Iterative Learning Control （ILC） captures interests of many scholars because of its capability of high precision control implement without identifying plant mathematical models, and it is widely applied in control engineering. Presently, most ILC algorithms still follow the original ideas of ARIMOTO, in which the iterative-learning-rate is composed by the control error with its derivative and integral values. This kind of algorithms will result in inevitable problems such as huge computation, big storage capacity for algorithm data, and also weak robust. In order to resolve these problems, an improved iterative learning control algorithm with fixed step is proposed here which breaks the primary thought of ARIMOTO. In this algorithm, the control step is set only according to the value of the control error, which could enormously reduce the computation and storage size demanded, also improve the robust of the algorithm by not using the differential coefficient of the iterative learning error. In this paper, the convergence conditions of this proposed fixed step iterative learning algorithm is theoretically analyzed and testified. Then the algorithm is tested through simulation researches on a time-variant object with randomly set disturbance through calculation of step threshold value, algorithm robustness testing,and evaluation of the relation between convergence speed and step size. Finally the algorithm is validated on a valve-serving-cylinder system of a joint robot with time-variant parameters. Experiment results demonstrate the stability of the algorithm and also the relationship between step value and convergence rate. Both simulation and experiment testify the feasibility and validity of the new algorithm proposed here. And it is worth to noticing that this algorithm is simple but with strong robust after improvements, which provides new ideas to the research of iterative learning control algorithms.
Variable pump driving variable motor(VPDVM) is the future development trend of the hydraulic transmission of an unmanned ground vehicle(UGV).VPDVM is a dual-input single-output nonlinear system with coupling,which is difficult to control.High pressure automatic variables bang-bang(HABB) was proposed to achieve the desired motor speed.First,the VPDVM nonlinear mathematic model was introduced,then linearized by feedback linearization theory,and the zero-dynamic stability was proved.The HABB control algorithm was proposed for VPDVM,in which the variable motor was controlled by high pressure automatic variables(HA) and the variable pump was controlled by bang-bang.Finally,simulation of VPDVM controlled by HABB was developed.Simulation results demonstrate the HABB can implement the desired motor speed rapidly and has strong robustness against the variations of desired motor speed,load and pump speed.
针对一种新型串并联双机器人联合作业系统的任务分配进行方法设计和整体优化。分别采用蚁群优化中的近似非确定性树搜索(Approximate nondeterministic tree search,ANTS)和最大最小蚂蚁系统(Max-min-ant-system,MMAS)作为任务分配的优化策略,并在MMAS中加入局部搜索以进一步优化路径构建过程中得到的局部最优解。仿真结果以及与之前相关研究成果的对比表明,MMAS在寻优过程中的迭代收敛速度优于ANTS,且经过一段时间的开发探索之后,获得的最优解的质量也比ANTS要好;MMAS与局部搜索相结合的方法比单独使用MMAS更加进一步提高了最终解的质量。进化曲线证明了算法对系统任务分配及优化的适应性和优越性。试验结果经与传统组合优化方法对比,进一步验证了算法的优化效果。
