声光双稳系统的自控制反馈耦合驱动混沌同步

首先从理论上提出自控制反馈耦合驱动混沌同步化方案，数值地分析了双Ｂｒａｇｇ型声光双稳系统混沌输出同步化条件，使用最大条件Ｌｙａｐｕｎｏｖ指数作为同步化判据．发现通过适当比例的耦合驱动可以使两组混沌系统达到同步的混沌输出，引入自控制反馈可以加速达到同步化的速度并减小所需的最小耦合强度，在噪声的影响下同样可以实现混沌的同步．

BIO-INSPIRED SELF-ADAPTIVE MANUFACTURING SYSTEM CONTROL ARCHITECTURE

Future manufacturing systems need to cope with frequent changes and disturbances, therefore their control architectures require constant adaptability, agility, stability, self-organization, intelligence, and robustness. Bio-inspired manufacturing system can well satisfy these requirements. For this purpose, by referencing the biological organization structure and the mechanism, a bio-inspired manufacturing cell is presented from a novel view, and then a bio-inspired self-adaptive manufacturing model is established based on the ultra-short feedback mechanism of the neuro-endocrine system. A hio-inspired self-adaptive manufacturing system coordinated model is also established based on the neuro-endocrine-immunity system （NEIS）. Finally, an example based on pheromone communication mechanism indicates that the robustness of the whole manufacturing system is improved by bio-inspired technologies.

Improved Smith prediction monitoring AGC system based on feedback-assisted iterative learning control

The performance of Smith prediction monitoring automatic gauge control(AGC) system is influenced by model mismatching greatly in strip rolling process. Aiming at this problem, a feedback-assisted iterative learning control strategy, which learned unknown modeling error by using previous control information repeatedly, was introduced into Smith prediction monitoring AGC system. Firstly, conventional Smith predictor and improved Smith predictor with PI-P controller were analyzed. Secondly, on the basis of establishing of feedback-assisted iterative learning control strategy for improved Smith predictor, process control signal update law and control error were deduced, then convergence condition of this strategy was put forward and proved. Finally, after modeling the automatic position control system, the PI-P Smith prediction monitoring AGC system with feedback-assisted iterative learning control was researched through simulation. Simulation results indicate that this system remains stable during model mismatching. The robustness and response of monitoring AGC is improved by development of feedback-assisted iterative learning control strategy for PI-P Smith predictor.

Control of a Unified Chaotic System via Single Variable Feedback

Based on the LaSalle invariance principle, we propose a simple adaptive-feedback for controlling the unified chaotic system. We show explicitly with numerical proofs that our method can easily achieve the control of chaos in the unified chaotic system using only a single variable feedback. The present controller, to our knowledge, is the simplest control scheme for controlling a unified chaotic system.

Robust adaptive control for a class of uncertain non-affine nonlinear systems using neural state feedback compensation

A robust adaptive control is proposed for a class of uncertain nonlinear non-affine SISO systems. In order to approximate the unknown nonlinear function, an affine type neural network(ATNN) and neural state feedback compensation are used, and then to compensate the approximation error and external disturbance, a robust control term is employed. By Lyapunov stability analysis for the closed-loop system, it is proven that tracking errors asymptotically converge to zero. Moreover, an observer is designed to estimate the system states because all the states may not be available for measurements. Furthermore, the adaptation laws of neural networks and the robust controller are given based on the Lyapunov stability theory. Finally, two simulation examples are presented to demonstrate the effectiveness of the proposed control method. Finally, two simulation examples show that the proposed method exhibits strong robustness, fast response and small tracking error, even for the non-affine nonlinear system with external disturbance, which confirms the effectiveness of the proposed approach.

A new adaptive bending method using closed loop feedback control

As the competition from companies in low cost countries increases,the need for more automated production which reduces labour cost while improving product quality is required.A new rotary compression bending set-up with automated closed-loop feedback control is thus being developed.By transferring in-process measurement data into an algorithm for predicting springback and bend angle prior to the unloading sequence,the dimensional accuracy is improved.This work focuses on the development of this steering model.Since the method used does not increase cycle time,it is attractive for high-volume industrial applications.More than 150 bending tests of AA6060 extrusions were conducted to determine the capability of the technology.The results show that by activating the automated closed-loop feedback system,the dimensional accuracy of the bent parts is more than three times better than that obtained by traditional compression bending.Since the steering model permits the direct use of additional process data,such as instant wall thickness and cross sectional distortions,it is believed that extension of the measurement capabilities would improve the accuracy of the methodology even further.

Adaptive output feedback control for uncertain nonholonomic chained systems

An adaptive output feedback control was proposed to deal with a class of nonholonomic systems in chained form with strong nonlinear disturbances and drift terms. The objective was to design adaptive nonlinear output feedback laws such that the closed-loop systems were globally asymptotically stable, while the estimated parameters remained bounded. The proposed systematic strategy combined input-state-scaling with backstepping technique. The adaptive output feedback controller was designed for a general case of uncertain chained system. Furthermore, one special case was considered. Simulation results demonstrate the effectiveness of the proposed controllers.

Global Robust and Adaptive Output Feedback Dynamic Positioning of Surface Ships

A constructive method was presented to design a global robust and adaptive output feedback controller for dynamic positioning of surface ships under environmental disturbances induced by waves, wind, and ocean currents. The ship's parameters were not required to be known. An adaptive observer was first designed to estimate the ship's velocities and parameters. The ship position measurements were also passed through the adaptive observer to reduce high frequency measurement noise from entering the control system. Using these estimate signals, the control was then designed based on Lyapunov's direct method to force the ship's position and orientation to globally asymptotically converge to desired values. Simulation results illustrate the effectiveness of the proposed control system. In conclusion, the paper presented a new method to design an effective control system for dynamic positioning of surface ships.

Dynamical output feedback stabilization for neutral systems with mixed delays

This paper is concerned with the issue of stabilization for the linear neutral systems with mixed delays. The attention is focused on the design of output feedback controllers which guarantee the asymptotical stability of the closed-loop systems. Based on the model transformation of neutral type, the Lyapunov-Krasovskii functional method is employed to establish the delay-dependent stability criterion. Then, through the controller parameterization and some matrix transformation techniques, the desired parameters are determined under the delay-dependent design condition in terms of linear matrix inequalities (LMIs), and the desired controller is explicitly formulated. A numerical example is given to illustrate the effectiveness of the proposed method.

Suppression of Spiral Waves and Spatiotemporal Chaos Under Local Self-adaptive Coupling Interactions

In this paper, a close-loop feedback control is imposed locally on the Fitzhugh-Nagumo （FHN） system to suppress the stable spirals and spatiotemporal chaos according to the principle of self-adaptive coupling interaction. The simulation results show that an expanding target wave is stimulated by the spiral waves under dynamic control period when a local area. of 5 × 5 grids is controlled, or the spiral tip is driven to the board of the system, It is also found that the spatiotemporal chaos can be suppressed to get a stable homogeneous state within 50 time units as two local grids are controlled mutually. The mechanism of the scheme is briefly discussed.

Automatic Stabilization of an Adaptive Feedback Control for Noise Cancelling In-ear Headphones

Headphones with an integrated active noise cancellation system have been increasingly introduced to the consumer market in recent years. When exposing the human ear to active noise sources in this striking distance, the ensuring of a safe sound pressure level is vital. In feedback systems, this is coupled with the stability of the closed control loop; stable controller design is thus essential. However, changes in the control path during run-time can cause the stable control system to become unstable, resulting in an overdrive of the speakers in the headphones. This paper proposes a method, which enables the real-time analysis of the current system state and if necessary stabilizes the closed loop while maintaining the active noise reduction. This is achieved by estimating and evaluating the open loop behavior with an adaptive filter and subsequently limiting the controller gain in respect to the stability margin.

STATE-FEEDBACK ADAPTIVE STABILIZING CONTROL DESIGN FOR A CLASS OF HIGH-ORDER NONLINEAR SYSTEMS WITH UNKNOWN CONTROL COEFFICIENTS

In this paper, a new approach is successfully addressed to design the state-feedback adaptive stabilizing control law for a class of high-order nonlinear systems in triangular form and with unknown and nonidentical control coefficients, whose stabilizing control has been investigated recently under the knowledge that the lower bounds of the control coefficients are exactly known. In the present paper, without any knowledge of the lower bounds of the control coefficients, based on the adaptive technique and appropriately choosing design parameters, we give the recursive design procedure of the stabilizing control law by utilizing the approach of adding a power integrator together with tuning functions. The state-feedback adaptive control law designed not only preserves the equilibrium at the origin, but also guarantees the global asymptotic stability of the closed-loop states and the uniform boundedness of all the other closed-loop signals.

STABILIZATION FOR A CLASS OF LARGE-SCALE STOCHASTIC NONLINEAR SYSTEMS WITH DECENTRALIZED CONTROLLER DESIGN

In this paper, a state feedback adaptive stabilization for a class of large-scale stochastic nonlinear systems is designed with Lyapunov and Backstepping method. In the systems there are uncertain terms, whose bounds are governed by a set of unknown parameters. The designed controllers would make the close-loop systems asymptotically stable and adaptive for the unknown parameters. As an application, a second order example is delivered to illustrate the approach.

Control strategy for gait transition of an underactuated 3D bipedal robot

Significant research interest has recently been attracted to the study of bipedal robots due to the wide variety of their potential applications.In reality,bipedal robots are often required to perform gait transitions to achieve flexible walking.In this paper,we consider the gait transition of a five-link underactuated three-dimensional(3 D)bipedal robot,and propose a two-layer control strategy.The strategy consists of a unique,event-based,feedback controller whose feedback gain in each step is updated by an adaptive control law,and a transition controller that guides the robot from the current gait to a neighboring point of the target gait so that the state trajectory can smoothly converge to the target gait.Compared with previous works,the transition controller is parameterized and its control parameters are obtained by solving an optimization problem to guarantee the physical constraints in the transition process.Finally,the effectiveness of the control strategy is illustrated on the underactuated 3 D bipedal robot.

Lag Synchronization Between Two Coupled Networks via Open-Plus-Closed-Loop and Adaptive Controls

In this paper,we study lag synchronization between two coupled networks and apply two types of control schemes,including the open-plus-closed-loop(OPCL) and adaptive controls.We then design the corresponding control algorithms according to the OPCL and adaptive feedback schemes.With the designed controllers,we obtain two theorems on the lag synchronization based on Lyapunov stability theory and Barbalat's lemma.Finally we provide numerical examples to show the effectiveness of the obtained controllers and see that the adaptive control is stronger than the OPCL control when realizing the lag synchronization between two coupled networks with different coupling structures.

Adaptive Stabilization for a Class of Feedforward Systems with Zero-Dynamics

This paper investigates adaptive state feedback stabilization for a class of feedforward nonlinear systems with zero-dynamics, unknown linear growth rate and control coefficient. For design convenience, the state transformation is first introduced and the new system is obtained. Then, the estimation law is constructed for the unknown control coefficient, and the state feedback controller is proposed with a gain updated on-line. By appropriate choice of the estimation law for the control coefficient and the dynamic gain, the states of the closed-loop system are globally bounded, and the state of the original system converges to zero. Finally, a simulation example is given to illustrate the correctness of the theoretical results.