Finite-time output feedback control for a class of second-order nonlinear systems with application to DC–DC buck converters

The problem of output feedback control for a class of second-order nonlinear systems is investigated in this paper. Using the techniques of finite-time control and finite-time convergent observer, an observer-based finite-time output feedback controller is proposed which can guarantee that the system’s state converges to the equilibrium in a finite time. As an application of the proposed theoretical results, the problem of finite-time control without current signal for the DC–DC buck converters is solved. Simulation results are given to demonstrate the effectiveness of the proposed method.     

Synchronization control for networked mobile robot systems based on Udwadia–Kalaba approach

This paper addresses the problem of synchronization control for networked multi-mobile robot systems from the perspective of analytical mechanics. By reformulating the task requirement as a constrained motion problem, a unified synchronization algorithm for networked multi-mobile robot systems with or without leaders is proposed in combination with algebraic graph theory and the Udwadia–Kalaba approach. With the proposed algorithm, the networked mobile robot system can achieve synchronization from arbitrary initial conditions for the leaderless case and realize accurate trajectory tracking with explicitly given reference trajectories for the leader-following case. Numerical simulations of a networked wheeled mobile robot system are performed under different network structures and various trajectory requirements to show the performance of the proposed control algorithm.

     

Adaptive synchronization of different Cohen–Grossberg chaotic neural networks with unknown parameters and time-varying delays

In this paper, an adaptive linear feedback controller is presented to study the synchronization problem of different Cohen–Grossberg neural networks with unknown parameters and time-varying delays. Lyapunov stability theory and Barbalat’s lemma are used to guarantee the response system can be synchronized with the drive system. The synchronization criteria of this paper which do not solve any linear matrix inequality are easily verified. These results remove some restrictions on amplification functions and activation functions. Finally, numerical simulations are carried out to illustrate the effectiveness of the obtained results.     

Complete synchronization of delay hyperchaotic Lü system via a single linear input

This work is devoted to investigating the complete synchronization of two identical delay hyperchaotic Lü systems with different initial conditions, and a simple complete synchronization scheme only with a single linear input is proposed. Based on the Lyapunov stability theory, sufficient conditions of synchronization are obtained for both linear feedback and adaptive control approaches. The problem of adaptive synchronization between two nearly identical delay hyperchaotic Lü systems with unknown parameters is also studied. A?single input adaptive synchronization controller is proposed, and the adaptive parameter update laws are developed. Numerical simulation results are presented to demonstrate the effectiveness of the proposed chaos synchronization scheme.     

Nonlinear extended state observer-based output feedback stabilization control for uncertain nonlinear half-car active suspension systems

Nonlinear Dynamics - This paper proposes a nonlinear extended state observer-based output feedback stabilization controller for a half-car active suspension system, to overcome factors leading to...     

一种基于比例反馈控制原理的动载荷时域反演方法

通过借鉴系统控制论中的比例反馈控制原理,提出了一种新的结构动载荷时域反演方法.该方法在原开环系统的输出与结构模型之间连接一个虚拟的比例反馈增益,使得原来的开环系统成为一个虚拟的闭环反馈控制系统,系统控制信号为实测的结构加速度响应.反馈控制器将系统输出与控制信号之闻的差值进行放大后作为反馈不断输入到结构模型中,直到差值趋于稳定,此时该差值与反馈增益的乘积经过高通滤波后即得到所反演的动态载荷.该方法将载荷反演问题的求解转化为正问题中的结构瞬态响应求解,采用一般的数值解法如New-mark法即可实现,因此计算比较简便迅速.该方法仅需要测量结构的加速度响应即可进行反演,便于实际应用,而且并不十分依赖于真实的初始条件,由于不存在误差累积的现象,反演结果具有较好的稳定性.最后,通过海洋平台结构冰载荷反演的模型实验和数值仿真证明了该方法的有效性.     

Synchronization in complex dynamical networks with interval time-varying coupling delays

This paper deals with the synchronization problem of complex dynamical networks with interval time-varying coupling delays. A simple local linear feedback controller is introduced to guarantee the synchronizability of the networks. Some delay-dependent synchronization conditions for the controlled complex dynamical networks are presented by using the Lyapunov–Krasovskii functional method and the reciprocally convex combination approach. Theoretical analysis and numerical examples show that the obtained conditions have less computational complexity and less conservatism than some recently reported ones.     

Robust H ∞ output tracking control for fuzzy networked systems with stochastic sampling and multiplicative noise

In this paper, the problem of robust sampled-data H _?? output tracking control is investigated for a class of fuzzy networked systems with stochastic sampling, multiplicative noise and time-varying norm-bounded uncertainties. For the sake of technical simplicity, only two different sampling periods are considered, their occurrence probabilities are given constants and satisfy Bernoulli distribution, and they can be extended to the case with multiple stochastic sampling periods. By using an input-delay method, the probabilistic system is transformed into a stochastic continuous time-delay system. A?new linear matrix inequality(LMI)-based procedure is proposed for designing state-feedback controllers, which would guarantee that the closed-loop networked system with stochastic sampling tracks the output of a given reference model well in the sense of H _??. Conservatism is reduced by taking the probability into account. Both network-induced delays and packet dropouts have been considered. Finally, an illustrative example is given to show the usefulness and effectiveness of the proposed H _?? output tracking design.     

Chimera states and synchronization behavior in multilayer memristive neural networks

To study the effect of electromagnetic induction on the spatiotemporal dynamic behavior of neural networks, in this paper, we have mainly studied both the synchronization behavior and the evolution of chimera states (CS) in coupled neural networks. To do this, a multilayer memristive neural network was constructed by selecting the Hindmarsh–Rose neurons as the network nodes, and the effect of electromagnetic induction is introduced by using the cubic flux-controlled memristive model as synapse. For simplicity, the following coupling scheme is adopted: only the coupling connections for the neurons between different layers are considered with memristive synapses, while those neurons in each layer are still bidirectional coupled with the classical electrical synapses. It is found that, by adjusting the coupled strength of electrical synapses and the parameters of memristive synapses, the coexistence behavior of coherent and incoherent states, i.e., the CS, could appear in each layer. It is interesting that the CS are also found in inter-layer memristive synapse network. Furthermore, we have discussed the synchronization behavior in this multilayer memristive neural network, one can find when the whole multilayer network is in a synchronization state, not only the spatiotemporal consistency of the CS in each layer neural networks is observed, but also the memductance of all memristive synapses is completely synchronized. Our results suggest that the electromagnetic induction may play an important role in regulating the dynamic behavior of neural networks, and the introduction of memristive synapse into the biological neural network will provide useful clues for revealing the memory behavior of the neural system in human brain.     

Adaptive synchronization of chaotic Cohen–Crossberg neural networks with mixed time delays

In this paper, the problem of adaptive synchronization is investigated for a class of Cohen–Crossberg neural networks with mixed time delays. Based on a Lyapunov–Krasovskii functional and the invariant principle of function differential equations as well as the adaptive control and linear feedback with update law, a linear matrix inequality approach is developed to derive some novel sufficient conditions achieving synchronization of the two coupled networks with mixed time delays. In particular, the mixed time delays in this paper synchronously consist of constant delays, time-varying delays, and distributed delays, which are more general than those discussed in the previous literature. Therefore, the results obtained in this paper comprise and generalize those given in the previous literature. A numerical example and its simulation are provided to show the effectiveness of the theoretical results.     

Observer design and output feedback stabilization for nonlinear multivariable systems with diffusion PDE-governed sensor dynamics

This paper addresses the problems of observer design and output feedback stabilization for a class of nonlinear multivariable systems, where the nonlinear system dynamics are described by ordinary differential equations (ODEs), and the sensor dynamics are governed by diffusion partial differential equations (PDEs). Based on the Luenberger observer theory, a Luenberger-type PDE-ODE cascaded observer is derived to estimate the state variables of the system. Then, an observer-based output feedback stabilizing controller is developed. The exponential stability of both the observer error system and closed-loop control system is proven via the Lyapunov direct method. Finally, numerical examples are provided to illustrate the effectiveness of the proposed design methods.     

Memristive magnetic coupling feedback induces wave-pattern transition

Nonlinear Dynamics - In this paper, we design a memristive system involving magnetic coupling with time-delayed feedback. In a way of autaptic connection, the memristive magnetic coupling feedback...     

Output feedback ℋ∞ synchronization for delayed chaotic neural networks

In this paper, we propose a new output feedback ?_∞ synchronization method for delayed chaotic neural networks with external disturbance. Based on Lyapunov–Krasovskii theory and linear matrix inequality (LMI) approach, the output feedback ?_∞ synchronization controller is presented to not only guarantee stable synchronization, but also reduce the effect of external disturbance to an ?_∞ norm constraint. The proposed controller can be obtained by solving the LMI problem. An illustrative example is given to demonstrate the effectiveness of the proposed method.     

Synchronization of stochastic chaotic neural networks with reaction-diffusion terms

In this paper, we are concerned with the synchronization problem of a class of stochastic reaction-diffusion neural networks with time-varying delays and Dirichlet boundary conditions. By using the Lyapunov–Krasovskii functional method, feedback control approach and stochastic analysis technology, delay-dependent synchronization conditions including the information of reaction-diffusion terms are presented, which are expressed in terms of linear matrix inequalities (LMIs). The feedback controllers can be constructed by solving the derived LMIs. Finally, illustrative examples are given to show the effectiveness of the proposed technique.     

Adaptive fuzzy backstepping output feedback control for a class of MIMO time-delay nonlinear systems based on high-gain observer

In this paper, an adaptive fuzzy backstepping output feedback control approach is developed for a class of multiinput and multioutput (MIMO) nonlinear systems with time delays and immeasurable states. Fuzzy logic systems are employed to approximate the unknown nonlinear functions, and an adaptive fuzzy high-gain observer is developed to estimate the unmeasured states. Using the designed high-gain observer, and combining the fuzzy adaptive control theory with the backstepping approach, an adaptive fuzzy output feedback control is constructed recursively. It is proved that all the signals of the closed-loop adaptive control system are semiglobally uniformly ultimately bounded (SUUB) and the tracking error converges to a small neighborhood of the origin.     

Robust synchronization of uncertain chaotic neural networks with randomly occurring uncertainties and non-fragile output coupling delayed feedback controllers

This paper deals with the synchronization control problem for the uncertain chaotic neural networks with randomly occurring uncertainties and randomly occurring control gain fluctuations. By introducing an improved Lyapunov–Krasovskii functional and employing reciprocally convex approach, a delay-dependent non-fragile output feedback controller is designed to achieve synchronization with the help of a drive–response system and the linear matrix inequality approach. Finally, numerical results and its simulations are given to show the effectiveness of the derived results.     

$$H_\infty $$ switching synchronization for multiple time-delay chaotic systems subject to controller failure and its application to aperiodically intermittent control

This paper is concerned with the \(H_\infty \) synchronization control problem for a class of chaotic systems with multiple delays in the presence of controller temporary failure. Based on the idea of switching, the synchronization error systems with controller temporary failure are modeled as a class of switched synchronization error systems. Then, a switching condition that incorporates the controller failure time is constructed by using piecewise Lyapunov functional and average dwell-time methods, such that the switched synchronization error systems are exponentially stable and satisfy a weighted \(H_\infty \) performance level. In the meantime, a switching state feedback \(H_\infty \) controller is derived by solving a set of linear matrix inequalities. More incisively, the obtained results can also be applied to the issue of aperiodically intermittent control. Finally, three simulation examples are employed to illustrate the effectiveness and potential of the proposed method.     

Decentralized direct adaptive output feedback fuzzy H ∞ tracking design of large-scale nonaffine nonlinear systems

A novel H _∞ tracking-based decentralized direct adaptive output feedback fuzzy controller is developed for a class of interconnected nonaffine uncertain nonlinear systems in this paper. By virtue of the proper filtering of the observation error dynamics to assure its strictly positive realness, the observer-based decentralized direct adaptive fuzzy control (DAFC) scheme is presented for a class of large-scale nonaffine nonlinear systems by the combination of H _∞ tracking technique, implicit function theorem, a state observer and a fuzzy inference system. The output feedback and adaptation mechanisms for each subsystem depend upon local measurements not only to achieve asymptotical tracking of a reference trajectory but to guarantee arbitrary small attenuation level of the mismatched errors and external disturbances on the tracking error. Simulation results confirm the effectiveness of the proposed decentralized output feedback scheme.     

Observer-based event-triggered control for semilinear time-fractional diffusion systems with distributed feedback

This paper is concerned with the observer-based distributed event-triggered feedback control for semilinear time-fractional diffusion systems under the Robin boundary conditions. To this end, an extended Luenberger-type observer is presented to solve the limitations caused by the impossible availability of full-state information that is needed for feedback control in practical applications due to the difficulties of measuring. With this, we propose the distributed output feedback event-triggered controllers via backstepping technique under which the considered systems admit Mittag–Leffler stability. It is shown that the given event-triggered control strategy could significantly reduce the amount of transmitted control inputs while guaranteeing the desired system performance with the Zeno phenomenon being excluded. A numerical illustration is finally presented to illustrate our theoretical results.