Yuequan Yang

Adaptive neural tracking control of pure-feedback nonlinear systems with unknown gain signs and unmodeled dynamics

In this paper, an novel adaptive tracking control is developed for a class of completely non-affine pure-feedback nonlinear systems using radial basis function neural networks (RBFNNs). Combining the dynamic surface control (DSC) technique and backstepping method, the explosion of complexity in the traditional backstepping design is avoided. Using mean value theorem and Youngs inequality, only one learning parameter need to be tuned online in the whole controller design, and the computational burden is effectively alleviated. It is proved that the proposed design method is able to guarantee semi-global uniform ultimate boundedness (SGUUB) of all signals in the closed-loop system. Simulation results verify the effectiveness of the proposed approach.

On sliding mode control of single input Markovian jump systems

In this paper, an equivalent control based sliding mode control is proposed for single input linear Markovian jump systems which guarantees the asymptotical stability. Furthermore, by using the stochastic system theory, a multi-step state transition conditional probability function is introduced for the continuous Markovian process, which is used to define the reaching and sliding probabilities. Furthermore, the formulas for calculating reaching and sliding probabilities are derived for situations where the control force may not be strong enough to ensure the fully asymptotical stability. Extensive simulations are conducted to validate the theoretical results and show the relationship between the control force and reaching and sliding probabilities.

Sliding mode control of MIMO Markovian jump systems

This paper addresses the sliding mode control problem for uncertain MIMO linear Markovian jump systems. Firstly, by using the linear matrix inequality approach, sufficient conditions are proposed to guarantee the stochastically asymptotical stability of the system on the sliding surfaces. Secondly, an equivalent control based sliding mode control is proposed, such that the closed-loop system can be driven onto the desired sliding surfaces in a finite time. Finally, combining with multi-step state transition probability, the reaching and sliding probabilities are derived for situations where the control force may not be strong enough to ensure the fully asymptotical stability. Simulation results are presented to illustrate the effectiveness of the proposed design method.

Stability probability in sliding mode control of second order Markovian jump systems

This paper explores the relationship between system stability conditional probability and the sliding mode control for second order continuous Markovian jump systems. By using the stochastic process theory, multi-step state transition conditional probability function is proposed for the continuous time discrete state Markovian process. A sliding mode control scheme is utilized to stabilize the continuous Markovian jump systems. The system stability conditional probability function is derived. It indicates that the system stability conditional probability is a monotonically bounded non-decreasing non-negative piecewise right continuous function of the control parameter. A numerical example is given to show the feasibility of the theoretical results.

A Distributed Hunting Approach for Multiple Autonomous Robots

A novel distributed hunting approach for multiple autonomous robots in unstructured mode-free environments, which is based on effective sectors and local sensing, is proposed in this paper. The visual information, encoder and sonar data are integrated in the robots local frame, and the effective sector is introduced. The hunting task is modelled as three states: search state, round-obstacle state, and hunting state, and the corresponding switching conditions and control strategies are given. A form of cooperation will emerge where the robots interact only locally with each other. The evader, whose motion is a priori unknown to the robots, adopts an escape strategy to avoid being captured. The approach is scalable and may cope with problems of communication and wheel slippage. The effectiveness of the proposed approach is verified through experiments with a team of wheeled robots.

Fairness and Dynamic Flow Control in Both Unicast and Multicast Architecture Networks

With the development of multicast service in the Internet, much attention has been drawn to multicast congestion control and analysis. Multicast traffic poses new challenges to the design of Internet congestion control protocols and system stability analysis. The rate control problem of feedback-based sessions on the coexistence of both unicast and multirate multicast traffic architecture networks is focused upon in this paper. First, a fairness problem is discussed in detail, and a reasonable consumption strategy is proposed. In the reasonable consumption strategy, scaling functions are adaptively adjusted based on a relationship between the session rates. Second, contraposing the case that available link capacities are changing with time for these feedback-based unicast and multicast sessions, stability analysis of a closed-loop rate control system under the modified rate mechanism is made based on Lyapunov stable theory. Finally, the simulations illustrate the effectiveness and goodness of the reasonable consumption strategy

An object recognition approach based on structural feature for cluttered indoor scene

In this paper, aiming at the indoor scene under monitoring by visual sensor network (VSN), an object recognition approach based on structural feature is presented. Firstly, we regard the output of existing line segment detector LSD with proper parameters as the preliminary extraction result and it still will be further restored and split. Then, we give an inference model based on structural features of object including line segment ontology characteristics and relative relationship between the line segments. Finally, the objects are recognized with position information through inference. The effectiveness of the approach is verified, and the results show that our approach does not rely on segmentation and has robustness on partial defect and structural deformation to some extent.

Weighted small world complex networks: smart sliding mode control

In this paper, we establish a novel weighted small world complex network which is generated by assigning special weight to rewired links according to concrete scenarios. The smart sliding mode control for weighted small world complex networks is developed based on the ergordicity characteristic of chaos dynamical systems. The most distinctive of this scheme is without involvement of linearization and other ideal assumptions, which forces system state of the modified small-world complex network to approach to the desired manifolds and eventually realize the asymptotical synchronization behavior of weighted small world complex networks.

Synchronization Control for Stochastic Neural Networks with Mixed Time-Varying Delays

Synchronization control of stochastic neural networks with time-varying discrete and continuous delays has been investigated. A novel control scheme is proposed using the Lyapunov functional method and linear matrix inequality (LMI) approach. Sufficient conditions have been derived to ensure the global asymptotical mean-square stability for the error system, and thus the drive system synchronizes with the response system. Also, the control gain matrix can be obtained. With these effective methods, synchronization can be achieved. Simulation results are presented to show the effectiveness of the theoretical results.

Adaptive dynamic surface control of nonlinear systems with perturbed uncertainties in strict-feedback form

Based on the approximation capability of radial basis neural networks and the integral-type Lyapunov function, adaptive dynamic surface control(DSC) is investigated for a class of strict-feedback nonlinear systems with unknown virtual control gain functions. The main advantages of the proposed scheme are that only one parameter is adjusted in the whole backstepping design by using Youngs inequality and dynamic surface control, and the computational burden is effectively alleviated. By theoretical analysis, the closed-loop control system is proved to be semi-globally uniformly ultimately bounded, with arbitrary small tracking error by appropriately choosing design constants. Simulation results demonstrate the effectiveness of the proposed method.