自由下落猫的非完整运动规划最优控制研究

研究猫在自由下落时姿态运动规划问题．自由落体的猫在空中转体运动由于角速度不可积,姿态运动方程呈现为非完整形式．当系统角动量为0时,导出由两个对称刚体组成的自由下落猫的非完整姿态运动方程．利用该非完整方程系统的控制问题可转化为无漂移系统的非完整运动规划问题．基于Ritz近似理论,给出自由落体猫姿态运动规划的Gauss-Newton算法．最后对自由落体猫作了数值仿真实验,仿真结果验证了该算法的有效性．     

航天器太阳帆板展开过程最优控制的适应Gauss伪谱法

研究了自适应Gauss伪谱法解决太阳帆板展开过程中航天器姿态最优控制的问题.由于帆板展开过程在工程应用中存在控制受限、状态受限、航天器姿态初始和终端状态受限等约束条件,航天器姿态控制问题可以看作是满足上述约束条件和边界条件,同时实现性能指标函数最优的最优控制问题.采用自适应Gauss伪谱法,通过判断时间区间是否需要细分及时间区间上节点数量是否需要增加,最大限度地提高计算效率,得到满足精度要求的帆板展开最优控制问题的解.最后对太阳帆板展开过程进行仿真,获得较好的姿态运动优化轨线及控制规律,论证了该方法在姿态控制问题中的有效性.     

航天器太阳帆板展开过程最优控制的自适应Gauss伪谱法

研究了自适应Gauss伪谱法解决太阳帆板展开过程中航天器姿态最优控制的问题.由于帆板展开过程在工程应用中存在控制受限、状态受限、航天器姿态初始和终端状态受限等约束条件,航天器姿态控制问题可以看作是满足上述约束条件和边界条件,同时实现性能指标函数最优的最优控制问题.采用自适应Gauss伪谱法,通过判断时间区间是否需要细分及时间区间上节点数量是否需要增加,最大限度地提高计算效率,得到满足精度要求的帆板展开最优控制问题的解.最后对太阳帆板展开过程进行仿真,获得较好的姿态运动优化轨线及控制规律,论证了该方法在姿态控制问题中的有效性.     

基于虚拟完整约束的欠驱动起重机控制方法

欠驱动系统的控制是非线性控制的一个重要领域,欠驱动系统指系统控制输入个数小于自由度个数的非线性系统.目前,欠驱动非线性系统动力学和控制研究的主要方法包括线性二次型最优控制方法和部分反馈线性化方法等,如何使系统持续的稳定在平衡位置一直是研究的难点.虚拟约束方法是指通过选择一个周期循环变化的变量作为自变量来设计系统的周期运动.该文以典型的欠驱动模型起重机为例,采用虚拟约束方法,使系统能够在平衡位置稳定或周期振荡运动.首先,通过建立虚拟约束,减少系统自由度变量;然后,通过部分反馈线性化理论推导出系统的状态方程;最后,通过线性二次调节器设计反馈控制器.仿真结果表明,重物在反馈控制下可以在竖直位置的附近达到稳定状态,反映了虚拟约束方法对欠驱动系统的有效性.     

基于小波分析的空间机械臂运动规划的最优控制

讨论了空间机械臂系统非完整运动规划的最优控制问题.利用小波分析方法,将离散正交小波函数引入最优控制,由小波级数展开式逼近替代传统的Fourier基函数,提出基于小波分析的最优控制数值算法.仿真结果表明,该方法对求解空间机械臂非完整运动规划问题是有效的.     

一种新型四阶S型轨迹规划算法的研究与应用北大核心CSCD

提出了一种四阶S型运动轨迹规划的新方法,根据给定参数计算得到最大速度三阶导数作用时间,最大加加速度作用时间和最大加速度作用的时间,通过循环计算的方式得到加速阶段所运动的距离与速度,以加速阶段所达到的速度作为最大速度计算得到最大速度作用的时间.此时,由浮点型转整型数据所产生的误差也可以在其最大速度的匀速阶段得到补偿,最后,减速阶段的数据由S曲线加减速控制中速度曲线的对称性得到.该算法在基于倍福PLC的三动子运料平台中进行了验证,实验表明,提出的S型运动轨迹规划算法在不用过多保存曲线数据的情况下也能获得平滑的速度和加速度,有效地提高了系统的柔性,同时简化了算法的实现,大大地节省了PLC的资源.     

基于Udwadia-Kalaba方法的并联机器人鲁棒伺服约束控制

针对2自由度冗余驱动并联机器人轨迹跟踪控制问题,提出了一种基于Udwadia Kalaba方程的鲁棒伺服控制方法.在负载、外部干扰以及制造误差的影响下,无法得到机器人精确、完整的运动模型,导致机器人控制性能变差.为解决这类不确定性带来的影响,提出了一种鲁棒控制方法.该方法通过保证系统的一致有界性和一致最终有界性,使系统能够精确跟踪理想约束轨迹.此外,该方法采用Udwadia Kalaba方程,求解控制过程中满足系统理想约束所需要的约束力.Udwadia Kalaba方程不需要Lagrange乘子或伪广义速度等辅助变量,可以同时处理完整约束和非完整约束,且可以获得满足轨迹约束的约束力解析解.利用Lyapunov函数对该鲁棒控制方法的稳定性进行了理论证明,并且通过仿真实验,验证了该鲁棒控制方法能够在非理想条件下实现给定轨迹的高精度跟踪控制.     

面向节能的单/多列车优化决策问题

主要研究了单/多列车运行优化控制问题.首先建立了面向节能的单列车能耗优化模型,采用节能控制策略对模型进行求解,得出节能运行的速度距离曲线;其次对多列车多区段的节能运行进行优化控制设计,以再生能量利用最大化为目标,分别建立多列车能耗优化通用模型和高峰/非高峰情形下优化模型,利用模拟退火算法求解模型,得出使总能耗最低的列车运行方案;最后针对晚点情况下追踪运行的多列车运行优化控制问题,分别建立随机和非随机晚点情况下实时控制模型,求解得到耗能最少的列车运行曲线.     

自由漂浮空间非合作目标的运动预测

空间非合作目标的运动预测是航天器在轨服务中的一个重要问题。在获得非合作目标的运动预测结果后,追踪星即可规划运动轨迹以接近目标并对其进行捕获。该文提出了一种自由漂浮空间非合作目标的运动预测方法。该方法的核心思想是首先辨识出目标的姿态动力学参数和目标的质心运动学参数,然后利用参数辨识结果和目标的动力学方程实现对目标的运动预测。在姿态动力学参数的辨识过程中,首先对目标的惯性参数进行初步辨识,然后采用自适应无迹Kalman滤波器对姿态动力学参数进行粗略辨识,最后通过最优化方法进一步提高姿态动力学参数的辨识精度。该文通过数值仿真验证了所提运动预测方法的有效性。仿真结果表明,无论目标是做单轴旋转还是翻滚运动,所提运动预测方法都能够实现对目标的长时间高精度的运动预测。     

一种Jerk平滑的非对称正弦轨迹规划设计方法

为了减小运动控制中三角函数类型轨迹规划在减速段产生的振动和冲击,文章提出了使减速段Jerk平滑过渡的非对称正弦轨迹规划的系统的设计方法.首先,根据S曲线的闭合形式非对称设计方法,将其在正弦算法中实现;然后,针对正弦加加速度在减速段存在突变的问题进行优化,并提出不同目标行程下确定运动参数的方法;最后,通过频域分析和仿真实...     

Multi-objective Optimization of Zero Propellant Spacecraft Attitude Maneuvers

The zero propellant maneuver (ZPM) is an advanced space station, large angle attitude maneuver technique, using only control momentum gyroscopes (CMGs). Path planning is the key to success, and this paper studies the associated multi-objective optimization problem. Three types of maneuver optimal control problem are formulated: (i) momentum-optimal, (ii) time-optimal, and (iii) energy-optimal. A sensitivity analysis approach is used to study the Pareto optimal front and allows the tradeoffs between the performance indices to be investigated. For example, it is proved that the minimum peak momentum decreases as the maneuver time increases, and the minimum maneuver energy decreases if a larger momentum is available from the CMGs. The analysis is verified and complemented by the numerical computations. Among the three types of ZPM paths, the momentum-optimal solution and the time-optimal solution generally possess the same structure, and they are singular. The energy-optimal solution saves significant energy, while generally maintaining a smooth control profile.     

Energy-Optimal Multi-Goal Motion Planning for Planar Robot Manipulators

In this work, the energy-optimal motion planning problem for planar robot manipulators with two revolute joints is studied, in which the end-effector of the robot manipulator is constrained to pass through a set of waypoints, whose sequence is not predefined. This multi-goal motion planning problem has been solved as a mixed-integer optimal control problem in which, given the dynamic model of the robot manipulator, the initial and final configurations of the robot, and a set of waypoints inside the workspace of the manipulator, one has to find the control inputs, the sequence of waypoints with the corresponding passage times, and the resulting trajectory of the robot that minimizes the energy consumption during the motion. The presence of the waypoint constraints makes this optimal control problem particularly difficult to solve. The mixed-integer optimal control problem has been converted into a mixed-integer nonlinear programming problem first making the unknown passage times through the waypoints part of the state, then introducing binary variables to enforce the constraint of passing once through each waypoint, and finally applying a fifth-degree Gauss–Lobatto direct collocation method to tackle the dynamic constraints. High-degree interpolation polynomials allow the number of variables of the problem to be reduced for a given numerical precision. The resulting mixed-integer nonlinear programming problem has been solved using a nonlinear programming-based branch-and-bound algorithm specifically tailored to the problem. The results of the numerical experiments have shown the effectiveness of the approach.     

具有外部扰动的空间刚性机械臂姿态与末端爪手协调运动的Terminal滑模控制算法设计

讨论了载体位置无控、姿态受控情况下,具有外部扰动的漂浮基空间刚性机械臂,载体姿态与末端爪手协调运动的控制算法设计问题．结合系统动量守恒关系及Lagrange方法,建立了漂浮基空间刚性机械臂完全能控形式的系统动力学方程及运动Jacobi关系,并将其转化为状态空间形式的系统控制方程．以此为基础,根据Terminal滑模控制技术,给出了系统相关Terminal滑模面的数学表达式,在此基础上提出了具有外部扰动情况下漂浮基空间刚性机械臂载体姿态与末端爪手协调运动的Terminal滑模控制方案．提出的控制方案不但确保了闭环系统滑模阶段的存在性,同时通过Terminal滑模函数的适当选取,还保证了输出误差在有限时间内的收敛性．此外,由于确保了无论何种情况下系统初始状态均在Terminal滑模面上,从而消除了其它滑模控制方法常有的到达阶段,使得闭环系统具有全局鲁棒性和稳定性．平面两杆空间刚性机械臂的系统数值仿真,证实了方法的有效性．     

A new approach to the incorporation of servo constraints in multibody dynamics

A new index reduction approach is developed to solve the servo constraint problems [2] in the inverse dynamics simulation of underactuated mechanical systems. The servo constraint problem of underactuated systems is governed by differential algebraic equations (DAEs) with high index. The underlying equations of motion contain both holonomic constraints and servo constraints in which desired outputs (specified in time) are described in terms of state variables. The realization of servo constraints with the use of control forces can range from orthogonal to tangential [3]. Since the (differentiation) index of the DAEs is often higher than three for underactuated systems, in which the number of degrees of freedom is greater than the control outputs/inputs, we propose a new index reduction method [1] which makes possible the stable numerical integration of the DAEs. We apply the proposed method to differentially flat systems, such as cranes [1,4,5], and non-flat underactuated systems. (© 2016 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim)     

Mitigating the curse of dimensionality: sparse grid characteristics method for optimal feedback control and HJB equations

We address finding the semi-global solutions to optimal feedback control and the Hamilton–Jacobi–Bellman (HJB) equation. Using the solution of an HJB equation, a feedback optimal control law can be implemented in real-time with minimum computational load. However, except for systems with two or three state variables, using traditional techniques for numerically finding a semi-global solution to an HJB equation for general nonlinear systems is infeasible due to the curse of dimensionality. Here we present a new computational method for finding feedback optimal control and solving HJB equations which is able to mitigate the curse of dimensionality. We do not discretize the HJB equation directly, instead we introduce a sparse grid in the state space and use the Pontryagin’s maximum principle to derive a set of necessary conditions in the form of a boundary value problem, also known as the characteristic equations, for each grid point. Using this approach, the method is spatially causality free, which enjoys the advantage of perfect parallelism on a sparse grid. Compared with dense grids, a sparse grid has a significantly reduced size which is feasible for systems with relatively high dimensions, such as the 6-D system shown in the examples. Once the solution obtained at each grid point, high-order accurate polynomial interpolation is used to approximate the feedback control at arbitrary points. We prove an upper bound for the approximation error and approximate it numerically. This sparse grid characteristics method is demonstrated with three examples of rigid body attitude control using momentum wheels.     

Solving Optimal Control Problems by Exploiting Inherent Dynamical Systems Structures

Computing globally efficient solutions is a major challenge in optimal control of nonlinear dynamical systems. This work proposes a method combining local optimization and motion planning techniques based on exploiting inherent dynamical systems structures, such as symmetries and invariant manifolds. Prior to the optimal control, the dynamical system is analyzed for structural properties that can be used to compute pieces of trajectories that are stored in a motion planning library. In the context of mechanical systems, these motion planning candidates, termed primitives, are given by relative equilibria induced by symmetries and motions on stable or unstable manifolds of e.g. fixed points in the natural dynamics. The existence of controlled relative equilibria is studied through Lagrangian mechanics and symmetry reduction techniques. The proposed framework can be used to solve boundary value problems by performing a search in the space of sequences of motion primitives connected using optimized maneuvers. The optimal sequence can be used as an admissible initial guess for a post-optimization. The approach is illustrated by two numerical examples, the single and the double spherical pendula, which demonstrates its benefit compared to standard local optimization techniques.