Xingjian Jing

An LMI approach to stability of systems with severe time-delay

This note describes the stability problems of uncertain systems with arbitrarily time-varying and severe time-delay. Using new Lyapunov-Krasovskii functionals, less conservative stability conditions are obtained for such systems. The results are illustrated using the numerical examples based on simple linear matrix inequalities.

Fuzzy Sampled-Data Control for Uncertain Vehicle Suspension Systems

This paper investigates the problem of sampled-data H∞ control of uncertain active suspension systems via fuzzy control approach. Our work focuses on designing state-feedback and output-feedback sampled-data controllers to guarantee the resulting closed-loop dynamical systems to be asymptotically stable and satisfy H∞ disturbance attenuation level and suspension performance constraints. Using Takagi-Sugeno (T-S) fuzzy model control method, T-S fuzzy models are established for uncertain vehicle active suspension systems considering the desired suspension performances. Based on Lyapunov stability theory, the existence conditions of state-feedback and output-feedback sampled-data controllers are obtained by solving an optimization problem. Simulation results for active vehicle suspension systems with uncertainty are provided to demonstrate the effectiveness of the proposed method.

Magnetorheological fluid dampers: A review on structure design and analysis

Magnetorheological fluid technology has gained significant development during the past decades. The application of magnetorheological fluids has grown rapidly in civil engineering, safety engineering, transportation, and life science with the development of magnetorheological fluid–based devices, especially magnetorheological fluid dampers. The magnetorheological fluid dampers could offer an outstanding capability in semiactive vibration control due to excellent dynamical features such as fast response, environmentally robust characteristics, large force capacity, low power consumption, and simple interfaces between electronic input and mechanical output. To address the fast growing demand on magnetorheological fluid damping technology in extensive engineering practices, the state-of-the-art development is presented in this article, which provides a comprehensive review on the structure design and its analysis of magnetorheological fluid dampers (or systems). This can be regarded as a useful complement to several existing reviews in the recent literature on magnetorheological fluids technology, magnetorheological fluid applications, modeling of magnetorheological fluids and dampers, control strategies of magnetorheological fluid systems, and so on. The review begins with an introduction of the basic features and relevant applications of magnetorheological fluids. Then several basic structure design issues of magnetorheological fluid dampers are introduced. Following this, typical magnetorheological dampers are discussed according to the arrangement configurations of magnetorheological fluid cylinders and magnetorheological fluid control valves. Furthermore, reinforced structure designs of magnetorheological fluid dampers are provided, which focus on coil configuration, fluid resistance channel design, and electromagnetic design. Thereafter, design issues of magnetorheological fluid damper systems are discussed, which involves sensor-based magnetorheological fluid damper systems, self-powered magnetorheological fluid damper systems, fail-safe magnetorheological fluid damper systems, and integrated spring magnetorheological fluid damper systems. Importantly, to have a systematic quantitative viewpoint of the analysis and design of magnetorheological fluid dampers, the review ends with a summary of performance analysis issues, including performance specification, analytical modeling, parameter optimization, and so on.

Adaptive fuzzy control of uncertain stochastic nonlinear systems with unknown dead zone using small-gain approach

This paper considers the adaptive fuzzy robust control problem for a class of single-input and single-output (SISO) stochastic nonlinear systems in strict-feedback form. The systems under study possess unstructured uncertainties, unknown dead-zone, uncertain dynamics and unknown gain functions. In the controller design, fuzzy logic systems are adopted to approximate the unknown functions, and the uncertain nonlinear system is therefore transformed into an uncertain parameterized system with unmodeled dynamics. By combining the backstepping technique with the stochastic small-gain approach, a novel adaptive fuzzy robust control scheme is developed. It is shown that the proposed control approach can guarantee that the closed-loop system is input-state-practically stable (ISpS) in probability, and the output of the system converges to a small neighborhood of the origin by appropriately tuning several design parameters. Simulation results are provided to illustrate the effectiveness of the proposed control approach.

Fuzzy Tracking Control for Nonlinear Networked Systems

This paper studies the observer-based tracking control problem for discrete-time nonlinear networked control systems with parameter uncertainties and unmeasurable state variables. A network-induced constraint, i.e., the intermittent measurement loss, is considered in the controller design. The uncertain nonlinear system is described by an interval type-2 (IT2) fuzzy Takagi–Sugeno model, in which the lower and the upper membership functions with corresponding coefficients are used to capture and express uncertainties existing in the system. A premise-variables-independent IT2 fuzzy observer is constructed to estimate the unmeasurable state variables, and then a novel IT2 fuzzy tracking controller is designed. Furthermore, sufficient criteria are established to guarantee the resulting closed-loop system to be stochastically stable. Finally, two examples are provided to show the effectiveness of the proposed approach.This paper studies the observer-based tracking control problem for discrete-time nonlinear networked control systems with parameter uncertainties and unmeasurable state variables. A network-induced constraint, i.e., the intermittent measurement loss, is considered in the controller design. The uncertain nonlinear system is described by an interval type-2 (IT2) fuzzy Takagi-Sugeno model, in which the lower and the upper membership functions with corresponding coefficients are used to capture and express uncertainties existing in the system. A premise-variables-independent IT2 fuzzy observer is constructed to estimate the unmeasurable state variables, and then a novel IT2 fuzzy tracking controller is designed. Furthermore, sufficient criteria are established to guarantee the resulting closed-loop system to be stochastically stable. Finally, two examples are provided to show the effectiveness of the proposed approach.

Disturbance Observer-Based Adaptive Tracking Control With Actuator Saturation and Its Application

This paper is concerned with the problem of adaptive tracking control for a class of nonlinear systems with parametric uncertainty, bounded external disturbance, and actuator saturation. In order to achieve robust output tracking for the saturated uncertain nonlinear systems, a combination of adaptive robust control (ARC) and a novel terminal sliding-mode-based nonlinear disturbance observer (TSDO) is proposed, where the modeling inaccuracy and disturbance are integrated as a lumped disturbance. Specifically, the observer errors of estimating the lump disturbances converge to zero in finite-time for improving the precision of estimation. The estimated disturbances are then used in the controller to compensate for the systems lumped disturbances. The analytical results show that the proposed scheme is stable and can guarantee the asymptotic tracking with the tracking error converging to zero even in the presence of disturbances. Finally, the developed method is illustrated the effectiveness by the application to control of a quarter-car model with active suspension system.

Behavior dynamics based motion planning of mobile robots in uncertain dynamic environments

Abstract This paper provides a new approach to the dynamic motion planning problems of mobile robots in uncertain dynamic environments based on the behavior dynamics from a control point of view. The fundamental behavior of a mobile robot in motion planning is regarded as a dynamic process of the interaction between the robot and its local environment, and then it is modeled and controlled for the motion-planning purpose. Based on behavior dynamics, the dynamic motion-planning problem of mobile robots is transformed into a control problem of the integrated planning-and-control system. And the dynamic motion-planning problem can be transformed into a conventional optimization problem in the robots acceleration space. Realization of the collision-avoidance behavior is shown to be just a control problem of the robots acceleration. The proposed method can directly provide the desired acceleration for mobile robots. No restrictions are assumed on the shape and trajectories of obstacles. No local minima are encountered in most cases. Collision avoidance between multiple mobile robots can also be realized. Stability of the whole planning-and-control system can be guaranteed. Our method provides a new insight to the motion-planning problem of mobile robots based on behavior dynamics and from the control point of view. Simulation experiments illustrate our results.

The parametric characteristic of frequency response functions for nonlinear systems

The characteristic of the frequency response functions of nonlinear systems can be revealed and analyzed by analyzing of the parametric characteristics of these functions. To achieve these objectives, a new operator is defined, and several fundamental and important results about the parametric characteristics of the frequency response functions of nonlinear systems are developed. These theoretical results provide a significant and novel insight into the frequency domain characteristics of nonlinear systems and circumvent a large amount of complicated integral and symbolic calculations which have previously been required to perform nonlinear system frequency domain analysis. Several new results for the analysis and synthesis of nonlinear systems are also developed. Examples are included to illustrate potential applications of the new results.

An Optimal PID Control Algorithm for Training Feedforward Neural Networks

The training problem of feedforward neural networks (FNNs) is formulated into a proportional integral and derivative (PID) control problem of a linear discrete dynamic system in terms of the estimation error. The robust control approach greatly facilitates the analysis and design of robust learning algorithms for multiple-input-multiple-output (MIMO) FNNs using robust control methods. The drawbacks of some existing learning algorithms can therefore be revealed clearly, and an optimal robust PID-learning algorithm is developed. The optimal learning parameters can be found by utilizing linear matrix inequality optimization techniques. Theoretical analysis and examples including function approximation, system identification, exclusive-or (XOR) and encoder problems are provided to illustrate the results.

New bound characteristics of NARX model in the frequency domain

New results about the bound characteristics of both the generalized frequency response functions (GFRFs) and the output frequency response for the NARX (Non-linear AutoRegressive model with eXogenous input) model are established. It is shown that the magnitudes of the GFRFs and the system output spectrum can all be bounded by a polynomial function of the magnitude bound of the first order GFRF, and the coefficients of the polynomial are functions of the NARX model parameters. These new bound characteristics of the NARX model provide an important insight into the relationship between the model parameters and the magnitudes of the system frequency response functions, reveal the effect of the model parameters on the stability of the NARX model to a certain extent, and provide a useful technique for the magnitude based analysis of nonlinear systems in the frequency domain, for example, evaluation of the truncation error in a volterra series expression of non-linear systems and the highest order needed in the volterra series approximation. A numerical example is given to demonstrate the effectiveness of the theoretical results.