An Expert Adaptive Algorithm of PID Controller

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噪声有源反馈自校正控制

提出了一种前馈补偿和误差反馈相结合的噪声有源自校正控制方法,给出了两种未知参数的在线自适应学习算法,在控制系统中引入两个反馈闭环以改善系统的性能。仿真结果表明,前馈补偿和误差反馈相结合的噪声有源自校正控制比传统的Ｆｉｌｔｅｒ－Ｘ控制更为有效。     

单参数统一混沌系统的自适应控制同步

研究了单参数统一混沌系统的同步问题,提出了参数自适应控制同步方法;通过构造适当的控制函数和设计参数的自适应控制律,实现了两个不同参数的统一混沌系统的同步;根据Lyapunov稳定性定理,导出了两不同参数混沌系统能实现同步的充分条件,无论驱动系统处于何种状态,该方法都可使响应系统按照驱动系统给定的轨道演化。数值仿真表明了该方法的有效性。     

Stability analysis in an AVQ model of Intemet congestion control algorithm

This paper focuses on the local stability of a classical congestion control model in the Internet,namely adaptive virtual queue(AVQ) algorithm with feedback delay.Firstly,necessary and sufficient stability conditions in terms of key tuning parameters are given,which can provide exact guidelines for setting system parameters.Furthermore,by computing the rightmost characteristic root,the optimal parameter configuration for AVQ is derived,which can guarantee superior stability performance.Finally,some simulation examples are given to illustrate the correctness of the theoretical analysis.     

Automated tuning of a closed-loop hand grasp neuroprosthesis

An automated tuning algorithm was developed to reduce the time and skill required to tune a closed-loop hand grasp neuroprosthesis. The time reduction results from simultaneous tuning of four gain parameters controlling the dynamic response of the system, and from automation of the calculation and decision processes. The method is therefore an automated parallel tuning method, replacing a manual sequential method in which only one parameter at a time was tuned. RMS error between the step input and the grasp output is minimized, with absence of oscillation as a constraint. The difference between the system's RMS ramp tracking errors for the two tuning methods was less than 1% of the ramp size regardless of the initial values of the parameters, implying that the tuning methods were equivalent. However, the parallel tuning method was faster and required fewer trials than the sequential method. The capability of the closed-loop system to regulate grasp output in the presence of disturbances was shown to be better than the capability without feedback     

一种协同神经网络不平衡参数的差分优化方法

协同模式识别方法是协同学原理应用于模式识别领域的一种新方法,识别过程中通过调整神经网络的注意参数,能改进系统的识别性能,对协同神经网络参数在不平衡条件下进行优化,能充分利用协同神经网络的自学习能力,以提高识别效果。差分进化作为一种有效的全局近似最优解的搜索算法,具有收敛性好、速度快的特点,文中提出了一种基于差分进化的方法对协同神经网络中参数进行优化,在协同神经网络的参数空间搜索最优参数,采用了均方适应度方差的机制自适应调整搜索速度和搜索精度,克服差分进化算法参数调整困难的不足,以提高算法的寻优能力,新方法具有全局兼局部寻优能力,不易陷入局部极值,同时新方法采用约简的序参量进化参数,使优化算法能有效提高协同神经网结的效率,实际图像的分类识别结果表明,注意参数的变化会导致完全不同的识别结果,另外,本文还将新算法与平衡参数的方法、其它优化的非平衡参数的协同学习算法进行了全局优化能力的比较,采用新方法具有更快的收敛速度和更优的分类识别效果。      

A New Neuro-Fuzzy Adaptive Genetic Algorithm

Novel neuro-fuzzy techniques are used to dynamically control parameter settings ofgenetic algorithms (GAs).The benchmark routine is an adaptive genetic algorithm (AGA) that uses afuzzy knowledge-based system to control GA parameters.The self-learning ability of the cerebellar modelariculation controller (CMAC) neural network makes it possible for on-line learning the knowledge onGAs throughout the run.Automatically designing and tuning the fuzzy knowledge-base system,neuro-fuzzy techniques based on CMAC can find the optimized fuzzy system for AGA by the renhanced learningmethod.The Results from initial experiments show a Dynamic Parametric AGA system designed by theproposed automatic method and indicate the general applicability of the neuro-fuzzy AGA to a widerange of combinatorial optimization.     

Adaptive Backstepping Control Using Combined Direct and Indirect Adaptation

A new adaptation mechanism is proposed for the tuning functions based adaptive backstepping control for a class of nonlinear systems. The new scheme combines direct and indirect parameter update regimes in an effort to improve the speed and accuracy of the adaptation which in turn leads to increased tracking performance. The parameter update dynamics is driven both by the identification error and the tracking error defined in the backstepping control. The closed loop error dynamics is shown to be globally stable by using a recursive mechanism based on Lyapunov analysis with tracking error convergence along with identification error convergence. The proposed scheme is tested on two benchmark examples in order to demonstrate its performance increase compared to the tuning functions based adaptive backstepping scheme.     

An evidence-theoretic k-NN rule with parameter optimization

The paper presents a learning procedure for optimizing the parameters in the evidence-theoretic k-nearest neighbor rule, a pattern classification method based on the Dempster-Shafer theory of belief functions. In this approach, each neighbor of a pattern to be classified is considered as an item of evidence supporting certain hypotheses concerning the class membership of that pattern. Based on this evidence, basic belief masses are assigned to each subset of the set of classes. Such masses are obtained for each of the k-nearest neighbors of the pattern under consideration and aggregated using Dempster's rule of combination. In many situations, this method was found experimentally to yield lower error rates than other methods using the same information. However, the problem of tuning the parameters of the classification rule was so far unresolved. The authors determine optimal or near-optimal parameter values from the data by minimizing an error function. This refinement of the original method is shown experimentally to result in substantial improvement of classification accuracy     

全闭环同步带传动系统的建模与控制方法研究

同步带传动是一种常用的传动机构。在高速高精度设备中，其动态性能直接影响到设备的精度和效率。对同步带系统进行建模，对电机模型、同步带传动模型和控制模型进行耦合分析并采用Simulink仿真系统中电流、速度以及位移全闭环的PD参数整定过程，获得了良好的动静态特性和最优控制参数。通过试验表明，运用参数整定仿真方法可高效优化实际控制对象的PD参数。并有效缩短调试时间。     

A simple direct-torque neuro-fuzzy control of PWM-inverter-fedinduction motor drive

In this paper, the concept and implementation of a new simple direct-torque neuro-fuzzy control (DTNFC) scheme for pulsewidth-modulation-inverter-fed induction motor drive are presented. An adaptive neuro-fuzzy inference system is applied to achieve high-performance decoupled flux and torque control. The theoretical principle and tuning procedure of this method are discussed. A 3 kW induction motor experimental system with digital signal processor TMS 320C31-based controller has been built to verify this approach. The simulation and laboratory experimental results, which illustrate the performance of the proposed scheme, are presented. Also, nomograms for controller design are given. It has been shown that the simple DTNFC is characterized by very fast torque and flux response, very-low-speed operation, and simple tuning capability     

Strained-Si Channel Super-Self-Aligned Back-Gate/Double-Gate Planar Transistors

We present a reproducible approach to the fabrication of super-self-aligned back-gate/double-gate n-channel and p-channel transistors with thin silicon channels and thick source/drain polysilicon regions. The device structure provides capability for scalable control of channel electrostatics, threshold variability without sacrificing source/drain series resistance, and capability of introducing strain to improve carrier transport. The separate device, circuit, and functional level back-gate access that is available through bottom interconnection also provides capability for adaptive power control and novel circuit design. Both n-channel and p-channel devices are demonstrated with the threshold tuning capability     

模糊控制在液压闭环同步系统中的应用研究

电液比例阀控液压双缸同步系统具有典型的非线性和不确定性,传统控制方法难以达到双缸同步精度要求。在传统控制方式基础上加入模糊控制器以实现控制系统参数的在线调整,可提高系统自适应能力。建立基于模糊控制算法的主从液压双缸同步模型,Matlab仿真结果表明,基于模糊算法的主从同步方式减小了双缸同步位移差波动范围,并具有较好的抗干扰能力和稳定性。     

Frequency-selective integrated circuits using phase-lock techniques

A system-oriented approach to the design of inductorless tuned integrated circuits is described. This design method uses the phase-locked loop (PLL) techniques to obtain the desired tuning and interference-rejection characteristics. The PLL approach does not require tight control of component tolerances, and offers a higher selectivity and frequency capability than the corresponding active-RC synthesis methods. In this paper, basic design parameters for phase-locked integrated circuits are given, and two separate design examples are described. First is a high- frequency (1 to 25 MHz) FM amplifier/detector, which forms a monolithic replacement for the IF strip and the detector sections of a conventional FM receiver or TV sound system. The second is an integrated FM multiplex receiver for multi-channel telemetry applications, which has the selectivity of a 6-pole bandpass filter and can be tuned by means of an external resistor or capacitor from a fraction of a cycle to over 300 kHz.     

一种基于前馈补偿的PID轨压控制设计方法

分析了5种共轨工况下轨压控制的需求,采用开环和闭环两种控制方法。针对轨压闭环控制的特点,设计了基于前馈的PID控制算法。为了提高PID控制精度,对参数设计方法进行了优化,并通过试验测试了两种工况下的轨压控制效果,轨压控制偏差在1.7%以内。     

Structural Bayesian Linear Regression for Hidden Markov Models

Linear regression for Hidden Markov Model (HMM) parameters is widely used for the adaptive training of time series pattern analysis especially for speech processing. The regression parameters are usually shared among sets of Gaussians in HMMs where the Gaussian clusters are represented by a tree. This paper realizes a fully Bayesian treatment of linear regression for HMMs considering this regression tree structure by using variational techniques. This paper analytically derives the variational lower bound of the marginalized log-likelihood of the linear regression. By using the variational lower bound as an objective function, we can algorithmically optimize the tree structure and hyper-parameters of the linear regression rather than heuristically tweaking them as tuning parameters. Experiments on large vocabulary continuous speech recognition confirm the generalizability of the proposed approach, especially when the amount of adaptation data is limited.     

带有未知死区的机器人积分变结构模糊控制

针对一类带有未知死区和外来干扰的不确定机器人系统,基于变结构控制原理,采用简化的死区输入模型,利用具有线性可调参数的模糊系统的逼近能力,提出了一种积分变结构自适应模糊控制方案.该方案取消了要求逼近误差平方可积的条件,同时利用Young's不等式减少了模糊系统调节参数的数目,从而降低了实现的复杂性.最后,利用Lyapun...     

Intelligent alignment of waveguide filters using a machine learningapproach

The authors investigate the application of a machine learning system to the tuning of waveguide filters. This system uses techniques from pattern recognition and adaptive signal processing. The manual tuning of the waveguide filters is very time consuming and expensive and a skilled operator is required. Here, the machine learning system is adapted in such a way that it can assist an unskilled operator to perform fast and accurate tuning of these filters. The machine learning approach is based on the manipulation of some raw data to extract a set of salient features that have strong significance in the behavior of the filters. These features are derived visually, by comparing the characteristics of a tuned filter to those of a faulty filter with known levels of maladjustments     

不确定分数阶多驱动一响应混沌系统同步

针对一类新的多驱动一响应混沌系统同步方式,基于分数阶系统稳定性理论和Lyapunov稳定性理论,运用追踪控制和滑模自适应控制方法设计了同步控制律和参数自适应律.对象模型考虑了不确定因素的影响,首先选取一类稳定的分数阶滑模曲面,然后提出了一种鲁棒同步方案.最后数值仿真验证了方案的正确性和有效性.     

液压伺服系统自适应模糊变结构控制

结合变结构控制、自适应控制和模糊技术等特点,提出一种自适应模糊变结构控制方法.首先,设计一个带积分开关平面函数的变结构控制器,并构造一个二维模糊边界层宽度调节器以削弱抖振.其次,基于Lyapunov稳定性理论,引入一自适应算法,自适应调节变结构控制参数.应用于液压伺服系统的控制实验结果表明,所提出的控制方法能削弱抖振,改善液压伺服系统稳态控制精度,具有较强的鲁棒自适应综合性能.