翻滚目标逼近的虚拟域逆动力学轨迹规划

针对追踪星自主逼近和跟踪翻滚目标特定部位的最优规划问题,提出了一种基于虚拟域逆动力学的多约束最优逼近轨迹规划方法.首先,在翻滚目标本体系下建立追踪星相对于翻滚目标特定部位的相对轨道动力学方程,并建立追踪星本体系相对于翻滚目标期望固连坐标系的相对姿态动力学方程;其次,考虑目标星外形、敏感器视场和执行机构控制能力等约束条件,建立时间/能量最优规划模型;然后,采用序列二次规划(sequential quadratic programming,SQP)方法求解时间/能量最优规划问题;最后,数值仿真验证了该方法在满足多约束条件下,可实现对翻滚目标自主逼近与跟踪的最优轨迹规划,同时与高斯伪谱法进行了对比,验证了本方法在计算效率方面的优势.     

Shared control for lane keeping assistance system based on multiple-phase handling inverse dynamics

To achieve user-friendly design, a novel shared control scheme for Lane Keeping Assistance (LKA) system is proposed in this paper. Instead of adjusting the control authority allocation, the proposed scheme achieves shared control by adjusting the displacement interval between each phase. Through this scheme, we aim to design a LKA system which can realize continuously shared control between assistant controller and human driver with a fixed driving authority allocation strategy. Firstly, the shared control problem is formulated as a multiple-phase inverse dynamics problem; then we covert the inverse dynamics problem into an Optimal Control Problem (OCP). Secondly, by applying Radau Pseudospectral Method (RPM), the OCP is converted into a Nonlinear Programming Problem (NLP), which is then solved by Sequential Quadratic Programming (SQP). Finally, simulations under two typical conditions are implemented to validate the effectiveness of the proposed shared control approach.     

基于函数滑模控制器的机械手轨迹跟踪控制

提出一种基于函数滑模控制器(FSMC)的控制策略,用于不确定机械手的轨迹跟踪控制。首先,由动力学模型和滑模函数得到系统的不确定项;然后,利用RBF神经网络逼近系统不确定项,由于神经网络逼近存在误差,而且在初始阶段误差较大,设计函数滑模控制器和鲁棒补偿项对神经网络逼近误差进行补偿,以克服普通滑模控制器容易引起的抖振问题,同时提高系统的跟踪控制性能。基于李亚普诺夫理论证明了闭环系统的全局稳定性,仿真实验也验证了方法的有效性。     

基于行为动力学方法的移动机器人轨迹追踪

研究了基于行为动力学方法的移动机器人轨迹追踪。在总结行为动力学理论的基础上,根据轨迹追踪任务要求,确定航向角和速度作为行为变量,同时构建了接近吸引子动力学方程,并在考虑机器人与路径期望点之间距离这一间接耦合参数基础上,建立了速度动力学方程,并分析了该动力系统的收敛性。最后的仿真结果表明该方法正确、可行,且机器人能有效地完成追踪任务。     

On adaptive inverse dynamics control of rigid robots

The authors derive an adaptive inverse dynamics control law for rigid robots that overcomes the most restrictive difficulty of such schemes to date, namely, the requirement that the inverse of the estimated inertia matrix remain bounded. The control scheme still requires the joint accelerations for its implementation. In practice, one would implement this scheme by estimating the acceleration from the measured velocity. Good performance could still be expected without direct acceleration measurement, assuming sufficient encoder resolution and sufficiently fast sampling. Another practical advantage of the authors' scheme is that since the `nominal' inverse dynamics or inner loop portion of their control law is fixed, it is more amenable to the development of a dedicated interface and can be `burned' onto a chip to reduce online computation requirements     

Adaptive inverse dynamics control of robots with uncertain kinematics and dynamics

It has been about two decades since the first globally convergent adaptive tracking controller was derived for robots with dynamic uncertainties. However, not until recently has the problem of concurrent adaptation to both the kinematic and dynamic uncertainties found its solution. This adaptive controller belongs to passivity-based control. Though passivity-based controllers have many attractive properties, in general, they are not able to guarantee the uniform performance of the robot over the entire workspace. Even in the ideal case of perfect knowledge of the manipulator parameters, the closed-loop system remains nonlinear and coupled. Thus the closed-loop tracking performance is difficult to quantify, while the inverse dynamics controllers can overcome these deficiencies. Therefore, in this work, we will develop a new adaptive Jacobian tracking controller based on the inverse manipulator dynamics. Using the Lyapunov approach, we have proved that the end-effector motion tracking errors converge asymptotically to zero. Simulation results are presented to show the performance of the proposed controller.     

Momentum control with hierarchical inverse dynamics on a torque-controlled humanoid

Hierarchical inverse dynamics based on cascades of quadratic programs have been proposed for the control of legged robots. They have important benefits but to the best of our knowledge have never been implemented on a torque controlled humanoid where model inaccuracies, sensor noise and real-time computation requirements can be problematic. Using a reformulation of existing algorithms, we propose a simplification of the problem that allows to achieve real-time control. Momentum-based control is integrated in the task hierarchy and a LQR design approach is used to compute the desired associated closed-loop behavior and improve performance. Extensive experiments on various balancing and tracking tasks show very robust performance in the face of unknown disturbances, even when the humanoid is standing on one foot. Our results demonstrate that hierarchical inverse dynamics together with momentum control can be efficiently used for feedback control under real robot conditions.     

基于强化迭代学习的四旋翼无人机轨迹控制

为进一步提升在未知环境下四旋翼无人机轨迹的跟踪精度,提出了一种在传统反馈控制架构上增加迭代学习前馈控制器的控制方法。针对迭代学习控制（ILC）中存在的学习参数整定困难的问题,提出了一种利用强化学习（RL）对迭代学习控制器的学习参数进行整定优化的方法。首先,利用RL对迭代学习控制器的学习参数进行优化,筛选出当前环境及任务下最优的学习参数以保证迭代学习控制器的控制效果最优;其次,利用迭代学习控制器的学习能力不断迭代优化前馈输入,直至实现完美跟踪;最后,在有随机噪声存在的仿真环境中把所提出的强化迭代学习控制（RL-ILC）算法与未经参数优化的ILC方法、滑模变结构控制（SMC）方法以及比例-积分-微分（PID）控制方法进行对比实验。实验结果表明,所提算法在经过2次迭代后,总误差缩减为初始误差的0.2%,实现了快速收敛;并且与SMC控制方法及PID控制方法相比,RL-ILC算法在算法收敛后不会受噪声影响产生轨迹波动。由此可见,所提算法能够有效提高无人机轨迹跟踪的准确性和鲁棒性。     

四旋翼飞行器的轨迹跟踪抗干扰控制

针对四旋翼飞行器轨迹跟踪问题中系统存在模型不确定和易受到外界扰动的情况,提出了基于切换函数的扩张状态观测器设计方法来对系统中的扰动进行估计,并将估计值与滑模控制器的设计相结合,实现了对系统中非匹配不确定性和匹配不确定性的抑制且实现了系统跟踪误差的一致最终有界.首先,根据变量间的耦合关系将飞行器系统模型分解为两个子系统模型,设计扩张状态观测器对子系统中的非匹配不确定性进行估计,并将估计值作为变量加入到切换函数的设计中;进而基于切换函数设计扩张状态观测器以估计经切换函数重构系统中的扰动,并在控制器中对扰动进行补偿.最后通过李雅普诺夫理论证明了控制系统的稳定性.通过仿真验证了本文提出的方法能够有效实现飞行器轨迹跟踪控制且能够抑止传统滑模控制的抖振现象.     

基于非线性制导的四旋翼轨迹跟踪控制

四旋翼是一种欠驱动、强耦合的可垂直起降的飞行器,为了实现其能够以设定速度跟踪空间轨迹,设计了一种基于非线性制导算法的轨迹跟踪控制方法。该方法分为了导引与控制两部分组成,导引部分以任务轨迹与期望速度为输入量通过非线性制导算法输出当前四旋翼的期望加速度,控制部分以得到的期望加速度为输入量采用串级PID算法对四旋翼进行姿态控制,从而实现四旋翼保持设定速度对任务轨迹的跟踪。仿真结果表明,所提方法能够实现四旋翼对复杂任务轨迹的精确跟踪,二维复杂轨迹跟踪距离偏差不超过±0.6m,速度偏差不超过2m/s;三维复杂轨迹除了受自身控制力限制的飞行段外,跟踪距离偏差基本控制在±4m以内,速度偏差不超过2m/s。