加速度驱动型三关节体操机器人的动力学建模与分析

针对加速度驱动型三关节体操机器人,从机理和数据两方面进行了动力学模型分析．首先通过机理分析得到模型的刚体动力学分量和电机驱动分量;然后通过加速度阶跃响应曲线分析得到纯滞后分量．综合这3种分量,确定三关节体操机器人的模型结构,并利用改进的遗传算法对该综合模型进行参数辨识．将辨识后的模型与实际系统进行比较,讨论了产生误差的原因．     

一类三关节体操机器人的类等效建模

无力矩驱动模式的电机驱动器使得三关节体操机器人控制难度增加,但具有实际意义.针对该类机器人系统,提出了基于＂类等效＂思想的简化动力学模型,该模型由刚体动力学和加速度驱动两个子模型构成.利用拉格朗日法建立刚体动力学方程;使用频率响应法得到加速度驱动模型的伯德图并确定了其阶次,根据阶跃响应曲线确定其纯滞后;使用改进的遗传算法辨识模型参数.通过对简化前后的模型进行对比实验以及简化模型的稳定控制实验,验证了简化模型的有效性和优越性.     

具有谐波减速器的机器人关节建模与动力学参数辨识

针对串联机器人,提出了一种改进的机器人关节模型,并采用该模型开展了机器人动力学建模与辨识工作。建立了机器人动力学模型,对机器人关节结构进行分析,改进了关节模型,并通过谐波减速器的输入力矩近似估计其摩擦力矩。选取傅里叶级数为激励轨迹并优化其参数,通过控制关节按照所得轨迹运动,采集并处理相关数据,并基于加权最小二乘法分别辨识机器人关节模型参数与连杆动力学参数。通过关节预测力矩对所得参数进行验证,结果表明,基于改进关节模型的机器人动力学模型精度得到明显提升。     

考虑关节柔性的模块机器人动力学参数辨识

提出了一种基于3维运动测量系统Optotrak3020的柔性关节模块机器人动力学模型参数辨识方法.首先将机器人的动力学模型参数化为不包括刚度力矩的线性形式,避免了参数矩阵的标定问题.激励轨迹基于有限的傅里叶级数函数,采用自适应遗传算法得出了优化的傅里叶级数系数.机器人每一关节单独跟随优化激励轨迹进行运动,同时电机位置、...     

工业机器人负载动力学参数辨识方法

为了解决负载变化导致的机器人控制性能降低的问题,本文在分析负载动力学参数对各关节力矩的影响的基础上,提出了一种仅驱动机器人的第3、4、5、6轴运行激励轨迹的辨识方法．首先,基于最小惯性参数集线性化工业机器人动力学模型;其次,在分析负载参数对各关节力矩的影响的基础上,选取相应的运动关节轴,设计适用于负载辨识的有限项傅里叶级数的优化激励轨迹;然后,在空载和带3种不用负载情况下运行激励轨迹,采集关节角度和关节力矩数据,并将数据通过低通滤波器处理;最后,基于动力学线性模型使用加权最小二乘法辨识负载动力学参数．机器人运行验证轨迹,通过计算负载力矩计算值和测量值的差的均方根（RMS）来评价负载辨识结果．同时将该方法与CAD（computer aided design）方法对比,结果显示前者最多可以将后者RMS值降为原来的16%,且该方法对不同负载辨识结果稳定有效．该方法避免了驱动所有关节轴运动的方式,减小了机器人耦合带来的误差,同时缩短了激励轨迹参数优化时间,有效提高负载动力学参数的辨识效率和效果．     

洁净机器人反应转矩观测与碰撞保护研究

提出一种基于反应转矩观测器的机器人碰撞保护方法;机器人的反应转矩由基于模型的干扰观测器估值得到,模型的建立包括电机系统转矩模型与机器人系统动力学模型两部分;由于洁净机器人特殊的构型及关节耦合关系,机器人的动力学建模被大大简化,同时对简化后的模型采用最小二乘法对惯性参数和摩擦参数进行辨识,提高了模型的精度;根据永磁同步电机的转矩模型与机器人动力学模型,可以得到基于电机电流的机器人关节转矩;机器人与外界环境接触时,关节转矩的增加量即为反应转矩;通过设计反应转矩观测器并采用力/位混合控制结构,实现基于电流的机器人主动柔顺控制功能,并在洁净机器人进行碰撞保护实验,实际运行结果验证了该方法的有效性。     

基于DSP的欠驱动体操机器人的摇起控制设计

针对欠驱动机器人控制系统,给出一种基于DSP控制的类人形的三关节欠驱动体操机器人。首先以ADSP2181为核心设计出控制器,通过高速PCI总线与上位机PC通讯,采用直流电机伺服控制。然后依据建立的体操机器人动力学模型提出基于能量增加的正弦和斜坡函数输入方式,经对体操机器人作摇起控制实验,实验显示,设计的三关节欠驱动体操机器人控制系统满足实时性、稳定性和准确性要求。     

直流电机双闭环调速系统(DLM)的建模与辨识

运用特征分析和“类等效”建模的方法, 把直流电机双闭环调速系统的模型, 简化成一个简单的具有非线性特性的状态空间模型. 进而提出了通过实测系统的转速响应, 利用改进的遗传算法对该模型参数进行精确辨识的方法. 为检验方法的有效性, 对该模型和常用模型进行了对比性验证实验. 实验结果表明由此建模和参数辨识方法建立的模型结构合理, 参数辨识精度高, 可以很好的替代实际调速系统进行系统的设计.     

水下机器人参数辨识的量子粒子群算法

水下机器人动力学模型参数辨识是水下机器人运动状态控制、路径跟踪、状态监测、故障诊断及容错系统开发的基础,是水下机器人研究的核心内容之一.针对Falcon开架缆控水下机器人的动力学模型,将量子粒子群优化算法引入到水下机器人动力学模型参数辨识之中,提出基于量子粒子群优化算法(Quantum-behaved PSO,QPSO)的水下机器人动力学模型参数辨识,并将其辨识结果与粒子群优化算法(Particle Swarm Optimization,PSO)及遗传算法(GA)的辨识结果进行比较.仿真结果表明应用QPSO算法的参数辨识结果明显优于其它对比方法,说明了算法的有效性与合理性.     

面向直接示教的机器人负载自适应零力控制

相对于基于末端多维力传感器的直接示教工业机器人,基于关节扭矩传感器信息的机器人可接触范围大,且安全性更好.但在变负载的复杂工况下,加上受到机器人自身摩擦力、重力、惯性力的影响,示教效率受到制约.为此,本文提出了一种零力控制方法.首先,引入柔性关节机械臂的动力学模型作为被控对象,并分析了该模型中电机摩擦力、惯量以及机械臂重力在直接示教过程中的影响.然后,为精确补偿机器人自身的重力,基于QR分解与最小二乘理论进行参数辨识.而且针对机器人更换末端执行器或抓取物品后模型参数发生变化的情况,提出一种变负载自适应零力控制方法.最后,在自主研发的7自由度协作型机器人平台上进行了实验.机械臂自身重力参数辨识后的模型最大计算误差在关节额定力矩的4%以内,单关节力牵引实验中通过调节零力控制参数,牵引力矩可由约13 N·m下降到约2 N·m.变负载下的机器人牵引实验中,控制器能够在10 s内更新参数.实验结果表明,在负载变化的情况下,该控制方法可帮助操作者轻松地拖拽机器人进行精准、高效的直接示教.     

Swing-up control of a 3-DOF acrobot using an evolutionary approach

We present a control method for a 3-DOF acrobot which is a model of a gymnast on a horizontal bar with three links, two active joints, and a passive joint. This robot is a nonholonomic and underactuated system. We propose two control methods for the 3-DOF acrobot. First, swing-up control is performed by genetic programming (GP), and stabilizing control is handled by a linear quadratic regulator (LQR). GP can search widely for the optimum input torques for swing-up so that the acrobot is able to reach a near balancing point. The LQR is then switched on to stabilize the system. In the simulation results, the 3-DOF acrobot could swing up to the desired position, and the proposed method could control the acrobot effectively.     

Intelligent Control for an Acrobot

The acrobot is an underactuated two-link planar robot that mimics the human acrobat who hangs from a bar and tries to swing up to a perfectly balanced upside-down position with his/her hands still on the bar. In this paper we develop intelligent controllers for swing-up and balancing of the acrobot. In particular, we first develop classical, fuzzy, and adaptive fuzzy controllers to balance the acrobot in its inverted unstable equilibrium region. Next, a proportional-derivative (PD) controller with inner-loop partial feedback linearization, a state-feedback, and a fuzzy controller are developed to swing up the acrobot from its stable equilibrium position to the inverted region, where we use a balancing controller to catch and balance it. At the same time, we develop two genetic algorithms for tuning the balancing and swing-up controllers, and show how these can be used to help optimize the performance of the controllers. Overall, this paper provides (i) a case study of the development of a variety of intelligent controllers for a challenging application, (ii) a comparative analysis of intelligent vs. conventional control methods (including the linear quadratic regulator and feedback linearization) for this application, and (iii) a case study of the development of genetic algorithms for off-line computer-aided-design of both conventional and intelligent control systems.     

基于T-S模型的体操机器人系统模糊变结构控制

针对体操机器人（Acrobot）这类非线性系统,给出一种基于T—S模型的模糊变结构控制律设计．首先采用T—S模型建模,得到Acrobot的全局模糊模型;然后基于Lyapunov理论设计出保证Acrobot全局渐近稳定的模糊变结构平衡控制器．仿真结果表明,所设计的模糊变结构控制器与普通变结构控制器相比．可使Acrobot系统在垂直向上平衡点附近具有更大的吸引域和更强的鲁棒性．     

Inheritable genetic algorithm for biobjective 0/1 combinatorial optimization problems and its applications.

In this paper, we formulate a special type of multiobjective optimization problems, named biobjective 0/1 combinatorial optimization problem BOCOP, and propose an inheritable genetic algorithm IGA with orthogonal array crossover (OAX) to efficiently find a complete set of nondominated solutions to BOCOP. BOCOP with n binary variables has two incommensurable and often competing objectives: minimizing the sum r of values of all binary variables and optimizing the system performance. BOCOP is NP-hard having a finite number C(n, r) of feasible solutions for a limited number r. The merits of IGA are threefold as follows: 1) OAX with the systematic reasoning ability based on orthogonal experimental design can efficiently explore the search space of C(n, r); 2) IGA can efficiently search the space of C(n, r+/-1) by inheriting a good solution in the space of C(n, r); and 3) The single-objective IGA can economically obtain a complete set of high-quality nondominated solutions in a single run. Two applications of BOCOP are used to illustrate the effectiveness of the proposed algorithm: polygonal approximation problem (PAP) and the problem of editing a minimum reference set for nearest neighbor classification (MRSP). It is shown empirically that IGA is efficient in finding complete sets of nondominated solutions to PAP and MRSP, compared with some existing methods.     

Application of interactive genetic algorithm to fashion design

In general, computer-aided design support systems have got an approach of traditional artificial intelligence, which statistically analyzes data such as the behavior of designer, to extract formal design behavior. This approach, however, can neither deal with continuous change of fashion nor reflect personal taste well, as it just depends on large amount of collected data. To overcome this sort of problem interactive genetic algorithm (IGA) has been recently proposed, as a new trend of evolutionary computation. IGA uses human's response as fitness value when the fitness function cannot be explicitly defined. This enables IGA to be applied to artistic domains, and we propose a fashion design aid system using it. Unlike the previous works that attempt to model the dress design by several spline curves, the proposed system is based on a new encoding scheme that practically describes a dress with three parts: body and neck, sleeve, and skirt. By incorporating the domain-specific knowledge into the genotype, we could develop a more realistic design aid system for women’s dress. We have implemented the system with OpenGL and VRML to enhance the system interface. The experiments with several human subjects show that the IGA approach to dress design aid system is promising.     

抑制孤立簇的软件模块化优化算法

针对传统软件模块化指标在解决软件模块化问题时容易导致孤立簇的问题,提出改进型软件模块化指标IMQ作为进化算法的适应函数以有效抑制孤立簇现象,并以IMQ最大化为目标建立软件模块化的数学规划模型,设计符合问题特点的基于相似度竞争和选择机制的改进遗传算法（IGA）求解该模型。首先,运用边收缩方法的启发式策略生成高质量的初始解,并将其作为种子植入到初始种群中;然后,利用IGA对模型进行求解,在提升搜索效率的同时进一步提高解的质量;最后,运用真实数据和仿真数据进行对比实验。实验结果表明IMQ指标能有效减少孤立簇的数目,而IGA比传统的多点爬山算法（IHC）和基于分组编码（GNE）的遗传算法具有更强的寻优能力和鲁棒性。     

Motion planning and tracking control for an acrobot based on a rewinding approach

This paper concerns motion planning and tracking control for an acrobot. We first introduce an artificial friction torque in order to construct a downward trajectory, and rewind it to make an upward trajectory. Then, we combine the upward trajectory with a stabilizing trajectory to make a complete trajectory. Finally, we use the pole assignment method to design a tracking controller that makes the acrobot exponentially track the whole trajectory. This enables the acrobot to be swung up from the straight-down position and stabilized at the straight-up position. Unlike the most commonly used switching stabilization control methods, the strategy presented here features a single controller for motion control in the whole motion space. It is simple and efficient. Simulation results demonstrate the validity of the method.     

Acrobot动态伺服控制及其对称虚约束方法研究

研究了Acrobot这一垂直平面欠驱动机械臂的动态伺服控制问题. 该问题期望驱动Acrobot到达构形空间中任意目标位置. 由于Acrobot不能稳定在除平衡点外的位置, 因此考虑将系统镇定到经过目标点的周期轨道上. 利用虚约束来描述这样的轨道, 进而给出了在所选虚约束作用下系统的零动态和积分曲线. 接着设计了级联形式的控制器, 内环控制器基于改进的反馈线性化方法, 引入了一个使内环呈现二阶系统特性的虚拟输入, 在该虚拟输入的基础上, 设计了基于Lyapunov稳定性理论的外环控制器. 最后通过数字仿真证明所提出的方法合理有效, 并且获得比基于能量的动态伺服方法更优的结果.