基于RBF神经网络的永磁同步伺服电机控制系统

针对永磁同步电机控制系统,建立其磁场定向控制数学模型。运用增量式数字PID的方法实现对PMSM的传统PID控制策略。在此基础上,借助RBF神经网络的学习能力,进行PID控制器参数的自适应整定,进一步改善PID控制器的性能。同时,为提高RBF网络性能,采用粒子群算法对网络进行优化。仿真表明,与传统PID控制比较,基于RBF的PID控制系统能提高PID控制器的性能,改善了PMSM控制系统的收敛速度和跟踪精度。     

基于新型CMAC控制方法的电动加载系统

在电动加载系统中,多余力矩强扰动和其他非线性因素直接影响力矩跟踪精度,传统的控制方法难以得到满意的控制效果。文中分析了传统CMAC算法不稳定的原因,提出了一种新型CMAC控制策略,并对其结构及算法进行了研究。在控制结构上以系统的指令输入和实际输出作为CMAC的激励信号,采用误差作为训练信号,并根据激励信号的特点,提出了非均匀量化的思想。动态仿真结果表明,该方法有效抑制了加载系统的多余力矩及摩擦等非线性因素干扰,提高了电动加载系统的控制精度,且增强了系统的稳定性。     

Robust cerebellar model articulation controller design for unknown nonlinear systems

In this study, a robust cerebellar model articulation controller (RCMAC) is designed for unknown nonlinear systems. The RCMAC is comprised of a cerebellar model articulation controller (CMAC) and a robust controller. The CMAC is utilized to approximate an ideal controller, and the weights of the CMAC are on-line tuned by the derived adaptive law based on the Lyapunov sense. The robust controller is designed to guarantee a specified H/sup /spl infin// robust tracking performance. In the RCMAC design, the sliding-mode control method is utilized to derive the control law, so that the developed control scheme has more robustness against the uncertainty and approximation error. Finally, the proposed RCMAC is applied to control a chaotic circuit. Simulation results demonstrate that the proposed control scheme can achieve favorable tracking performance with unknown the controlled system dynamics.     

Learning of Hybrid Fuzzy Controller for the Optical Data Storage Device

A hybrid track-seeking fuzzy controller for an optical disk drive (ODD) is proposed in this paper. The proposed hybrid fuzzy controller (HFC) smoothes the voltage applied to the sled motor and improves the track-seeking efficiency. The HFC consists of two subsystems including an intelligent time switch and a driving force controller. Both subsystems are designed based on fuzzy logic inferences. The main functions of the proposed HFC are to drive the optical head unit (OHU) to the target track neighborhood as fast as possible and smoothly park the OHU in the least time in the target track neighborhood. An automatic learning approach based on genetic algorithms (GAs) is proposed for learning the fuzzy rules for both the intelligent time switch and driving force controller. Modulated orthogonal membership functions are utilized in both fuzzy controllers to improve the GA learning efficiency. The number of parameters needed to parameterize the fuzzy rule base is greatly reduced with the modulated orthogonal membership functions. Compared to the conventional track-seeking controller currently utilized in most ODDs that employ a speed profile as the reference signal for the track-seeking feedback control system, the proposed HFC outperforms the conventional track-seeking control schemes. Experiments are performed to justify the performance comparison.     

Design of incremental fuzzy PI controllers for a gas-turbine plant

In this paper, incremental fuzzy proportional integral (PI) speed and temperature controllers for a heavy-duty gas-turbine plant are presented. To improve performance, an analysis of incremental fuzzy PI control is provided, and new fuzzy control rules are proposed. In applying the fuzzy PI control to a gas-turbine plant, all gains are optimized by an adaptive genetic algorithm. We show the performance improvement of the proposed controller compared with conventional PI controller via simulations.     

Neural network-based control of balanced robotic manipulators with joint flexibility

This paper addresses an improvement on the controlled performance of balanced manipulators in a practical level by implementing neural network based controller. The mass balancing of robotic manipulators has been shown to have favorable effects on the dynamic characteristics. However, it was also pointed out that for the manipulators having a certain degree of flexibility at the joints, due to the lowered structural natural frequencies and reduced velocity related terms, mass balancing results in vibratory motion at high speed operation. Such a vibratory tendency of the balanced flexible joint manipulator limits the admissible range of servo gains of the conventional controllers, making those controllers unsuitable for controlling the manipulator at high speeds.

To avoid such difficulty, an artificial neural network (NN) controller is introduced to realize the dynamic control of the balanced flexible joint manipulators. A feedforward type of NN controller is proposed and its performance is evaluated through a series of numerical simulations. The proposed NN controller showed much better tracking performances over the conventional PD controller.     

一种基于浮点数编码遗传算法的CMAC控制仿真研究

在保留CMAC原有增强和局部特性的基础上。结合模糊逻辑的思想。采用模糊隶属度函数作为接收域函数，提出了一种广义模糊小脑模型神经网络（GFAC）。将GFAC神经网络用于控制系统中，并利用浮点数编码的遗传算法（FGA）对参数寻优，给出一种FGA-GFAC控制器。应用于船舶航向控制的仿真结果表明，当存在风浪干扰海况下，船舶航向的控制取得令人满意的效果。     

一种面向非线性系统控制的自适应模糊小脑神经网络及研究

利用Lyapunov自稳定性准则,将自适应机制引入到模糊小脑神经网络(CMAC)的实时学习算法之中,提高其在闭环控制系统中的鲁棒性,使其能够有效地对模型未知的非线性系统进行实时控制.仿真表明自适应CMAC神经网络由于采用了基于Lyapunov自稳定准则的学习算法,系统的跟踪稳定性和误差收敛性都能够得到保证,而且不需离线学习阶段,实时控制效果较好.     

多控制器集群流媒体缓存代理的研究

为满足用户分散的大规模视频流媒体应用需求,该文提出了一种基于多控制器集群缓存代理结构(Multi- controller based Cluster Streaming Cache Proxy, MCSCP),该体系由一组控制器构成控制子系统,由多个内容存储器构成分布式存储子系统。对控制子系统的关键问题进行了深入研究:采用图论思想给出控制器组的选取算法,设计了一种基于优先级的主控制器选举和在线切换协议(Priority based Master-controller Election and Handover, PMEH)。建立数学模型分析了多控制器体系对系统可靠性的改善程度,讨论了控制器选取算法的性能,研究了协议中报文发送间隔变量与系统开销的关系,并使用仿真实验给出了该参数的最佳取值范围。     

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.     

基于CMAC的卡尔曼滤波技术在组合导航系统中的应用

经典卡尔曼滤波器要求假设系统的动态模型和观测模型的噪声统计特性已知,而组合导航系统的噪声具有非先验性。为了解决这一问题,提出小脑模型神经网络（CMAC）辅助卡尔曼滤波器。仿真试验结果表明,该辅助算法的精度与经典卡尔曼滤波算法相比提高了2倍,收敛时间缩短近200 s,并有效地克服了传统神经网络学习速度慢,泛化能力弱的缺点,使系统具有自适应能力,以应付动态环境的扰动。     

Position control of a pneumatic surgical robot using PSO based 2-DOF H∞ loop shaping structured controller

This research proposed novel development of a 2-DOF H_∞ loop shaping structured controller based on Particle Swarm Optimization (PSO) that considers the closed-loop dynamic response, robustness, stability, and minimal control input in design criteria to control position of 3-DOF pneumatic surgical robot. Unlike other conventional H_∞ controllers, the proposed controller offers robustness, high performance, but cost-effective simple structure, which has recently received attention from several researchers and preferred in industrial applications. The proposed technique is simulated and experimented on a nonlinear system of a pneumatic 3-DOF surgical robot for a Minimally Invasive Surgery (MIS). Mechanical design, dynamics modeling, and system identification of the surgical robot are conducted. The simulation results verify that the proposed controller can gain a better H_∞ sub-optimal solution than the conventional 2-DOF H_∞ loop shaping controller. Also, the experiments confirm that the proposed controller is capable to tolerate the perturbed conditions and can be alternative to the conventional controllers in pneumatic controlled system     

Hybrid learning control schemes with input shaping of a flexible manipulator system

The paper describes a practical approach to investigate and develop a hybrid iterative learning control scheme with input shaping. An experimental flexible manipulator rig and corresponding simulation environment are used to demonstrate the effectiveness of the proposed control strategy. A collocated proportional-derivative (PD) controller utilizing hub-angle and hub-velocity feedback is developed for control of rigid-body motion of the system. This is then extended to incorporate iterative learning control with acceleration feedback and genetic algorithms (GAs) for optimization of the learning parameters and a feedforward controller based on input shaping techniques for control of vibration (flexible motion) of the system. The system performance with the controllers is presented and analysed in the time and frequency domains. The performance of the hybrid learning control scheme with input shaping is assessed in terms of input tracking and level of vibration reduction. The effectiveness of the control schemes in handling various payloads is also studied.     

Evolutionary algorithms for fuzzy control system design

This paper provides an overview on evolutionary learning methods for the automated design and optimization of fuzzy logic controllers. In a genetic tuning process, an evolutionary algorithm adjusts the membership functions or scaling factors of a predefined fuzzy controller based on a performance index that specifies the desired control behavior. Genetic learning processes deal with the automated design of the fuzzy rule base. Their objective is to generate a set of fuzzy if-then rules that establishes the appropriate mapping from input states to control actions. We describe two applications of genetic-fuzzy systems in detail: an evolution strategy that tunes the scaling and membership functions of a fuzzy cart-pole balancing controller and a genetic algorithm that learns the fuzzy control rules for an obstacle-avoidance behavior of a mobile robot     

An online learning fuzzy controller

A fuzzy controller with online learning capability is reported in this paper. The controller learns from a standard proportional plus derivative (PD) controller. It is implicitly assumed that the tuning parameters of the PD controller are already known. The learning is realized via Wang's table lookup scheme. The controllers are applied successfully to control an open-loop unstable system, i.e., the ball and plate system. Experimental studies have demonstrated the performance of the proposed controller.     

A method for improving the robustness of PID control

In this paper, an effective method is proposed for robust proportional-integral-derivative (PID) control that is easily implementable on commonly used equipment such as programmable logic controller (PLC) and programmable automation controller (PAC). The method is based on a two-loop model following control (MFC) system containing a nominal model of the controlled plant and two PID controllers. Basic features exhibited by the MFC structure are presented, and a technique to tune both component controllers is given. The proposed structures have been implemented in a programmable logic controller and tested on control plants with perturbed parameters. Also, the proposed control system has been checked for its performance in cases when the operation of PID controllers is based on fuzzy logic. Tuning rules for the fuzzy controllers in the presented MFC system have been proposed. Results of tests lend support to the view that the proposed control structures may find wide application to robust control of plants with time-varying parameters.     

Robust loop shaping-fuzzy gain scheduling control of a servo-pneumatic system using particle swarm optimization approach

In this paper, a new technique called robust loop shaping-fuzzy gain scheduled control (RLS-FGS) is proposed to design an effective nonlinear controller for a long stroke pneumatic servo system. In our technique, a nonlinear dynamic model of a long stroke pneumatic servo plant is identified by the fuzzy identification method and is used as the plant for our design. The structure of local controllers is selected as PID control which is proven by many research works that this type of control has many advantages such as simple structure, well understanding, and high performance. The proposed technique uses particle swarm optimization (PSO) to find the optimal local controllers which maximize the average stability margin. In addition, performance weighting function which is normally difficult to obtain is automatically determined by PSO. By the proposed technique, the RLS-FGS controller can be designed, and the structure of local controllers is still not complicated. As seen in the simulation and experimental results, our proposed technique is better than the classical gain scheduled PID controller tuned by pole placement and the conventional fuzzy PID controller tuned by ISE method in terms of robust performance.     

伺服运动系统CMAC神经网络算法研究

为改善高速高精设备加工质量,利用小脑模型具有不依赖于被控对象的精确数学模型、极强的非线性拟合能力、对运行工况适应能力强的优势,将其与PID控制相结合形成PID＋CMAC控制策略;并借助Turbo PMAC的＂开放伺服＂功能,在Turbo PMAC运动控制器上实现了PID＋CMAC控制策略。测试结果表明PID＋CMAC控...     

一种改进的航空发动机CMAC与PD并行的控制算法

针对某型航空发动机数字式电子调节系统控制性能提升的需要,将CMAC与PD的并行控制算法首次应用于该数控系统低压转子转速调节通道,同时为了增强该通道克服超转的能力,提出了一种改进的CMAC与PD的并行控制算法,该改进算法通过给CMAC的控制量输出乘上一个时变的"恢复系数σ"可以动态地改变系统总控制量。仿真结果表明该改进算法响应速度快,静态误差小,在控制发动机非线性时变模型时具备较强的克服超调能力。     

软件定义网络中基于负载均衡的多控制器部署算法

随着软件定义网络规模扩大,控制层与数据层解耦带来了诸如控制器部署等新问题。该文提出基于负载均衡的多控制器部署算法(Multi-Controller Deployment Algorithm Based on Load Balance, MCDALB)。算法首先根据网络拓扑结构及其负载情况,确定控制器数量K;然后根据控制器容量限制,提出一种近似比为2的多控制器负载均衡算法,将网络划分成K个控制区域;最后根据区域内所有交换机到控制器距离总和最小原则,在控制区域部署控制器。为了验证算法的性能,选取实际网络拓扑进行实验。实验结果表明,与AL, WL算法相比,该算法在满足控制器负载近似比为2的同时,网络最大延时差距不超过0.65 ms。