Yun Zhang

Bio: Yun Zhang is an academic researcher from Guangdong University of Technology. The author has contributed to research in topics: Fuzzy logic & Nonlinear system. The author has an hindex of 36, co-authored 289 publications receiving 4472 citations. Previous affiliations of Yun Zhang include Guangzhou University.

Fuzzy Adaptive Quantized Control for a Class of Stochastic Nonlinear Uncertain Systems

Abstract: In this paper, a fuzzy adaptive approach for stochastic strict-feedback nonlinear systems with quantized input signal is developed. Compared with the existing research on quantized input problem, the existing works focus on quantized stabilization, while this paper considers the quantized tracking problem, which recovers stabilization as a special case. In addition, uncertain nonlinearity and the unknown stochastic disturbances are simultaneously considered in the quantized feedback control systems. By putting forward a new nonlinear decomposition of the quantized input, the relationship between the control signal and the quantized signal is established, as a result, the major technique difficulty arising from the piece-wise quantized input is overcome. Based on fuzzy logic systems’ universal approximation capability, a novel fuzzy adaptive tracking controller is constructed via backstepping technique. The proposed controller guarantees that the tracking error converges to a neighborhood of the origin in the sense of probability and all the signals in the closed-loop system remain bounded in probability. Finally, an example illustrates the effectiveness of the proposed control approach.

Adaptive Consensus of Nonlinear Multi-Agent Systems With Non-Identical Partially Unknown Control Directions and Bounded Modelling Errors

Abstract: Existing Nussbaum function based results on consensus of multi-agent systems require that the unknown control directions of all the agents should be the same. This note proposes an adaptive method to relax such a requirement to allow non-identical control directions, under the condition that some control directions are known. Technically, a novel idea is proposed to construct a new Nussbaum function, from which a conditional inequality is developed to handle time-varying input gains. Then, the inequality is integrated with adaptive control technique such that the proposed Nussbaum function for each agent is adaptively updated. Moreover, in addition to parametric uncertainties, each agent has non-parametric bounded modelling errors which may include external disturbances and approximation errors of static input nonlinearities. Even in the presence of such uncertainties, the proposed control scheme is still able to ensure the states of all the agents asymptotically reach perfect consensus. Finally, simulation study is performed to show the effectiveness of the proposed approach.

Adaptive least squares support vector machines filter for hand tremor canceling in microsurgery

Abstract: One of the main problems for effective control of a minimally invasive surgery (MIS) is the imprecision that caused by hand tremor. In this paper, a novel adaptive filter, the least squares support vector machines adaptive filter (LS-SVMAF), is proposed to overcome this problem. Compared with traditional methods like multi layer perceptron (MLP), LS-SVM shows a superior performance of nonlinear modeling with small scale of data set or high dimensional input space. With the LS-SVMAF, we can model and predict the hand tremor more effectively and improve the precision and reliability in the master–slave robotic system for microsurgery. Simulation results demonstrate the effectiveness of the proposed filter and its superior performance over its competing rivals.

Adaptive Fuzzy Control for a Class of Stochastic Pure-Feedback Nonlinear Systems With Unknown Hysteresis

Abstract: This paper addresses the problem of adaptive fuzzy control for a class of stochastic pure-feedback nonlinear systems with unknown direction hysteresis. Compared with the existing researches on hysteresis problem, the stochastic disturbances and the unknown hysteresis are simultaneously considered in the pure-feedback systems. In addition, the hysteresis parameters as well as the direction of hysteresis are unknown. By introducing an auxiliary virtual controller and employing the new properties of Nussbaum function, the major technique difficulty arising from the unknown direction hysteresis is overcome. Based on the fuzzy logic system's online approximation capability, a novel adaptive fuzzy control scheme is presented via the backstepping technique. It is shown that the proposed control scheme guarantees that all the signals of the closed-loop system are semiglobally uniformly bounded in probability, and the tracking error converges to a neighborhood of the origin in the sense of mean quantic value. Finally, simulation results further demonstrate the effectiveness of the proposed control scheme.

Adaptive Neural Control for Dual-Arm Coordination of Humanoid Robot With Unknown Nonlinearities in Output Mechanism

Abstract: To achieve an excellent dual-arm coordination of the humanoid robot, it is essential to deal with the nonlinearities existing in the system dynamics. The literatures so far on the humanoid robot control have a common assumption that the problem of output hysteresis could be ignored. However, in the practical applications, the output hysteresis is widely spread; and its existing limits the motion/force performances of the robotic system. In this paper, an adaptive neural control scheme, which takes the unknown output hysteresis and computational efficiency into account, is presented and investigated. In the controller design, the prior knowledge of system dynamics is assumed to be unknown. The motion error is guaranteed to converge to a small neighborhood of the origin by Lyapunov’s stability theory. Simultaneously, the internal force is kept bounded and its error can be made arbitrarily small.

The sector bound approach to quantized feedback control | NOVA. The University of Newcastle's Digital Repository

Abstract: This paper studies a number of quantized feedback design problems for linear systems. We consider the case where quantizers are static (memoryless). The common aim of these design problems is to stabilize the given system or to achieve certain performance with the coarsest quantization density. Our main discovery is that the classical sector bound approach is nonconservative for studying these design problems. Consequently, we are able to convert many quantized feedback design problems to well-known robust control problems with sector bound uncertainties. In particular, we derive the coarsest quantization densities for stabilization for multiple-input-multiple-output systems in both state feedback and output feedback cases; and we also derive conditions for quantized feedback control for quadratic cost and H/sub /spl infin// performances.

A Survey on Smart Grid CommunicationInfrastructures: Motivations, Requirements andChallenges

Abstract: A communication infrastructure is an essential part to the success of the emerging smart grid. A scalable and pervasive communication infrastructure is crucial in both construction and operation of a smart grid. In this paper, we present the background and motivation of communication infrastructures in smart grid systems. We also summarize major requirements that smart grid communications must meet. From the experience of several industrial trials on smart grid with communication infrastructures, we expect that the traditional carbon fuel based power plants can cooperate with emerging distributed renewable energy such as wind, solar, etc, to reduce the carbon fuel consumption and consequent green house gas such as carbon dioxide emission. The consumers can minimize their expense on energy by adjusting their intelligent home appliance operations to avoid the peak hours and utilize the renewable energy instead. We further explore the challenges for a communication infrastructure as the part of a complex smart grid system. Since a smart grid system might have over millions of consumers and devices, the demand of its reliability and security is extremely critical. Through a communication infrastructure, a smart grid can improve power reliability and quality to eliminate electricity blackout. Security is a challenging issue since the on-going smart grid systems facing increasing vulnerabilities as more and more automation, remote monitoring/controlling and supervision entities are interconnected.

Adaptive Neural Network Control of an Uncertain Robot With Full-State Constraints

Abstract: This paper studies the tracking control problem for an uncertain ${n}$ -link robot with full-state constraints The rigid robotic manipulator is described as a multiinput and multioutput system Adaptive neural network (NN) control for the robotic system with full-state constraints is designed In the control design, the adaptive NNs are adopted to handle system uncertainties and disturbances The Moore–Penrose inverse term is employed in order to prevent the violation of the full-state constraints A barrier Lyapunov function is used to guarantee the uniform ultimate boundedness of the closed-loop system The control performance of the closed-loop system is guaranteed by appropriately choosing the design parameters Simulation studies are performed to illustrate the effectiveness of the proposed control

Event-Triggered Adaptive Control for a Class of Uncertain Nonlinear Systems

Abstract: In this technical note, the problem of event-trigger based adaptive control for a class of uncertain nonlinear systems is considered. The nonlinearities of the system are not required to be globally Lipschitz. Since the system contains unknown parameters, it is a difficult task to check the assumption of the input-to-state stability (ISS) with respect to the measurement errors, which is required in most existing literature. To solve this problem, we design both the adaptive controller and the triggering event at the same time such that the ISS assumption is no longer needed. In addition to presenting new design methodologies based on the fixed threshold strategy and relative threshold strategy, we also propose a new strategy named the switching threshold strategy. It is shown that the proposed control schemes guarantee that all the closed-loop signals are globally bounded and the tracking/stabilization error exponentially converges towards a compact set which is adjustable.