基于RBF神经网络的不确定机械臂自适应反演控制研究

针对机械臂位置跟踪问题,基于RBF神经网络控制理论,提出了一种自适应反演控制方法．该方法利用反演控制技术解决了系统的非线性问题,通过神经网络对系统中不确定函数进行逼近,实现神经网络自适应反演控制,最后通过Lyapunov稳定性理论证明所得闭环系统的最终一致有界．仿真结果证明该算法的有效性．     

Hybrid intelligent PID control design for PEMFC anode system

Control design is important for proton exchange membrane fuel cell （PEMFC） generator. This work researched the anode system ofa 60-kW PEMFC generator. Both anode pressure and humidity must be maintained at ideal levels during steady operation. In view of characteristics and requirements of the system, a hybrid intelligent PID controller is designed specifically based on dynamic simulation. A single neuron PI controller is used for anode humidity by adjusting the water injection to the hydrogen cell. Another incremental PID controller, based on the diagonal recurrent neural network （DRNN） dynamic identification, is used to control anode pressure to be more stable and exact by adjusting the hydrogen flow rate. This control strategy can avoid the coupling problem of the PEMFC and achieve a more adaptive ability. Simulation results showed that the control strategy can maintain both anode humidity and pressure at ideal levels regardless of variable load, nonlinear dynamic and coupling characteristics of the system. This work will give some guides for further control design and applications of the total PEMFC generator.     

平面并联机构的动力学建模及自适应控制

Dynamic model and control strategy of parallel mechanism have always been a problem in robotics research. In this paper, different dynamics formulation methods are discussed first. A model of redundant driven parallel mechanism with a planar parallel manipulator is then constructed as an example. A nonlinear adaptive control method is introduced. Matrix pseudo-inversion is used to get a desired actuator torque from a desired end-effector coordinate while the feedback torque is directly calculated in the actuator space. This treatment avoids forward kinematics computation that is very difficult in a parallel mechanism. Experiments with PID together with the descibed adaptive control strategy were carried out for a planar parallel mechanism. The results show that the proposed adaptive controller outperforms conventional PID methods in tracking desired input at a high speed.     

Dynamic velocity feed-forward compensation control with RBF-NN system identification for industrial robots

A dynamic velocity feed-forward compensation control (DVFCC) approach with RBF neural network (RBF-NN) dynamic model identification was presented for the adaptive trajectory tracking of industrial robots.The proposed control approach combined the advantages of traditional feedback closed-loop position control and computed torque control based on inverse dynamic model.The feed-forward compensator used a nominal robot dynamics as accurate dynamic model and on-line identification with RBF-NN as uncertain part to improve dynamic modeling accuracy.The proposed compensation was applied as velocity feed-forward by an inverse velocity controller that can convert torque signal into velocity in the standard industrial controller.Then,the need for a torque control interface was avoided in the real-time dynamic control of industrial robot.The simulations and experiments were carried out on a gas cutting manipulator.The results show that the proposed control approach can reduce steady-state error,suppress overshoot and enhance tracking accuracy and efficiency in joint space and Cartesian space,especially under highspeed condition.     

超声波电机的速度智能控制研究

针对超声波电机参数的时变性、系统内在的非线性和系统的强耦合性等特点,提出基于免疫遗传算法的超声波电机模糊神经网络速度控制策略．实验结果表明,与传统模糊神经网络速度控制相比,采用该方法的系统能较好地实现设定的超声波电机速度参考模型的自适应跟踪,响应速度脉动小,具有控制灵活、适应性强、控制精度高、鲁棒性强等优点．     

基于RBF神经网络辨识的直接甲醇燃料电池电堆非成性建模与自适应模糊控制

1 Introduction Direct methanol fuel cell ( DMFC) is desirable toserve as the power systemfor portable devices such ascellular phones , portable computers ,etc. due to thetheoretically high energy density and the liquid fuelused that can be stored and tran…     

输入力矩受限空间机械臂的鲁棒自适应控制

讨论了在输入力矩受限情况下,载体位置、姿态均不受控制的漂浮基空间机械臂系统的鲁棒自适应控制问题．建立了系统的动力学方程,并借助增广变量法,成功地克服了空间机械臂系统鲁棒自适应控制方案设计的难点,设计了一种增广鲁棒自适应控制器,并应用李雅普诺夫直接方法对控制方案的渐进稳定性条件作了证明．通过系统数值仿真,证实了控制方法的有效性．     

基于RBF神经网络的空间机械臂关节空间轨迹跟踪补偿控制

在PD策略的计算力矩法基础上,讨论了系统参数不确定的空间机械臂系统的控制问题.针对空间机械臂载体的位置不受控制,姿态受控制的情况下,对系统动量守恒关系进行了分析,得到了空间机械臂的系统动力学方程.采用PD策略的计算力矩法,考虑协调参数的不确定性,得到了系统的闭环动态误差方程.在此基础上提出了一种基于RBF神经网络的补偿学习控制方法,设计了具有不确定性的自由漂浮空间机械臂关节空间的补偿控制方案.将基于神经网络的补偿学习控制与计算力矩法相结合,利用进化学习来消除系统参数不确定性而造成的轨迹跟踪不准确的问题,实现了对空间机械臂关节空间内的轨迹跟踪控制.数值仿真的结果验证了该方法的有效性.     

基于参数自调整模糊神经网络的直接甲醇燃料电池电特性分析

This paper introduces the effects of cell operating temperature, methanol concentration and airflow rate, respectively, on the performance of direct methanol fuel cell （DMFC）. A novel method based on fuzzy neural networks identification technique is proposed to establish the performance model of DMFC. Three dynamic performance models of DMFC under the influences of cell operating temperature, methanol concentration, and airflow rate are identified and established separately. Simulation results show that modeling using fuzzy neural networks identification is satisfactory with high accuracy. It is applicable to DMFC control systems.     

Pressure observer based adaptive robust trajectory tracking control of a parallel manipulator driven by pneumatic muscles

This paper presents a pressure observer based adaptive robust controller (POARC) for posture trajectory tracking of a parallel manipulator driven by three pneumatic muscles without pressure sensors. Due to model errors of the static forces and friction forces of pneumatic muscles, simplified average flow rate characteristics of valves, unknown disturbances of entire system, and unmeasured pressures, there exist rather severe parametric uncertainties, nonlinear uncertainties and dynamic uncertainties in modeling of the parallel manipulator. A nonlinear pressure observer is constructed to estimate unknown pressures on the basis of a single-input-single-output (SISO) decoupling model that is simplified from the actual multiple-input-multiple-output (MIMO) coupling model of the parallel manipulator. Then, an adaptive robust controller integrated with the pressure observer is developed to accomplish high precision posture trajectory tracking of the parallel manipulator. The experimental results indicate that the system with the proposed POARC not only achieves good control accuracy and smooth movement but also maintains robustness to disturbances.     

Adaptive backstepping sliding mode control for nonlinear systems with input saturation

An adaptive backstepping sliding mode control is proposed for a class of uncertain nonlinear systems with input saturation.A command filtered approach is used to prevent input saturation from destroying the adaptive capabilities of neural networks (NNs).The control law and adaptive updating laws of NNs are derived in the sense of Lyapunov function,so the stability can be guaranteed even under the input saturation.The proposed control law is robust against the disturbance,and it can also eliminate the impact of input saturation.Simulation results indicate that the proposed controller has a good performance.     

受未知环境约束的位控操作机器人臂阻抗力控制

提出了一种面向位控操作机器人臂的阻抗力控制策略．利用在线力反馈数据对未知约束环境的形状进行估计以获得接触点处的环境切矢和法矢，根据该切矢和法矢实时生成目标阻抗模型的虚拟参考运动轨迹，在力误差信号的驱动下由目标阻抗模型产生机器人的指令运动轨迹，通过跟踪该指令运动轨迹机器人臂能够保持和未知约束环境的接触跟踪并将力误差限制在可接受的范围之内，力误差信号的动态行为与目标阻抗模型一致．为了验证控制策略的未知约束环境跟踪能力和力控制能力，以一个三杆平面机器人为例进行了计算机仿真，并在Adept-3精密装配机器人上进行了玻璃灯泡表面恒力跟踪的实验研究，仿真和实验结果表明中提出的策略具有很好的未知表面跟踪和力控制能力．     

集中供热系统热网控制策略的研究

集中供热系统热网有着多输入、多输出、非线性、时变性、强耦合的特点,智能解耦控制算法是解决热网流量控制的有效方法。文章讨论了神经网络解耦控制策略,设计了基于非线性、多变量的神经网络解耦控制器。应用于实验室热网控制的结果表明,该控制算法有明显的控制效果,能实现稳定供热和均匀供热的整体目标。     

基于广义BP算法的模型参考自适应控制

改进了传统的神经网络BP算法,提出了基于广义BP算法的非线性不确定系统的神经网络模型参考自适应控制方案.并将其用于非线性不确定系统的控制,仿真实验表明了控制器具有良好的非线性控制性能.     

基于递阶遗传算法的神经网络预测控制(英文)

针对具有时延非线性系统提出一种神经网络预测控制．相应的预测模型通过递阶遗传算法离线训练数据并实时多步递推得到;性能函数中引入模型误差校正以克服模型失配及干扰等对系统造成的影响．仿真结果表明该系统具有良好动态响应及鲁棒性．     

混联式混合动力客车逻辑控制策略研究

混合动力客车的控制策略决定整车的性能。分析某混联式混合动力客车的组成结构和基于逻辑控制的控制策略,建立相应的混联式混合动力客车仿真模型及其控制器,通过AVL/CRUISE和MATLAB/Simulink联合仿真分析,获得该车在中国典型城市公交工况下的动力性和百公里油耗。仿真结果表明,各主要部件能按照控制规则实现状态的合理切换,基于逻辑控制的控制策略能满足整车性能指标要求。     

基于DSC的自适应补偿神经网络控制

针对一类可转化为“标准块控制形”的MIMO非线性系统,基于动态面控制技术,提出一种鲁棒自适应神经网络控制算法。采用径向基函数神经网络逼近不确定性模型,通过引入一阶滤波器,消除后推设计中由于反复对虚拟控制的求导而导致的复杂性问题,同时补偿项的引入可避免反馈线性化方法中可能出现的控制器奇异性问题,无需控制增益矩阵正定、可逆的条件。利用李亚普诺夫方法,证明了闭环系统是半全局一致终结有界,适当选取设计常数,跟踪误差可收敛到原点的一个小邻域内。计算机仿真结果表明此法的有效性。     

柔性机器人动力学跟踪变阻抗控制

在人与机器人协调的过程中,控制系统不仅要考虑运动轨迹的位置精度,还要考虑人和机器人与环境接触的灵活性和平滑性。针对柔性多关节机器人动力学前馈的方法进行了研究,使得机器人可以快速、准确地移动到指定位置,控制精度高。提出了一种阻抗控制算法,通过调节机器人末端的阻抗来实现机器人的柔顺控制。依靠动力学的控制策略可以通过调整目标阻抗参数使机器人适应环境。通过对算法的有效性进行仿真验证,该算法可以改善大多数柔性串联机器人的运动控制问题,并且不涉及降低性能以适应操作和控制。     

一种基于无线环境预测的TCP拥塞控制改进机制

可靠传输协议TCP是有线网络成功的关键,它把丢包作为拥塞标志,迅速启动拥塞控制减轻网络负荷。然而,无线网络丢包主要是由于误码、切换等原因造成,如果无差别地启动拥塞控制会造成端到端性能恶化,表现在过激的发送窗口调整、吞吐量的急剧下降。在分析TCP吞吐量模型的基础上,提出一种基于神经网络区分误码和拥塞的新机制,实现无线环境的TCP拥塞控制。仿真结果表明,该机制改善了无线环境下TCP的性能。     

基于神经网络的直流电机模型辨识及应用

为了提高控制系统的控制性能,实现自适应控制,提出了一种基于神经网络模型的参数辨识方法.首先,为一台未知参数的直流电机设计了基于DSP(数字信号处理器)的控制器,再通过采集卡获取大量的实时观测数据,然后对数据进行预处理,将预处理后的数据按照神经网络辨识的原理完成模型辨识.通过比较直流电机实际阶跃响应与模型阶跃响应,对辨识模型进行检验,最后设计了该系统的PI校正装置.结果表明该方法可操作性强、辨识精度高、应用性强.