柔性机械臂在自适应自动化系统中的性能分析与优化策略

文章探讨了柔性机械臂在自适应自动化系统中的性能分析与优化。首先介绍了柔性机械臂的基本原理、结构和在自适应系统中的重要性。随后深入分析了柔性机械臂在自适应系统中的工作性能,并提出了性能优化的策略。还讨论了柔性机械臂在自适应系统中的集成方法和技术,以及在不同领域的应用前景,旨在为柔性机械臂在自适应自动化系统中的应用提供理论支持和实践指导。

自适应办公自动化系统的设计与实现

提供给用户一系列自定义工具,构建了一个能全方位自动适应应用环境变化的OA系统。通过在数据库中建立并管理映射表,同时结合控件动态生成技术,在较大限度上解决了因系统需求变更导致二次开发的可能性。

谈自适应办公自动化系统的设计与实现

传统的OA系统功能固定,很难适应新的变化.近年来结合面向对象技术和软件重用的思想,有些系统已经在做相应的尝试,逐步为某些模块增添更多的灵活性,比如办公流程的动态调整、经办人员的任意设定等.但对于整个系统,却缺少包括业务、流程、人员和输出在内的一体化解决方案.

基于CAS理论的指挥自动化系统

分析了指挥自动化系统的复杂性,提出了应用自适应复杂系统理论的基于智能主体的自适应指挥自动化系统的体系结构,并对多主体系统的组织结构进行了分析和设计。

湖北电网首套智能稳定控制装置

介绍了湖北电网内首套投入运行的智能化稳定控制装置-汉川电厂B厂稳定控制装置的原理与实现,该装置的应用标示了湖北电网稳定控制的执行环节由人工对装置进行投退发展到智能化自动执行的突变,是以后湖北电网实现集中分散、分层分区稳定控制的基础。

Robust adaptive dynamic surface control for nonlinear uncertain systems

We propose a new method for robust adaptive backstepping control of nonlinear systems with parametric uncertainties and disturbances in the strict feedback form. The method is called dynamic surface control. Traditional backstepping algorithms require repeated differentiations of the modelled nonlinearities. The addition of n first order low pass filters allows the algorithm to be implemented without differentiating any model nonlinearities, thus ending the complexity arising due to the 'explosion of terms' that makes other methods difficult to implement in practice. The combined robust adaptive backstepping/first order filter system is proved to be semiglobally asymptotically stable for sufficiently fast filters by a singular perturbation approach. The simulation results demonstrate the feasibility and effectiveness of the controller designed by the method.

Two Adaptive Control Algorithms in Semi-Active Suspension System

To reduce the vibration in the suspension, semi active suspension system was employed. And its CARMA model was built. Two adaptive control schemes, the minimum variance self tuning control algorithm and the pole configuration self tuning control algorithm, were proposed. The former can make the variance of the output minimum while the latter can make dynamic behavior satisfying. The stability of the two schemes was analyzed. Simulations of them show that the acceleration in the vertical direction has been reduced greatly. The purpose of reducing vibration is realized. The two schemes can reduce the vibration in the suspension and have some practicability.

Adaptive terminal sliding mode control for high-order nonlinear dynamic systems

An adaptive terminal sliding mode control (SMC) technique is proposed to deal with the tracking problem for a class of high-order nonlinear dynamic systems. It is shown that a function augmented sliding hyperplane can be used to develop a new terminal sliding mode for high-order nonlinear systems. A terminal SMC controller based on Lyapunov theory is designed to force the state variables of the closed-loop system to reach and remain on the terminal sliding mode, so that the output tracking error then converges to zero in finite time which can be set arbitrarily. An adaptive mechanism is introduced to estimate the unknown parameters of the upper bounds of system uncertainties. The estimates are then used as controller parameters so that the effects of uncertain dynamics can be eliminated. It is also shown that the stability of the closed-loop system can be guaranteed with the proposed control strategy. The simulation of a numerical example is provided to show the effectiveness of the new method.

Development of the Embedded Protective Device Based on ARM

An embedded protective device for 35kV power line is worked out based on Philips’ LPC2292 ARM MCU. Several aspects such as embedded design technique adopted in the system framework, application of adaptive theory in data acquisition, Board Support Packet (BSP) developing and task dispatching related to operating system are discussed. Both hardware and software framework of the system are given. Advanced hardware platform and software development environment is applied in design of the system, with the advanced co-design technology.

Neuron PI control for semi-active suspension system of tracked vehicle

A neuron proportion integration (PI) control strategy for semi-active suspension system of tracked vehicle was proposed based on its unique structure and the multiple and complex environment of the driving traffic. An adaptive genetic algorithm is used to optimize the parameters of the neuron PI controller. The simulation result of the neuron PI control for semi-active suspension system of tracked vehicle indicates that the vertical amplitude,pitch angle and vertical acceleration of the vehicle are well controlled. The root mean square (RMS) of the vertical amplitude decreases by 37.2%,and 45.2% for the pitch angle,38.6% for the vertical acceleration. The research of neuron PI control experiment for the semi-active suspension system of the tracked vehicle model mining in benthal indicates that the RMS of the weight acceleration vibrating along the vertical direction decreases by 29.5%,the power spectral density resonance peak of the acceleration of the car body decreases by 23.8%.

Adaptive Control of Active Balancing System for a Fast Speed-varying Jeffcott Rotor with Actuator Time Delay

Due to actuator time delay existing in an adaptive control of the active balancing system for a fast speed-varying Jeffcott rotor, if an unsynchronized control force (correction imbalance) is applied to the system, it may lead to degradation in control efficiency and instability of the control system. In order to avoid these shortcomings, a simple adaptive controller was designed for a strictly positive real rotor system with actuator time delay, then a Lyapunov-Krasovskii functional was constructed after an appropriate transform of this sys-tem model, the stability conditions of this adaptive control system with actuator time delay were derived. After adding a filter function, the active balancing system for the fast speed-varying Jeffcott rotor with actuator time delay can easily be converted to a strictly positive real system, and thus it can use the above adaptive controller satisfying the stability conditions. Finally, numerical simulations show that the adaptive controller proposed works very well to perform the active balancing for the fast speed-varying Jeffcott rotor with actuator time delay.

Design of the multirobot intelligent team formation's control architecture and performance analysis of the system

The layered control architecture is designed for the need of the multirobot intelligent team formation.There are three levels:the cooperation task level,the coordination behavior level and the action planning level.The cooperation task level uses the potential grid method,which improves the safety of the path and reduces the calculation complexity.The coordination behavior level uses the reinforcement learning which can strengthen the robots’ intelligence.The action planning level uses the fuzzy planning methods to realize the action matching.The communication model transfers the message in different level.This architecture shows not only the independence and the intelligence of the single robot but also the cooperation and the coordination among the robots.In each level,the task is distributed reasonably and clearly.Finally the feasibility of the architecture is verified further in the simulation of the experiment.The expansibility of the architecture is good and the architecture can be used in the similar system.

A Novel Stochastic Blind Adaptive Multiuser Detector for CDMA Systems

Blind adaptive multiuser detector has become a research hotspot in recent years due to a number of advantages, but many blind adaptive algorithms involve low convergence rate. This paper presents a novel stochastic blind adaptive multiuser detector without requiring training sequences, which needs only two system parameters: the signature sequence of the desired user i, s i and the variance of the additive white Gaussian noise (AWGN),σ 2. Simulation results show that by reasonably choosing time varying step size, the proposed detector can not only improve the convergence rate, but also reduce the limiting NSE (Normalized Squared Error) values, so it can effectively increase the performance of the system.

Hybrid denoising-jittering data processing approach to enhance sediment load prediction of muddy rivers

Successful modeling of hydroenvironmental processes widely relies on quantity and quality of accessible data,and noisy data can affect the modeling performance.On the other hand in training phase of any Artificial Intelligence(AI) based model,each training data set is usually a limited sample of possible patterns of the process and hence,might not show the behavior of whole population.Accordingly,in the present paper,wavelet-based denoising method was used to smooth hydrological time series.Thereafter,small normally distributed noises with the mean of zero and various standard deviations were generated and added to the smooth time series to form different denoised-jittered data sets.Finally,the obtained pre-processed data were imposed into Artificial Neural Network(ANN) and Adaptive Neuro-Fuzzy Inference System(ANFIS)models for daily runoff-sediment modeling of the Minnesota River.To evaluate the modeling performance,the outcomes were compared with results of multi linear regression(MLR) and Auto Regressive Integrated Moving Average(ARIMA)models.The comparison showed that the proposed data processing approach which serves both denoising and jittering techniques could enhance the performance of ANN and ANFIS based runoffsediment modeling of the case study up to 34%and 25%in the verification phase,respectively.

ADAPTIVE SYSTEMS THEORY: SOME BASIC CONCEPTS, METHODS AND RESULTS

The adaptive systems theory to be presented in this paper consists of two closely related parts: adaptive estimation (or filtering, prediction) and adaptive control of dynamical systems. Both adaptive estimation and control are nonlinear mappings of the on-line observed signals of dynamical systems, where the main features are the uncertain-ties in both the system's structure and external disturbances, and the non-stationarity and dependency of the system signals. Thus, a key difficulty in establishing a mathematical theory of adaptive systems lies in how to deal with complicated nonlinear stochastic dynamical systems which describe the adaptation processes. In this paper, we will illustrate some of the basic concepts, methods and results through some simple examples. The following fundamental questions will be discussed: How much information is needed for estimation? How to deal with uncertainty by adaptation? How to analyze an adaptive system? What are the convergence or tracking performances of adaptation? How to find the proper rate of adaptation in some sense? We will also explore the following more fundamental questions: How much uncertainty can be dealt with by adaptation ? What are the limitations of adaptation ? How does the performance of adaptation depend on the prior information ? We will partially answer these questions by finding some 'critical values' and establishing some 'Impossibility Theorems' for the capability of adaptation, for several basic classes of nonlinear dynamical control systems with either parametric or nonparametric uncertainties.

Robust adaptive cross-coupling position control of biaxial motion system

The stability and synchronous performance are usually hard to be improved simultaneously in the biaxial cross-coupling position motion control system.Based on analyzing the characteristics of the cross-coupling control system,a robust adaptive cross-coupling control strategy is proposed.To restrict influences of destabilizing factors and improve both of stability and synchronous performance,the strategy forces dual axes to track the same reference model using Narendra adaptive control theory.And then,a robust parameters adaptive law is proposed.The stability analysis of the proposed strategy is conducted by applying Lyapunov stability theory.Related simulations and experiments indicate that the proposed strategy can improve synchronous performance and stability simultaneously.

Dynamic PMD compensator for 40 Gb/s PM-DQPSK system

The adaptive polarization mode dispersion(PMD) compensation in high-speed transmission system has become more and more necessary for the link PMD causing strong signal distortions.A dynamic adaptive PMD compensator in 40 Gb/s polar-multiplex differential quadrature phase shift keying(PM-DQPSK) system is reported.Experimental results show that the PMD compensator can track the average polarization state variation at 65 rad/s without any lost of the optimum tracking.The 1st-order PMD compensation is demonstrated experimentally,and the compensator can increase the maximal tolerable PMD value by 26 ps from 17 ps to 43 ps in an optical transmission system.

From foraging to autonoetic consciousness： The primal self as a consequence of embodied prospective foraging

The capacity to adapt to resource distributions by modulating the frequency of exploratory and exploitative behaviors is common across metazoans and is arguably a principal selective force in the evolution of cognition. Here we （I） review recent work investigating behavioral and biological commonalities between external foraging in space and internal foraging over envi- ronments specified by cognitive representations, and （2） explore the implications of these commonalities for understanding the origins of the self. Behavioural commonalities include the capacity for what is known as area-restricted search in the ecological literature： this is search focussed around locations where resources have been found in the past, but moving away from locations where few resources arc found, and capable of producing movement pattems mimicking L6vy flights. Area-restricted search shares a neural basis across metazoans, and these biological commonalities in vertebrates suggest an evolutionary homology be- tween external and internal foraging. Internal foraging, and in particular a form we call embodied prospective foraging, makes available additional capacities for prediction based on search through a cognitive representation of the external environment, and allows predictions about outcomes of possible future actions. We demonstrate that cognitive systems that use embodied prospec- tive foraging require a primitive sense of self, needed to distinguish actual from simulated action. This relationship has implica- tions for understanding the evolution of autonoetic consciousness and self-awareness.

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.