| 1. | The accurate tracking of a series of desired trajectory is achieved by extension to a class of nonlinear system
并且推广到一类非线性系统的跟踪控制，实现了对一列期望轨迹的精确跟踪。 |
| 2. | The controller designed guarantees the semiglobal stability of the closed - loop system and the semiglobal output tracking of given desired trajectory
所设计的控制器不仅保证闭环系统半全局稳定而且使得输出能够半全局跟踪期望轨迹。 |
| 3. | Based on the results and with the augmentation approach , an adaptive control scheme for space robot system with unknown payload parameters to track the desired trajectory in joint space is developed
该系统动力学方程的优点是关于一组组合惯性参数能保持惯常的线性函数关系。 |
| 4. | The proposed control law can guarantee that the mobile robot will track the desired trajectory in the global sense as well as in finite time when the desired rotate velocity is a nonzero constant
使得对角速度为非零常数的期望轨迹，非完整移动机器人能够实现全局跟踪，并能在有限时间内完全跟踪上期望轨迹。 |
| 5. | The problem to improving the rate of convergence and the accuracy of tracking of ilc for deterministic linear systems is considered , in the meanwhile , the effects of the plant characteristics , various types of disturbances , errors in initial conditions and the " slowly " varying desired trajectories on the convergence and performance of ilc for uncertain linear and nonlinear systems are also investigated
针对确定的线性系统，主要研究能够提高算法的收敛速度和跟踪精度的迭代学习控制技术；针对不确定的线性和非线性系统，主要考虑系统的特性、各种干扰、初始状态偏移和不确定的未建模动态以及缓慢变化的期望轨迹对迭代学习控制过程收敛性和跟踪性能的影响。 |
| 6. | And i also propose a new method oftracking control . this method divides desired trajectory into many pieces and follows the pieces one by one . with the method , the calculation of curvature of tracking trajectory and complex calculating process can be avoided , and the performance of real - time and precision can be improved
在跟踪方面，本文提出了将被跟踪路径分成若干微小线段，应用以微小直线段代替曲线的方法，然后分段跟踪，并给出了这种方法的稳定性证明，仿真结果证明了其有很好的跟踪效果。 |
| 7. | The very important works by the researchers in the modeling and control for flexible manipulators are introduced ; the equations of the non - holonomic constrain and dynamics are formulated . on the base of dynamic model function derived by the planar two - link flexible manipulator , the model function can be obtained by coordinate transformation ; at last the control scheme based on model of the system is put in to use to insure track desired trajectory of joint angle
在第四章中，首先，我们对柔性机械臂的动力学建模和系统控制问题做了比较全面的介绍；然后，运用lagrange方程并结合假设模态法建立了平面两杆柔性空间机械臂系统的动力学方程。在此基础上，通过坐标变换导出了以可测关节角为控制输出变量的系统控制模型，基于此控制模型提出了柔性空间机械臂关节运动的动力学优化控制方案。 |
| 8. | As neural network has the ability of self - learning , that utilizes prior output data of uncertain system to estimate iteratively the static state property of system in order to achieve ideal approaching precision for identification of the positive model , a robust iterative learning control scheme on the basis of better positive model is designed . the neural network is used to identify the positive model of nonlinear system on iterative axis , which can give feed - forward action of iterative learning controller to reduce the effects of nonlinear properties and model uncertainties . meanwhile , feedback action of iterative learning controller make joint movement follow the desired trajectory on time axis by using controlled parameters derived by the neural network
由于神经网络具有自学习能力，它可利用不确定性系统的历史输出数据对系统的稳态特性进行估计，使得对系统正向模型的辨识达到理想的逼近精度，然后在此正向模型的基础上进行学习控制律的设计：即采用神经网络辨识非线性系统的正向模型，并消除系统不确定性和外部干扰的影响，使关节运动沿迭代轴方向逼近期望轨迹；迭代学习控制器在线学习控制参量，使关节运动沿时间轴方向跟踪期望轨迹。 |
| 9. | Finally , for the definite mechanism and indefinite size of industrial robots ' executor as well as the size ^ optimization goal , the mathematic model is set up to optimize the executor ' size with using complex optimization method . next , for the optimized executor , the desired trajectory and planning goal , the kinematics model and proper referent and motive coordinates systems are set up . by making use of the result of the previous motion analysis , genetic algorithm is applied to the trajectory planning of executor
最后，对于结构给定而构件尺度未定的工业机器人执行机构，先根据机构尺度优化目标，建立数学模型、用复合形法进行构件尺度优化；再根据给定的期望轨迹和规划目标，建立数学模型，利用复指数变换法对执行机构进行运动分析的结果，采用进化算法对工业机器人进行轨迹规划。 |
| 10. | Iterative learning control ( ilc ) is a technique for improving the transient response performance of systems or processes that operate repetitively over a fixed time interval . it refines the next control input using the information such as current control input and error signals after each trial until the specified desired trajectory is followed to a high precision
迭代学习控制针对具有重复运行性质的被控对象，利用对象以前运行的信息，通过迭代的方式修正控制信号，实现在有限时间区间上的完全跟踪任务。 |
