Ding-Xue Zhang

Robust mode-free sliding mode control of multi-fingered hand with position synchronization in the task space

In this paper, for the purpose of improving operation performance of multi-fingered hand and multiple robotic manipulators, a robust position synchronization mode-free sliding-mode control (SMC) strategy is proposed. By invoking the Lyapunov stability approach, the effectiveness of the proposed approach is testified to be robust while facing various disturbances and dynamic uncertainties. Besides, according to the practical application, the kinematic diversity is taken into consideration. We assume each individual in the multi-agent system to be with kinematic redundancy or without. Finally, we present computer simulation results to verify the effectiveness of the proposed algorithm.

Multiconsensus of second-order multiagent systems with input delays

The multiconsensus problem of double-integrator dynamic multiagent systems has been investigated. Firstly, the dynamic multiconsensus, the static multiconsensus, and the periodic multiconsensus are considered as three cases of multiconsensus, respectively, in which the final multiconsensus convergence states are established by using matrix analysis. Secondly, as for the multiagent system with input delays, the maximal allowable upper bound of the delays is obtained by employing Hopf bifurcation of delayed networks theory. Finally, simulation results are presented to verify the theoretical analysis.

Consensus-based distributed optimisation of multi-agent networks via a two level subgradient-proximal algorithm

This paper presents a consensus-based stochastic subgradient algorithm for multi-agent networks to minimise multiple convex but not necessarily differential objective functions, subject to an intersection set of multiple closed convex constraint sets. Compared with the existing results an alternative subgradient algorithm is first introduced based on two level subgradient iterations, where the first level is to minimise the component functions, and the second to enforce the iterates not oscillate from the constraint set wildly. In addition, a distributed consensus-based type of the proposed subgradient algorithm is constructed within the framework of multi-agent networks for the case when the iteration index of local objective functions and local constraint sets is not homologous. Detailed convergence analysis of the proposed algorithms is established using matrix theories and super-martingale convergence theorem. In addition, a pre-step convergence factor is obtained in this study to characterise the distance between the iterations and the optimal set, while some existing literatures only present a convergence work. Simulation results are given to demonstrate the effectiveness of the developed theoretical results.

Multi-tracking of second order discrete-time multi-agent networks with aperiodic communication

A novel multi-tracking problem is introduced in multi-agent networks. The states of multiple agents in each subnetwork asymptotically converge to the same desired trajectory in the presence of information exchanges among subnetworks. A protocol with aperiodic sampled-data is proposed to achieve the multi-tracking of second order discrete-time multi-agent networks. The stability of dynamic systems is equivalent to the robust asymptotic stability of discrete-time systems with time-varying matrix uncertainties of polyhedral type. Necessary and sufficient conditions are established. Simulation results illustrate the effectiveness of the developed theory.

Distributed coordination of multiple mobile actuators for pollution neutralization

Abstract This paper is concerned with distributed coordination of multiple mobile actuators for pollution neutralization in a polluted environment, where a static mesh sensor network is pre-deployed for measuring the concentration of contaminants, and mobile actuators with neutralizing chemicals implement spraying operation at a steady rate to reduce the contaminants continuously. A hazard intensity distribution is introduced to evaluate adverse impact of contaminants on the environment. Autonomous actuators are dynamically deployed to minimize the total hazard intensity. This coordination problem can be formulated as a distributed deployment problem based on centroidal Voronoi tessellation (CVT). Two control strategies with switching motion controllers are proposed to achieve optimal deployment of mobile actuators for unlimited and limited actuating range respectively. To escape local minimum and balance the actuator workload, a novel workload adjustment strategy is designed to change the normalized amount of neutralizer sprayed by mobile actuators, which makes each actuator approach a common workload. Compared with pure CVT and switching motion controller, the total hazard intensity can be further decreased if the workload adjustment strategy is implemented. Simulation examples are provided to validate the effectiveness of the proposed method.

Robust multi-tracking of heterogeneous multi-agent systems with uncertain nonlinearities and disturbances

Abstract A robust multi-tracking problem is solved for heterogeneous multi-agent systems with uncertain nonlinearities and disturbances. The nonlinear function satisfies a Lipschitz condition with a time-varying gain, the integral of which is bounded by a linear function. A distributed impulsive protocol is proposed, where the position data and velocity data of desired trajectories are needed only at sampling instants. Based on the system decomposition technique, the error dynamic system of achieving multi-tracking is decomposed into two impulsive dynamic systems with vanishing perturbation and nonvanishing perturbation, respectively. Constructing a nominal model, then the multi-tracking problem is converted into the stability of impulsive dynamic system with nonvanishing perturbation under some conditions. It is proved that the proposed impulsive protocol is robust enough to solve the multi-tracking problem. Numerical examples are presented to illustrate the effectiveness of our theoretical results.

Disturbance attenuation over a first-order moving average Gaussian noise channel

In this paper, the problem of disturbance attenuation has been studied for a linear time-invariant feedback control system with a first-order moving average Gaussian noise channel. By applying the concept of entropy power, a lower bound of signal-to-noise ratio has been achieved which is necessary for stabilisation of a system with the limited channel input power constraint. Moreover, the problem of minimising the influence of a stochastic disturbance on the output has also been investigated, and the controller design method has been obtained by using Youla parameterisation and H2 theory. It is shown that the minimum variance of the system output depends not only on the disturbance variance, noise variance and unstable poles, but also on the non-minimum phase zeros and channel parameter. Finally, the effectiveness of the proposed results is illustrated by a numerical example.

Second-order consensus of discrete-time multi-agent systems via one-step delayed data

This paper considers the second-order consensus problem of discrete-time multi-agent systems over fixed and undirected network topologies. A distributed consensus protocol utilizing the current and one-step delayed position information is proposed. It is interesting to found that second-order consensus in such a discrete-time multi-agent system cannot be ensured without any one-step delayed position data while it can be reached by appropriately choosing the sampling interval and the coupling strength with one-step delayed position data. A necessary and sufficient condition for reaching second-order consensus in a discrete-time multi-agent dynamical system on the sampling interval, the control gain parameters and the eigenvalues of the Laplacian matrix has been established under fixed and undirected topology. Finally, numerical examples are given to illustrate the theoretical analysis.

Joint Smith predictor and neural network estimation scheme for compensating randomly varying time-delay in networked control system

In this paper, we dealt with the problems of network induced delay and randomly varying time-delay (RVTD) controlled plant in networked control systems (NCS). These inherent challenges make the conventional control methods, e.g, a mathematical model of Smith predictor combined with fuzzy adaptive controller, more difficult to meet the quality requirements for NCS stability. Specifically, based on analyzing the existing techniques, we propose a novel method to efficiently compensate the RVTD for NCS. This so-called RVTD compensator for the Smith predictor using Neural network estimation scheme has not only the features of simple Smith predict structure, but also the characteristics of adaptivity, stability, and fast response. The simulation results via TrueTime Beta2.0 platform demonstrate that our design significantly improves the performance of NCS.