Adaptive neural network control for coordinated motion of a dual-arm space robot system with uncertain parameters

Control of coordinated motion between the base attitude and the arm joints of a free-floating dual-arm space robot with uncertain parameters is discussed. By combining the relation of system linear momentum conversation with the Lagrangian approach, the dynamic equation of a robot is established. Based on the above results, the free-floating dual-arm space robot system is modeled with RBF neural networks, the GL matrix and its product operator. With all uncertain inertial system parameters, an adaptive RBF neural network control scheme is developed for coordinated motion between the base attitude and the arm joints. The proposed scheme does not need linear parameterization of the dynamic equation of the system and any accurate prior-knowledge of the actual inertial parameters. Also it does not need to train the neural network offline so that it would present real-time and online applications. A planar free-floating dual-arm space robot is simulated to show feasibility of the proposed scheme.     

Exploring human rhythmic gait movement in the role of cerebral cortex signal

The rhythmic movement is a spontaneous behavior due to the central pattern generator(CPG).At present,the CPG model only shows the spontaneous behavior,butdoes not refer to the instruction regulation role of the cerebral cortex.In this paper,a modified model based on the Matsuoka neural oscillator theory is presented to better show the regulation role of the cerebral cortex signal to the CPG neuronal network.The complex interaction between the in put signal and other parameters in the CPG networkis establish...     

Exploring human rhythmic gait movement in the role of cerebral cortex signal

The rhythmic movement is a spontaneous behavior due to the central pattern generator (CPG). At present, the CPG model only shows the spontaneous behavior, but does not refer to the instruction regulation role of the cerebral cortex. In this paper, a modified model based on the Matsuoka neural oscillator theory is presented to better show the regulation role of the cerebral cortex signal to the CPG neuronal network. The complex interaction between the input signal and other parameters in the CPG network is established, making all parameters of the CPG vary with the input signal. In this way, the effect of the input signal to the CPG network is enhanced so that the CPG network can express the self-regulation movement state instead of being limited to the spontaneous behavior, and thus the regulation role of the cerebral cortex signal can be reflected. Numerical simulation shows that the modified model can generate various movement forms with different modes, frequencies, and interchanges between them. It is revealed in theories that the cerebral cortex signal can regulate the mode and frequency of the gait in the course of the gait movement.     

A finite-time recurrent neural network for solving online time-varying Sylvester matrix equation based on a new evolution formula

Sylvester equation is widely used to study the stability of a nonlinear system in the control field. In this paper, a finite-time Zhang neural network (FTZNN) is proposed and applied to online solution of time-varying Sylvester equation. Differing from the conventional accelerating method, the design of the proposed FTZNN model is based on a new evolution formula, which is presented and studied to accelerate the convergence speed of a recurrent neural network. Compared with the original Zhang neural network (ZNN) for time-varying Sylvester equation, the FTZNN model can converge to the theoretical time-varying solution within finite time, instead of converging exponentially with time. Besides, we can obtain the upper bound of the finite convergence time for the FTZNN model in theory. Simulation results show that the proposed FTZNN model achieves the better performance as compared with the original ZNN model for solving online time-varying Sylvester equation.     

Control strategy of central pattern generator gait movement under condition of attention selection

As a typical rhythmic movement, human being’s rhythmic gait movement can be generated by a central pattern generator (CPG) located in a spinal cord by self-oscillation. Some kinds of gait movements are caused by gait frequency and amplitude variances. As an important property of human being’s motion vision, the attention selection mechanism plays a vital part in the regulation of gait movement. In this paper, the CPG model is amended under the condition of attention selection on the theoretical basis of Matsuoka neural oscillators. Regulation of attention selection signal for the CPG model parameters and structure is studied, which consequentially causes the frequency and amplitude changes of gait movement output. Further, the control strategy of the CPG model gait movement under the condition of attention selection is discussed, showing that the attention selection model can regulate the output model of CPG gait movement in three different ways. The realization of regulation on the gait movement frequency and amplitude shows a variety of regulation on the CPG gait movement made by attention selection and enriches the controllability of CPG gait movement, which demonstrates potential influence in engineering applications.     

基于虚拟力概念的柔性臂空间机器人模糊退步自适应控制算法设计

讨论了漂浮基柔性臂空间机器人系统的动力学模拟、运动轨迹跟踪控制算法设计及柔性振动主动抑制。采用多体动力学建模方法并结合假设模态法,建立了漂浮基柔性臂空间机器人的系统动力学模型。基于该模型,针对系统惯性参数未知情况,提出了刚性运动基于模糊基函数网络自适应调节的退步控制算法,以完成柔性臂空间机器人载体姿态及机械臂各关节铰的协调运动。然后,为了主动抑制系统柔性振动,运用虚拟力的概念,构造了同时反映柔性模态和刚性运动轨迹的混合期望轨迹,通过改造原有的控制算法,提出了基于虚拟力概念的模糊退步自适应控制算法;这样不但保证了之前刚性运动控制方案对模型不确定的鲁棒性,而且能主动抑制柔性振动,从而提高了轨迹跟踪性能。理论分析及数值仿真算例均表明了控制方法的可行性。     

考虑摩擦特性的柔性关节空间机器人自适应CMAC神经网络鲁棒控制

讨论了关节摩擦力矩影响下,具有柔性铰关节的漂浮基空间机器人系统的动力学控制问题.设计了基于高斯基函数的小脑神经网络(CMAC)鲁棒控制器和摩擦力矩补偿器.用奇异摄动理论对系统的动力学模型进行快慢变子系统分解,针对快变子系统,设计力矩微分反馈控制器来抑制机械臂关节柔性引起的振动;对于慢变子系统,设计了基于自适应CMAC神...     

Adaptive sliding mode control of dynamic system using RBF neural network

This paper presents a robust adaptive sliding mode control strategy using radial basis function (RBF) neural network (NN) for a class of time varying system in the presence of model uncertainties and external disturbance. Adaptive RBF neural network controller that can learn the unknown upper bound of model uncertainties and external disturbances is incorporated into the adaptive sliding mode control system in the same Lyapunov framework. The proposed adaptive sliding mode controller can on line update the estimates of system dynamics. The asymptotical stability of the closed-loop system, the convergence of the neural network weight-updating process, and the boundedness of the neural network weight estimation errors can be strictly guaranteed. Numerical simulation for a MEMS triaxial angular velocity sensor is investigated to verify the effectiveness of the proposed adaptive RBF sliding mode control scheme.     

基于ED-LSTM的智能汽车神经网络横向动力学建模与控制

车辆动力学建模过程中通常会进行简化和假设, 导致模型在某些工况下无法准确反映车辆的实际动态特性, 影响控制精度甚至安全性. 鉴于此, 该文提出了一种基于数据驱动的非线性建模与控制方法, 建立了新型神经网络车辆横向动力学多步预测模型, 实现了智能汽车对参考轨迹的跟踪控制. 首先, 在分析车辆单轨模型并考虑轮胎非线性和纵向负载转移的基础上, 基于编码器?解码器结构设计神经网络横向动力学模型. 其中, 使用串行排列来扩展微分方程描述不完全的动力学信息, 隐藏层神经元学习车辆的高度非线性和强耦合特性, 进而提高模型全局计算精度. 利用所构建的数据集进行模型训练和测试, 结果表明, 相比于物理模型, 所提出的模型在不同路面附着系数条件下均具有更高的建模精度, 具有隐式预测路面摩擦条件能力. 其次, 利用提出的模型设计轨迹跟踪控制算法, 根据车辆稳态转向假设, 计算所需的前轮转向角和稳态质心侧偏角, 将稳态质心侧偏角纳入基于路径误差的转向反馈中, 实现参考轨迹跟踪控制. 最后, 使用CarSim/Simulink联合仿真及HIL实验测试进行不同工况试验的对比分析, 对所提出的基于神经网络模型的控制算法进行评价, 结果表明, 该模型能够实现智能汽车在高速下精确的跟踪控制效果, 并具有良好的横向稳定性.      

基于Takagi—Sugeno模型的半主动TLCD对偏心结构的减震控制

采用在结构水平双向设置TLCD半主动控制装置的方法，对偏心结构在多维地震作用下的振动控制问题进行了研究。首先论述了基于Takagi—Sugeno模型的模糊神经网络的基本理论。然后介绍结构-TLCD扭转耦联控制系统微分方程及其状态空间求解方法，最后在建立起半主动控制策略的基础上。利用基于Takagi—Sugeno模型的模糊神经网络，根据控制准则调整TLCD的开孔率，实现对结构的半主动控制。数值结果表明，这种方法能对结构的平动反应和扭转反应都能起到较好的减震效果。     

冲击地压时列建模的进化神经网络方法

考虑到冲击地压时序的特点,采用基于免疫进化规划的进化神经网络进行了冲击地压非线性系统的建模研究。并采用一个矿山实测得到的震级数列进行了进化神经网络方法的实用性验证,结果表明,进化神经网络不但模型拟合精度高,而且预测性能也较好。     

空间机器人捕获航天器后基于无源性理论的鲁棒镇定控制

研究了漂浮基空间机器人捕获非合作航天器过程对系统产生的冲击效应及其后联合体系统镇定运动的控制问题。为此,利用拉格朗日方法及牛顿-欧拉法分别获得了捕获前空间机器人及目标航天器的动力学模型;结合动量守恒定律、系统运动几何关系及力的传递规律,分析了捕获过程相互碰撞所产生的冲击效应,建立了捕获完成后两者联合体的系统动力学模型。在此基础上,针对同时存在不确定参数及外部扰动的联合体系统,设计了基于无源性理论的镇定运动神经网络H_∞鲁棒控制算法。本文提出的基于无源性理论设计的鲁棒控制算法具有良好的动态特性及较强的鲁棒性,可快速完成系统的镇定控制,实现轨迹的精确跟踪。系统数值模拟仿真验证了本文控制方案的正确性。     

多自由度仿生推进系统的CPG控制

为了提高航行稳定性和机动性而设计的四尾鳍组合推进水下航行器,尾鳍运动自由度众多且相互耦合,稳定且快速的控制方案对提高航行器的整体性能至关重要。本文根据尾鳍运动特点,建立了中枢模式发生器(CPG)模型,协调控制8个驱动舵机,实现巡游、倒退、偏航、俯仰等各种航行状态下尾鳍的组合运动;通过陀螺仪监测航行器的偏航角与俯仰角,形成反馈信号引入CPG模型,对尾鳍运动进行反馈控制,进一步提高了航行稳定性。     

机械臂臂杆刚度主动控制下的末端振动特性研究

机械臂在运动过程中会因臂杆柔性引发结构变形和弹性振动,降低机械臂末端的定位精度和运动稳定性,将结构振动控制方法用于机械臂的振动抑制研究具有重要意义. 基于变刚度主动控制的设计思想,提出了臂杆刚度主动控制方法,通过改变机械臂臂杆的轴向受力状况来主动改变机械臂的刚度. 采用变形耦合法描述了机械臂的非线性变形,进而结合假设模态法和拉格朗日方程建立了臂杆的变刚度动力学模型,并进行了数值仿真. 在此基础上,设计了基于臂杆刚度主动控制方法的单自由度实验台,分析了不同预紧力下机械臂末端的振动特性. 数值仿真和实验结果表明,随着预紧力的增加,机械臂末端的振动幅值得到衰减,验证了臂杆刚度主动控制方法的有效性. 通过采用响应面法建立了机械臂末端的振动响应与预紧力的关系,并基于内部映射牛顿法的子空间置信域法优化算法对预紧力进行了优化分析,得到了最优预紧力. 该研究可为机械臂的精细动力学建模和振动抑制提供一定的理论依据,并为研究经济型低刚度材料的刚化问题提供了方向,以利用廉价低刚度材料取代目前所应用的昂贵高刚度材料.      

Experimental verification of a memristive neural network

This paper presents an electronic circuit able to emulate the behavior of a neural network based on memristive synapses. The latter is built with two flux-controlled floating memristor emulator circuits operating at high frequency and two passive resistors. Synapses are connected in a way that a bridge circuit is obtained, and its dynamical behavioral model is derived from characterizing memristive synapses. Analysis of the memristor characteristics for obtaining a suitable synaptic response is also described. A neural network of one neuron and two inputs is connected using the proposed topology, where synaptic positive and negative weights can easily be reconfigured. The behavior of the proposed artificial neural network based on memristors is verified through MATLAB, HSPICE simulations and experimental results. Synaptic multiplication is performed with positive and negative weights, and its behavior is also demonstrated through experimental results getting 6% of error.     

RESEARCH ON VIBRATION CHARACTERISTICS OF THE MANIPULATOR END UNDER ACTIVE CONTROL OF ARM STIFFNESS$^{\bf 1)}$

机械臂在运动过程中会因臂杆柔性引发结构变形和弹性振动,降低机械臂末端的定位精度和运动稳定性,将结构振动控制方法用于机械臂的振动抑制研究具有重要意义. 基于变刚度主动控制的设计思想,提出了臂杆刚度主动控制方法,通过改变机械臂臂杆的轴向受力状况来主动改变机械臂的刚度. 采用变形耦合法描述了机械臂的非线性变形,进而结合假设模态法和拉格朗日方程建立了臂杆的变刚度动力学模型,并进行了数值仿真. 在此基础上,设计了基于臂杆刚度主动控制方法的单自由度实验台,分析了不同预紧力下机械臂末端的振动特性. 数值仿真和实验结果表明,随着预紧力的增加,机械臂末端的振动幅值得到衰减,验证了臂杆刚度主动控制方法的有效性. 通过采用响应面法建立了机械臂末端的振动响应与预紧力的关系,并基于内部映射牛顿法的子空间置信域法优化算法对预紧力进行了优化分析,得到了最优预紧力. 该研究可为机械臂的精细动力学建模和振动抑制提供一定的理论依据,并为研究经济型低刚度材料的刚化问题提供了方向,以利用廉价低刚度材料取代目前所应用的昂贵高刚度材料.     

NEURAL NETWORK MODEL OF MAGNETORHEOLOGICAL DAMPER FOR VIBRATION ISOLATION PLATFORM OF WHOLE-SPACECRAFT

为改善星箭界面振动环境,设计六杆隔振平台,采用磁流变阻尼器作为半主动控制元件,替代原有锥壳过渡支架.对整星隔振平台用磁流变阻尼器进行性能测试,得到反映磁流变阻尼器阻尼特性的实验数据.建立具有两个隐含层的反向传播神经网络对阻尼器进行建模,用于预测磁流变阻尼器阻尼特性以及控制系统设计.提出一种串行算法优化网络结构、权值和阈值,保证网络具有较好的泛化能力和稳定性.仿真结果表明,与参数化模型相比,提出的神经网络模型具有较小的训练误差和较强的泛化能力,能够很好地预测阻尼器的阻尼特性.     

2021-03期目录

针对毁伤试验数据少、不均匀、不连续、范围窄等带来的计算精度不高的问题。研究通过数据挖掘技术进行毁伤效应计算。利用数据库管理毁伤数据,通过数据清洗技术识别并清除数据异常点,以保证数据库中数据的质量。建立了算法评价方法以选择最优经验算法。通过特征选择对高维毁伤数据进行降维,确定毁伤效应的主要控制参数进行神经网络学习和k-近邻检索。在此基础上建立基于数据融合的“三阶段”毁伤效应计算模型,可依据试验数据、经验算法和神经网络模型进行毁伤效应计算。实际应用表明,所提出的计算方法,能够满足实际应用需求。     

基于并行卷积神经网络的地磁方向适配性分析

针对地磁方向适配性分析时人工特征提取主观性较强、所取特征难以表达深层的结构性特征的问题,并为了进一步提高方向适配性分析的准确率,提出了一种基于并行卷积神经网络的地磁方向适配性分析方法。首先,从不同角度建立了地磁场在6个代表方向上的适配性分析图;然后,从同一磁场的不同角度出发,利用卷积神经网络自动完成了特征学习,得到了更为全面的方向适配性特征描述;最后,在并行卷积神经网络所得特征的基础上,利用BP网络建立了地磁方向适配性的分析模型。仿真结果证明,该方法可以有效避免人工特征提取和计算等复杂步骤,实现了地磁方向适配性分析的自动化,而且可以获得优于传统网络和单路卷积神经网络的准确率。     

VIBRATION SUPPRESSION OF A FLEXIBLE PIEZOELECTRIC BEAM USING BP NEURAL NETWORK CONTROLLER

This paper aims at modeling and developing vibration control methods for a flexible piezoelectric beam. A collocated sensor/actuator placement is used. Finite element analysis (FEA) method is adopted to derive the dynamics model of the system. A back propagation neural network (BPNN) based proportional-derivative (PD) algorithm is applied to suppress the vibration. Simulation and experiments are conducted using the FEA model and BPNN-PD control law. Experimental results show good agreement with the simulation results using finite element modeling and the neural network control algorithm.