEE TRANSACTIONS ON AUTOMATIC CONTROL
(2023)
Article
Mathematics, Applied
Guangtong Zhao, Liang Cao, Xiaomeng Li, Qi Zhou
Summary: This paper investigates the consensus tracking control problem for stochastic nonlinear multiagent systems with nonstrict-feedback structure and unmeasurable states. By proposing an event-triggered estimator and an observer, an adaptive neural control strategy and a modified dynamic event-triggered mechanism are developed to achieve control and restrictions on the system. Experimental results confirm the convergence of consensus tracking errors and the boundedness of system signals with the proposed controllers.
APPLIED MATHEMATICS AND COMPUTATION
(2022)
Article
Engineering, Electrical & Electronic
Qingtan Meng, Qian Ma, Yang Shi
Summary: This paper investigates the problem of fixed-time stabilization for a class of nonlinear systems using event-triggered control. The event-triggered mechanism is applied to handle the coexistence of low-order and high-order nonlinearities in the system. By dividing the initial value of the system into two cases and designing an event-triggered controller for each case, the authors are able to deal with both low-order and high-order terms and achieve the objective of fixed-time stabilization. The paper proves the global fixed-time stability of the nonlinear system under the designed controller using switching control theory, while excluding the occurrence of Zeno behavior. The effectiveness of the proposed technology is demonstrated through two simulations.
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS I-REGULAR PAPERS
(2022)
Article
Computer Science, Artificial Intelligence
Yang Liu, Zhen Wang, Qian Ma, Hao Shen
Summary: This paper studies the multistability of delayed recurrent neural networks (DRNNs) with a class of piecewise nonlinear activation functions. The coexistence and stability of multiple equilibrium points (EPs) of DRNNs are proved. DRNNs with the proposed activation function can have more total EPs and more locally stable EPs compared to DRNNs with sigmoidal activation functions, indicating a larger storage capacity when applied in associative memory.
Article
Engineering, Electrical & Electronic
Hui Xu, Guozeng Cui, Qian Ma, Ze Li, Wanjun Hao
Summary: This study investigates the problem of fixed-time distributed formation control for multiple quadrotor unmanned aerial vehicles (QUAVs) suffering from external disturbances. By using the fixed-time disturbance observer (FTDO), unknown disturbances are accurately estimated without requiring the information of initial observer errors. The proposed singularity-free distributed formation controllers, based on the fusion of command filter technique, nonsmooth error compensation mechanism, and backstepping design method, ensure that the whole closed-loop system is practically fixed-time stable, and the formation tracking errors converge to a small neighborhood of the zero in a fixed time. Finally, a numerical simulation verifies the validity of the devised distributed formation control scheme.
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS
(2023)
Article
Engineering, Electrical & Electronic
Zhaoming Sheng, Qian Ma, Shengyuan Xu
Summary: This paper presents a novel prescribed-time output feedback control strategy for high-order nonlinear systems. A new coordinate transformation method is proposed, introducing a time-varying gain that goes to infinity towards the terminal time and a constant scaling gain. The reduced-order observer and output feedback controller are constructed using the homogeneous domination approach. Unlike existing results, this control strategy ensures that the convergence time is independent of initial conditions and design parameters, with adjustable convergence rate. The design process is simplified and the control scheme can operate even after prescribed time. A simulation example verifies the validity of the proposed control strategy.
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS
(2023)
Article
Computer Science, Artificial Intelligence
Wenhui Liu, Qian Ma, Yuan Lu, Shengyuan Xu
Summary: This work investigates the adaptive fixed-time disturbance rejection control issue for a class of time-delay nonlinear systems subject to event-triggered and quantized input signals. A disturbance observer is proposed to promote the controller design. Combined with the backstepping technique and fuzzy logic systems, an adaptive fuzzy event-triggered and quantized fixed-time control scheme is designed. The Lyapunov-Krasovskii functional method is utilized to achieve system stability and deal with unknown time delays. The proposed control algorithm reduces transmission burden and improves the robustness of the time-delay nonlinear system. A numerical example and a practical example are given to validate the feasibility of the control scheme.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Yingkang Xie, Qian Ma, Jason Gu, Guopeng Zhou
Summary: This article addresses the problem of adaptive fuzzy event-triggered fixed-time practical tracking control for flexible-joint robot system. Fuzzy logic systems are used due to the difficulties in obtaining the system's nonlinearities. Second-order command filters and a novel compensation system are employed to ensure stability and convergence of the error. The proposed control strategy, based on backstepping technique, guarantees boundedness of the closed-loop system variables and arbitrary small tracking error in fixed time.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Yingkang Xie, Qian Ma
Summary: This article explores the adaptive event-triggered-based neural network control for switching nonlinear systems with nonstrict-feedback structure and time-varying delays. The switching observer is designed for estimating unmeasurable states, and a compensation system is introduced to deal with the existence of time-varying input delay. The study establishes the average dwell-time (ADT) scheme and the event-triggered controller to achieve the semiglobal uniform ultimate boundedness (SGUUB) of all variables and to avoid Zeno behavior. The numerical simulation demonstrates the effectiveness of the proposed control approach.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Lan Yao, Zhen Wang, Xia Huang, Yuxia Li, Qian Ma, Hao Shen
Summary: This article investigates the exponential synchronization of Markovian jump neural networks with time-varying delays using stochastic sampling and looped-functional approach. A less conservative exponential synchronization criterion is derived and a mode-independent stochastic sampled-data controller is designed. Numerical results demonstrate the effectiveness of the proposed control strategy.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Automation & Control Systems
Qingtan Meng, Qian Ma, Shengyuan Xu
Summary: This article addresses the issue of global stabilization for a class of nonlinear uncertain time-delay systems with saturated input. The saturated input is handled by a new auxiliary system with dynamic gain and the linear growth condition with unknown growth rates is eliminated. A novel adaptive observer-controller coupling design framework is proposed, ensuring global boundedness of the closed-loop system. Finally, simulation results demonstrate the effectiveness of the proposed technique.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Wei Liu, Jianhang Zhao, Huanyu Zhao, Qian Ma, Shengyuan Xu, Ju H. Park
Summary: This article studies a finite-time fuzzy adaptive dynamic surface control (DSC) method based on a nonlinear disturbance observer (NDO) for nonlinear systems with external disturbances and preassigned performance indices. By constructing a finite-time preassigned-performance function (FTPF), the tracking error is confined within its boundaries, satisfying performance metrics. The proposed composite NDO (CNDO) scheme incorporates fuzzy adaptive control to estimate unknown composite disturbances.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2023)
Article
Automation & Control Systems
Yingkang Xie, Qian Ma, Shengyuan Xu
Summary: This article explores adaptive event-triggered finite-time control for uncertain nonlinear systems with time delay. The Lyapunov-Krasovskii function is used to handle time-varying state delays, while fuzzy-logic systems are used to deal with unknown nonlinearities. A novel switch function is used to derive virtual control laws that avoid singularity hindrance. A dynamic event-triggered controller is designed to reduce communication pressure, and it is proven to be Zeno free. The proposed control strategy ensures arbitrarily small tracking errors in finite time and bounded variables in the closed-loop system. Simulation results are provided to demonstrate the effectiveness of the control strategy.
IEEE TRANSACTIONS ON CYBERNETICS
(2023)
Article
Automation & Control Systems
Wenhui Liu, Qian Ma, Shengyuan Xu
Summary: This paper investigates the output-feedback-based event-triggered control issue of a class of uncertain nonlinear systems considering state quantization and input delay. A discrete adaptive control scheme is designed based on the dynamic sampled and quantized mechanism by constructing the state observer and adaptive estimation function. The global stability of the time-delay nonlinear systems is ensured by the Lyapunov-Krasovskii functional method and a stability criterion. In addition, the Zeno behavior will not happen in the event-triggering. Numerical and practical examples are presented to validate the effectiveness of the designed discrete control algorithm with input time-varying delay.
IEEE TRANSACTIONS ON CYBERNETICS
(2023)
Article
Automation & Control Systems
Qian Ma, Qingtan Meng, Shengyuan Xu
Summary: In this article, the authors investigate distributed output optimization for general uncertain high-order nonlinear multiagent systems. They utilize the dynamic gain approach to overcome the influence of unknown optimal solution. They propose a distributed optimal coordinator with an adjustable parameter to steer the generated signals towards the optimal solution. By developing an iterative design strategy, they design dynamic reference-tracking controllers to ensure that each agent's output follows the coordinator's generated value. Simulation studies validate the effectiveness of the proposed method.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Chunping Xiong, Qian Ma, Jian Guo, Frank L. Lewis
Summary: This article studies the optimal synchronization of linear heterogeneous multiagent systems with partial unknown knowledge of the system dynamics. A framework of heterogeneous multiagent graphical games is formulated, and it is proved that the optimal control policy is not only in Nash equilibrium, but also the best response to fixed control policies of its neighbors. Then, model-based policy iteration and data-based off-policy integral reinforcement learning algorithms are proposed to solve the optimal control policy and handle the partially unknown system dynamics.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
