蛇形机器人跟踪误差预测的自适应轨迹跟踪控制器

为了满足蛇形机器人轨迹跟踪运动的精度需要,消除外界干扰对机器人跟踪误差的影响,提出了一种蛇形机器人跟踪 误差预测的自适应轨迹跟踪控制器。 所提出的控制器实现了机器人干扰变量、摩擦系数和控制参数的预测,并用预测值和虚拟 控制函数来补偿系统的控制输入,抵消了蛇形机器人在轨迹跟踪过程中的侧滑角,避免了干扰变量对机器人带来的负面影响, 提高了轨迹跟踪的误差稳定性与控制精度。 在建立蛇形机器人模型后,利用积分形式的侧滑角补偿项改进了视线法,并设计了 蛇形机器人的自适应轨迹跟踪控制器。 使机器人的位置误差在 10 s 内实现收敛,角度误差小于 0. 03 rad,预测值误差在 5 s 内 收敛。 通过仿真实验,验证了所提出的控制器的有效性和优越性。     

一种基于交叉耦合控制的串联机器人笛卡尔空间轨迹跟踪方法

针对串联机器人在笛卡尔空间轨迹跟踪问题,将交叉耦合策略和机器人运动学结合起来,设计了机器人在笛卡尔空间位置控制中耦合误差的一般性构建方法,进而提出了一种新型脱离动力学模型的控制算法.该算法不仅能保证单个关节的运动稳定,同时还可以保证所有的关节运动协调,使位置误差和耦合误差都收敛到零.最后,设计了基于HIT/DLR灵巧手笛卡尔空间位置控制实验,验证了该方法的有效性.     

Trajectory planning and tracking control of a ground mobile robot:A reconstruction approach towards space vehicle

With the development of the similarity calculation method, the orbital motion of space vehicle can be translated into a sequence of waypoints that reflect position and velocity on the ground. In this paper, a motion control system is proposed to make the mobile robot pass through the desired waypoints for reconstructing the orbital motion. First, the parameterized trajectory optimization method is applied to generate a curvature-continuous trajectory from the waypoints, the position and velocity demands are presented as the equality constraints. Virtual positions are introduced to reduce the oscillation, and the total execution time of the whole trajectory is selected as the optimization parameter to reduce the computational burden. Then, an equivalence transformation is provided to translate the error system into an affine form, which is beneficial for the feedback controller design. Based on this, a nonlinear trajectory tracking controller is proposed, which includes a feedforward controller and an error feedback controller, and its exponential stability is proved using Persistency of Excitation Lemma. In addition, a shunting neural dynamics model is employed to avoid sharp velocity jumps. Finally, the performed experiments verify the effectiveness of the proposed method.     

Robust control for a biaxial servo with time delay system based on adaptive tuning technique

A robust control method for synchronizing a biaxial servo system motion is proposed in this paper. A new network based cross-coupled control and adaptive tuning techniques are used together to cancel out the skew error. The conventional fixed gain PID cross-coupled controller (CCC) is replaced with the adaptive cross-coupled controller (ACCC) in the proposed control scheme to maintain biaxial servo system synchronization motion. Adaptive-tuning PID (APID) position and velocity controllers provide the necessary control actions to maintain synchronization while following a variable command trajectory. A delay-time compensator (DTC) with an adaptive controller was augmented to set the time delay element, effectively moving it outside the closed loop, enhancing the stability of the robust controlled system. This scheme provides strong robustness with respect to uncertain dynamics and disturbances. The simulation and experimental results reveal that the proposed control structure adapts to a wide range of operating conditions and provides promising results under parameter variations and load changes.     

实时修正偏移量的循迹机器人控制系统研究

提出一种循迹机器人控制系统,通过实时修正机器人坐标与理想运动轨迹之间的偏移,实现机器人快速、有效的循迹。首先,采用粒子群优化算法规划理想运动轨迹;其次,根据正交落地码盘与陀螺仪实时获取机器人坐标及运动轨迹,经与线性化的理想运动轨迹比对,确定偏移量及方向;最后,将偏移量修正转化为机器人运动速度控制,从而实现了高精度闭环运动控制。实验结果表明,基于该控制系统搭建的机器人平台,线性化的理想轨迹和实际轨迹比对的最大偏差值为50.0 mm,在此偏差值内,实时修正偏移量,机器人能够自动回到线性化的理想轨迹上运行,且运行平稳、舒展,完全保证了机器人所要求完成的终点任务,与现有循迹机器人控制方法相比,提出的循迹机器人控制系统实现了较高精度的循迹与定位。     

APPROACH TO IMPROVEMENT OF ROBOT TRAJECTORY ACCURACY BY DYNAMIC COMPENSATION

Some dynamic factors, such as inertial forces and friction, may affect the robot trajectory accuracy. But these effects are not taken into account in robot motion control schemes. Dynamic control methods, on the other hand, require the dynamic model of robot and the implementation of new type controller. A method to improve robot trajectory accuracy by dynamic compensation in robot motion control system is proposed. The dynamic compensation is applied as an additional velocity feedforward and a multilayer neural network is employed to realize the robot inverse dynamics. The complicated dynamic parameter identification problem becomes a learning process of neural network connecting weights under supervision. The finite Fourier series is used to activate each actuator of robot joints for obtaining training samples. Robot control system, consisting of an industrial computer and a digital motion controller, is implemented. The system is of open architecture with velocity feedforward function. The proposed m     

大范围平动并联机器人运动学解耦与速度自适应规划

并联机器人是一种多支链、多关节、强耦合非线性系统,具有高速、高刚度和大负载等明显优势而被广泛应用到工业领域.然而,随着关节数量的增加导致该类机器人运动学解耦和高精度平稳控制的难度较大.为实现大范围平动3-RRRU并联机器人自动化轨迹跟踪和控制的平稳性,针对运动学解耦和速度自适应规划方法展开了系统、深入地研究.首先,应用...     

Iterative tuning of decoupling controller for 3-DOF precision motion stage

A practical iterative tuning algorithm of a decoupling controller is presented to eliminate intrinsic coupling among 3-DOF precision motion stage. General decoupling control cannot eliminate coupling completely, which will prevent the enhancement of control accuracy in high-precision motion system. The proposed algorithm can be used to tune the parameters of a decoupling controller iteratively through minimizing a quadratic cost function of the tracking error in non-movement direction when the stage moves in one direction. The tuning algorithm addressed for the motion stage needs only measurement signals in an actual motion system rather than a detailed model of the stage. The proposed algorithm is demonstrated by experimental results. It can be applied further in other multi-DOF motion systems.     

[误差建模及精度保证方法]基于稳定裕度的机床轮廓控制器增益设计方法

机床轮廓控制器通常被设计为P控制器,其控制增益是决定控制效果的关键参数。为定量设计轮廓控制器增益值,提出了基于稳定裕度的控制器增益设计方法。首先基于最小二乘法辨识机床各轴的离散传递函数模型,然后根据传递函数模型及Jury准则确定增益的稳定域,最后根据稳定裕度构造目标函数,并在稳定域内求解满足稳定裕度要求的轮廓控制器增益值。在三轴机床上设计轮廓控制器,实施轨迹联动实验,以验证方法的有效性。实验结果表明,基于稳定裕度设计的轮廓控制器可在保证机床运动平稳性的条件下使螺旋形轨迹的轨迹误差最大值减小39.06%,扇形轨迹的轨迹误差最大值减小少34.33%。     

基于光学动作捕捉的室内移动机器人轨迹跟踪

以基于麦克纳姆轮的全向移动机器人为研究对象,首先对基于标记点的光学动作捕捉系统的定位原理进行研究,然后对移动机器人的驱动原理和位姿误差进行分析,建立运动学模型和位姿误差模型,通过反演法设计虚拟反馈,并结合李雅普诺夫函数构造出具有全局渐近稳定的轨迹跟踪控制器。接着对测试环境和试验样机进行搭建,将光学动作捕捉系统采集的位姿信息反馈到机器人的控制回路中,最后对直线和圆周轨迹进行跟踪仿真。通过在试验样机上实验验证,总结出利用光学动作捕捉系统对移动机器人采集的定位信息,可令移动机器人拥有良好的轨迹跟踪性能。     

Robust disturbance rejection control of a biped robotic system using high-order extended state observer

This study addressed the problem of robust control of a biped robot based on disturbance estimation. Active disturbance rejection control was the paradigm used for controlling the biped robot by direct active estimation. A robust controller was developed to implement disturbance cancelation based on a linear extended state observer of high gain class. A robust high-gain scheme was proposed for developing a state estimator of the biped robot despite poor knowledge of the plant and the presence of uncertainties. The estimated states provided by the state estimator were used to implement a feedback controller that was effective in actively rejecting the perturbations as well as forcing the trajectory tracking error to within a small vicinity of the origin. The theoretical convergence of the tracking error was proven using the Lyapunov theory. The controller was implemented by numerical simulations that showed the convergence of the tracking error. A comparison with a high-order sliding-mode-observer-based controller confirmed the superior performance of the controller using the robust observer introduced in this study. Finally, the proposed controller was implemented on an actual biped robot using an embedded hardware-in-the-loop strategy.     

Finite-time sliding surface constrained control for a robot manipulator with an unknown deadzone and disturbance

This paper presents finite-time sliding mode control (FSMC) with predefined constraints for the tracking error and sliding surface in order to obtain robust positioning of a robot manipulator with input nonlinearity due to an unknown deadzone and external disturbance. An assumed model feedforward FSMC was designed to avoid tedious identification procedures for the manipulator parameters and to obtain a fast response time. Two constraint switching control functions based on the tracking error and finite-time sliding surface were added to the FSMC to guarantee the predefined tracking performance despite the presence of an unknown deadzone and disturbance. The tracking error due to the deadzone and disturbance can be suppressed within the predefined error boundary simply by tuning the gain value of the constraint switching function and without the addition of an extra compensator. Therefore, the designed constraint controller has a simpler structure than conventional transformed error constraint methods and the sliding surface constraint scheme can also indirectly guarantee the tracking error constraint while being more stable than the tracking error constraint control. A simulation and experiment were performed on an articulated robot manipulator to validate the proposed control schemes.     

A novel auto-tuning PID control mechanism for nonlinear systems

In this paper, a novel Runge–Kutta (RK) discretization-based model-predictive auto-tuning proportional-integral-derivative controller (RK-PID) is introduced for the control of continuous-time nonlinear systems. The parameters of the PID controller are tuned using RK model of the system through prediction error-square minimization where the predicted information of tracking error provides an enhanced tuning of the parameters. Based on the model-predictive control (MPC) approach, the proposed mechanism provides necessary PID parameter adaptations while generating additive correction terms to assist the initially inadequate PID controller. Efficiency of the proposed mechanism has been tested on two experimental real-time systems: an unstable single-input single-output (SISO) nonlinear magnetic-levitation system and a nonlinear multi-input multi-output (MIMO) liquid-level system. RK-PID has been compared to standard PID, standard nonlinear MPC (NMPC), RK-MPC and conventional sliding-mode control (SMC) methods in terms of control performance, robustness, computational complexity and design issue. The proposed mechanism exhibits acceptable tuning and control performance with very small steady-state tracking errors, and provides very short settling time for parameter convergence.     

轮式移动机器人轨迹跟踪控制算法的研究

为解决轮式移动机器人平面运动控制问题,将平面几何理论应用于轨迹跟踪控制中。建立了机器人平面运动模型,并以此为基础研究了直线和圆弧的轨迹跟踪控制算法;提出了导航圆方法,将机器人实时位置信息和目标轨迹之间的角度偏差及位置偏差综合成一个角度,然后对该角度进行了PID调节;在实验中,将直线、圆弧轨迹跟踪算法实际运用于机器人的运动控制。研究结果表明,该算法能将机器人轨迹的偏差有效地控制在±1 cm以内。     

考虑外部扰动的四轮移动机器人运动轨迹控制优化方法

控制机器人的运动轨迹，有助于提高四轮移动机器人工作的整体性能，提出考虑外部扰动的四轮移动机器人运动轨迹控制优化方法，利用人工势场法根据机器人运动特点规划其运动轨迹。考虑外部扰动和不确定性因素，结合低通滤波器和滑膜控制器设计运动轨迹控制器，实现四轮移动机器人运动轨迹的优化控制。实验结果表明，所提方法的轨迹控制效果好，距离误差小，稳定性高。     

基于模糊神经网络的不确定机器人实时轨迹跟踪控制的研究

针对机器人建模的不精确性以及扰动的存在给机器人控制增加难度的问题,提出了一种基于模糊神经网络的不确定机器人实时轨迹跟踪控制方法。该控制方法的控制器由模糊神经网络(FNN)控制器和CMAC控制器组成,FNN控制器代替传统的计算力矩法,CMAC控制器在线补偿控制误差,有效补偿机器人存在的各种不确定性。对二自由度机器人的仿真结果表明了所提出的控制方法的可行性。     

基于优化ADRC的单臂机器人轨迹跟踪研究

针对机器人在不确定环境下受到内外界挠动的影响,末端执行器不能跟踪已规划的轨迹运行,以Kinova MICO2机器人为例,研究轨迹自抗挠跟踪模型。建立了机器人关节空间运动模型,根据挠动特点构造了二阶ADRC框架模型,解决了末端执行器偏离规划轨迹的问题。分析了机器人ADRC参数对轨迹跟踪的意义,为了确保系统跟踪的动态稳定性,建立GA-RBF网络优化了原始ADRC。最后分析轨迹跟踪前后的离散点绝对误差,验证机器人轨迹跟踪的可靠性。实验结果表明,GA-RBF优化的ADRC使得机器人实际轨迹能较好收敛于理想的规划轨迹,为工业应用中对轨迹要求较高的任务提供算法参考。     

Adaptive robust motion trajectory tracking control of pneumatic cylinders with LuGre model-based friction compensation

Friction compensation is particularly important for motion trajectory tracking control of pneumatic cylinders at low speed movement. However, most of the existing model-based friction compensation schemes use simple classical models, which are not enough to address applications with high-accuracy position requirements. Furthermore, the friction force in the cylinder is time-varying, and there exist rather severe unmodelled dynamics and unknown disturbances in the pneumatic system. To deal with these problems effectively, an adaptive robust controller with LuGre model-based dynamic friction compensation is constructed. The proposed controller employs on-line recursive least squares estimation（RLSE） to reduce the extent of parametric uncertainties, and utilizes the sliding mode control method to attenuate the effects of parameter estimation errors, unmodelled dynamics and disturbances. In addition, in order to realize LuGre model-based friction compensation, the modified dual-observer structure for estimating immeasurable friction internal state is developed. Therefore, a prescribed motion tracking transient performance and final tracking accuracy can be guaranteed. Since the system model uncertainties are unmatched, the recursive backstepping design technology is applied. In order to solve the conflicts between the sliding mode control design and the adaptive control design, the projection mapping is used to condition the RLSE algorithm so that the parameter estimates are kept within a known bounded convex set. Finally, the proposed controller is tested for tracking sinusoidal trajectories and smooth square trajectory under different loads and sudden disturbance. The testing results demonstrate that the achievable performance of the proposed controller is excellent and is much better than most other studies in literature. Especially when a 0.5 Hz sinusoidal trajectory is tracked, the maximum tracking error is 0.96 mm and the average tracking error is 0.45 mm. This paper constructs an adaptive robust controller     

一种基于RBF补偿的自适应复合同步控制

针对Linapod并联机器人的同步轨迹跟踪问题,提出一种新的自适应复合同步控制策略。以Linapod 机构为研究对象,设计了一种将前馈控制、PD控制以及RBF适配器补偿相结合的复合同步控制策略,并根据李雅普洛夫稳定性条件给出了RBF适配器权值更新率,从而保证了控制系统的运动稳定性。研究结果表明,在该控制器的作用下,当向各轴加入单位正弦信号干扰和不加入干扰时,各轴跟踪误差都能小于0.003 mm,从而验证了该控制策略的有效性和正确性。     

机器人关节跟踪带死区的变参数EW-RLS算法研究

提出一种带死区的变参数EW-RLS算法,提出误差级的概念,并给出基于鲁棒极小极大原理选择误差级的准则。在机器人关节上进行的实验验证了该改进算法的良好性能。利用该算法,用最优PID控制器进行机器人轨迹跟踪研究。