Real-time parallel adaptive neural network control for nonlinear servomechanisms—an approach using direct adaptive techniques

In this paper, a parallel adaptive neural network control system applicable to nonlinear dynamical systems of the type commonly encountered in many practical position control servomechanisms is developed. The controller is based on the use of direct adaptive techniques and an approach of using an additional parallel neural network to provide adaptive enhancements to a basic fixed neural network-based nonlinear controller. Properties of the proposed new controller are discussed in the paper and it is shown that if Gaussian radial basis function networks are used for the additional parallel neural network, uniformly stable adaptation is assured and asymptotic tracking of the position reference signal is achieved. The effectiveness of the proposed adaptive neural network control system is demonstrated in real-time implementation experiments for position control in a servomechanism with asymmetrical loading and changes in the load.     

A simple alternative to neural network control scheme for robotmanipulators

Recent research results have shown that neural network techniques are effective in compensating highly nonlinear uncertainties in the robot model where computed torque method is used for robot motion control. One excellent work was reported by Ishiguro et. al. (1992). The purpose of this note is to present a simple alternate solution to the same control problem which does not require the use of a neural network. The solution is based on the disturbance rejection technique developed by Hsia (1989-92). Computer simulations show that the alternate control method works better     

Robust fuzzy neural network sliding mode control scheme for IPMSM drives

This article proposes a robust fuzzy neural network sliding mode control (FNNSMC) law for interior permanent magnet synchronous motor (IPMSM) drives. The proposed control strategy not only guarantees accurate and fast command speed tracking but also it ensures the robustness to system uncertainties and sudden speed and load changes. The proposed speed controller encompasses three control terms: a decoupling control term which compensates for nonlinear coupling factors using nominal parameters, a fuzzy neural network (FNN) control term which approximates the ideal control components and a sliding mode control (SMC) term which is proposed to compensate for the errors of that approximation. Next, an online FNN training methodology, which is developed using the Lyapunov stability theorem and the gradient descent method, is proposed to enhance the learning capability of the FNN. Moreover, the maximum torque per ampere (MTPA) control is incorporated to maximise the torque generation in the constant torque region and increase the efficiency of the IPMSM drives. To verify the effectiveness of the proposed robust FNNSMC, simulations and experiments are performed by using MATLAB/Simulink platform and a TI TMS320F28335 DSP on a prototype IPMSM drive setup, respectively. Finally, the simulated and experimental results indicate that the proposed design scheme can achieve much better control performances (e.g. more rapid transient response and smaller steady-state error) when compared to the conventional SMC method, especially in the case that there exist system uncertainties.     

自适应神经网络的光电跟踪成像消旋控制系统

为解决机载光电跟踪系统在跟踪过程中由于工作平台框架的转动导致成像画面的旋转问题,提出了一种基于自适应神经网络的消旋控制方法。系统以消旋指令角作为给定位置信息,以光电编码器实测角度值前后两拍之差作为实测速度值,组成速度反馈内环;以陀螺仪测得的角度值作为位置反馈值,构成位置外环;校正算法采用二阶超前-滞后校正并加入了自适应神经网络算法对其控制参数进行自适应调整。实验结果表明,在消旋拍摄过程中,消旋速度满足设计要求,拍摄图片清晰,消旋精度(均方值)达到1.4’,比传统校正方法输出误差减少了46%。     

An adaptive network prefetch scheme

In this paper, we present an adaptive prefetch scheme for network use, in which we download files that will very likely be requested in the near future, based on the user access history and the network conditions. Our prefetch scheme consists of two parts: a prediction module and a threshold module. In the prediction module, we estimate the probability with which each file will be requested in the near future. In the threshold module, we compute the prefetch threshold for each related server, the idea being that the access probability is compared to the prefetch threshold. An important contribution of this paper is that we derive a formula for the prefetch threshold to determine its value dynamically based on system load, capacity, and the cost of time and system resources to the user. We also show that by prefetching those files whose access probability is greater than or equal to its server's prefetch threshold, a lower average cost can always be achieved. As an example, we present a prediction algorithm for web browsing. Simulations of this prediction algorithm show that, by using access information from the client, we can achieve high successful prediction rates, while using that from the server generally results in more hits     

An improved scheme for direct adaptive control of dynamical systems using backpropagation neural networks

This paper presents an improved direct control architecture for the on-line learning control of dynamical systems using backpropagation neural networks. The proposed architecture is compared with the other direct control schemes. In this scheme the neural network interconnection strengths are updated based on the output error of the dynamical system directly, rather than using a transformed version of the error employed in other schemes. The ill effects of the controlled dynamics on the on-line updating of the network weights are moderated by including a compensating gain layer. An error feedback is introduced to improve the dynamic response of the control system. Simulation studies are performed using the nonlinear dynamics of an underwater vehicle and the promising results support the effectiveness of the proposed scheme.     

A distributed robotic control system based on a temporal self-organizing neural network

A distributed robot control system is proposed based on a temporal self-organizing neural network, called competitive and temporal Hebbian (CTH) network. The CTH network can learn and recall complex trajectories by means of two sets of synaptic weights, namely, competitive feedforward weights that encode the individual states of the trajectory and Hebbian lateral weights that encode the temporal order of trajectory states. Complex trajectories contain repeated or shared states which are responsible for ambiguities that occur during trajectory reproduction. Temporal context information are used to resolve such uncertainties. Furthermore, the CTH network saves memory space by maintaining only a single copy of each repeated/shared state of a trajectory and a redundancy mechanism improves the robustness of the network against noise and faults. The distributed control scheme is evaluated in point-to-point trajectory control tasks using a PUMA 560 robot. The performance of the control system is discussed and compared with other unsupervised and supervised neural network approaches. We also discuss the issues of stability and convergence of feedforward and lateral learning schemes.     

无波前传感自适应光学神经网络控制方法

针对无波前探测自适应光学系统,研究了基于神经网络的波前控制方法.建立了无波前探测自适应光学仿真模型,分别采用卷积神经网络(Convolution Neural Network,CNN)和普通神经网络(General Neural Network,GNN)作为控制算法,远场光斑图像为神经网络的输入信号,一定阶数的泽尼克模...     

Call admission control for an adaptive heterogeneous multimedia mobile network

A novel call admission control (CAC) scheme for an adaptive heterogeneous multimedia mobile network with multiple classes of calls is investigated here. Different classes of calls may have different bandwidth requirement, different request call holding time and different cell residence time. At any time, each cell of the network has the capability to provide service to at least a given number of calls for each class of calls. Upon the arrival (or completion or hand off) of a call, a bandwidth degrade (or upgrade) algorithm is applied. An arriving call to a cell, finding insufficient bandwidth available in this cell, may either be disconnected from the network or push another call out of the cell toward a neighboring cell with enough bandwidth. We first prove that the stationary distribution of the number of calls in the network has a product form and then show how to apply this result in deriving explicit expressions of handoff rates for each class of calls, in obtaining the disconnecting probabilities for each class of new and handoff calls, and in finding the grade of service of this mobile network     

Generalized predictive control based on self-recurrent wavelet neural network for stable path tracking of mobile robots: adaptive learning rates approach

In this paper, a generalized predictive control (GPC) method based on self-recurrent wavelet neural network (SRWNN) is proposed for stable path tracking of mobile robots. Since the SRWNN has a self-recurrent mother wavelet layer, it can well attract the complex nonlinear system although the SRWNN has less mother wavelet nodes than the wavelet neural network. Thus, the SRWNN is used as a model identifier for approximating on-line the states of the mobile robot. In our control system, since the control inputs, as well as the parameters of the SRWNN identifier are trained by the gradient descent method with the adaptive learning rates (ALRs), the optimal learning rates which are suitable for the time-varying trajectory of the mobile robot can be found rapidly. The ALRs for training the parameters of the SRWNN identifier and those for learning the control inputs are derived from the discrete Lyapunov stability theorem, which are used to guarantee the convergence of the GPC system. Finally, simulation results are provided to demonstrate the effectiveness of the proposed control strategy.     

Architecture and control of an adaptive high capacity flat network

This article discusses the design of a scalable high-performance multiservice network based on programmable transport. A meshed network with dynamically adjustable link capacities and nodes which provide data packing into “containers” for transport is proposed. A ring-based network, exchanging data containers among its nodes, is the preferred implementation due to its flexibility, maintainability, and high reliability. With lossless rings, the quality of service is controlled solely by the origin and destination nodes, without any interference from other data streams. Flexible programmable transport greatly improves the performance, simplifies the controls, and facilitates scalability. The concept is a departure from classical network thinking. By reducing the complexity of the network core, an economical, reliable, and manageable network with feature-rich edge nodes can be realized. An architecture with recursive ring-based structures provides a high degree of flexibility in bandwidth allocation and is compatible with current transport networks and future all-optical networks     

输出不可量测非线性系统的神经模型参考自适应控制

该文针对被控对象输出不可量测的非线性系统,引入一个便于在线辨识的扩展神经网络模型,提出一种基于前馈-反馈结构的神经网络模型参考自适应控制方法。给出了具有全局收敛性的网络训练算法,并分析了控制系统的稳定性。仿真结果表明该控制方法是有效的,而且对网络初始权值的选取及被控对象特性参数的扰动都具有良好的鲁棒性。     

Implementation of LLCC-resonant driving circuit and adaptive CMAC neural network control for linear piezoelectric ceramic motor

In this paper, an adaptive cerebellar-model articulation computer (CMAC) neural network (NN) control system is developed for a linear piezoelectric ceramic motor (LPCM) that is driven by an LLCC-resonant inverter. The motor structure and LLCC-resonant driving circuit of an LPCM are introduced initially. The LLCC-resonant driving circuit is designed to operate at an optimal switching frequency such that the output voltage will not be influenced by the variation of quality factor. Since the dynamic characteristics and motor parameters of the LPCM are highly nonlinear and time varying, an adaptive CMAC NN control system is designed without mathematical dynamic model to control the position of the moving table of the LPCM drive system to achieve high-precision position control with robustness. In the proposed control scheme, the dynamic backpropagation algorithm is adopted to train the CMAC NN online. Moreover, to guarantee the convergence of output tracking error for periodic commands tracking, analytical methods based on a discrete-type Lyapunov function are utilized to determine the optimal learning-rate parameters of the CMAC NN. The effectiveness of the proposed driving circuit and control system is verified by experimental results in the presence of uncertainties, and the advantages of the proposed control system are indicated in comparison with a traditional integral-proportional position control system. Accurate tracking response and superior dynamic performance can be obtained due to the powerful online learning capability of the CMAC NN with optimal learning-rate parameters.     

Fuzzy neural networks for direct adaptive control

It is well known that sliding-mode control is simple and insensitive to uncertainties and disturbances. However, control input chattering is the main problem of the classical sliding-mode controller (SMC). In this paper, a fuzzy neural network SMC (FNNSMC) is presented for a class of nonlinear systems. The FNNSMC can eliminate the chattering, unlike the SMC, but there is larger rising time in the FNNSMC than in the SMC. In some cases, small rise time is important. To decrease the rising time of the FNNSMC, an adaptive controller is proposed where the SMC and the FNNSMC are incorporated by a smooth transformation. This adaptive control scheme can improve the dynamical performance and eliminate the high-frequency chattering in the control signal. The system stability is proved by using the Lyapunov function. The simulation results demonstrate the advantages of the proposed adaptive controller.     

A neural network control system with parallel adaptive enhancementsapplicable to nonlinear servomechanisms

In this paper, we present a technique for using an additional parallel neural network to provide adaptive enhancements to a basic fixed neural network-based nonlinear control system. This proposed parallel adaptive neural network control system is applicable to nonlinear dynamical systems of the type commonly encountered in many practical position control servomechanisms. Properties of the controller are discussed, and it is shown that if Gaussian radial basis function networks are used for the additional parallel neural network, uniformly stable adaptation is assured and the approximation error converges to zero asymptotically. In the paper, the effectiveness of the proposed parallel adaptive neural network control system is demonstrated in real-time implementation experiments for position control in a servomechanism with asymmetrical loading and changes in the load     

A prediction-based neural network scheme for lossless data compression

This paper proposes a modified block-adaptive prediction-based neural network scheme for lossless data compression. A variety of neural network models from a selection of different network types, including feedforward, recurrent, and radial basis configurations are implemented with the scheme. The scheme is further expanded with combinations of popular lossless encoding algorithms. Simulation results are presented, taking characteristic features of the models, transmission issues, and practical considerations into account to determine optimized configuration, suitable training strategies, and implementation schemes. Estimations are used for comparisons of these characteristics with the existing schemes. It is also shown that the adaptations of the improvised scheme increases performance of even the classical predictors evaluated. In addition, the results obtained support that the total processing time of the two-stage scheme can, in certain cases, be faster than just using lossless encoders. Findings of the paper may be beneficial for future work, such as, in the hardware implementations of dedicated neural chips for lossless compression.     

Trajectory control of robotic manipulators using neural networks

The authors present a nonlinear compensator using neural networks for trajectory control of robotic manipulators. The neural networks are not used to learn inverse-dynamics but to compensate nonlinearities of robotic manipulators. The performance of the proposed neural network controller is compared with that of the adaptive controller proposed by J.J. Craig (1988), and the effectiveness of the proposed neural network controller in compensating the unstructured uncertainties is clarified. A learning scheme using a model of known dynamics of manipulators is also proposed. The model learning can be done offline and needs no data recording of actual manipulator operation     

Optimization of an RFID location identification scheme based on the neural network

An indoor localization technology is increasingly critical as location‐aware applications evolve. Researchers have proposed several indoor localization technologies. Because most of the proposed indoor localization technologies simply involve using the received signal strength indicator value of radio‐frequency identification (RFID) for indoor localization, radio‐frequency interference, and environmental factors often limit the accuracy of localization results. Therefore, this study proposes an accurate RFID localization based on the neural network (ARL‐N²), a passive RFID indoor localization scheme for identifying tag positions in a room, combining a location identification based on dynamic active RFID calibration algorithm with a backpropagation neural network (BPN). The proposed scheme composed of two phases: in the training phase, an appropriate BPN architecture is constructed using the training data derived from the coordinates of reference tags and the coordinates obtained using the localization algorithm. By contrast, the online phase involves calculating the tracking tag coordinates and using these values as BPN inputs, thereby enhancing the estimated location. A performance evaluation of the ARL‐N² schemes confirms its high localization accuracy. The proposed method can be used to locate critical objects in difficult‐to‐find areas by creating minimal errors and applying and economical technique. Copyright © 2013 John Wiley & Sons, Ltd.     

自适应RBF神经网络在CDMA移动通信上行链路功率控制中的应用研究

提出了一种自适应RBF神经网络功率控制方案。详细研究了该网络在DS-CDMA通信中，进行上行链路闭环功率控制(基于信扰比(SIR))的应用理论，给出了该网络参数的计算方法。最后用计算机仿真法模拟出该控制器的运行性能。结果表明基于SIR的自适应RBF神经网络功率控制器能自适应地调整移动台的发射功率，使基站接收信号的信扰比始终非常接近于一个常数，且有比定步长功率控制更小的SIR跟踪误差，从而可以降低接收信号的中断概率、提高信道容量。