In the actual production and life process, there are a plenty of time-varying linear systems. In this paper, ILC is extended to linear time-varying systems, and the proposed ILC update law is adopted to update the time-varying model, and the convergence performance of the algorithm is proved. Simpson integral method has an advantage in calculating the variable parameters to solve time-varying systems. Combining the extreme learning machine(ELM) in machine learning with ILC, the regression technology is employed to train the model to update the time-varying model parameters over time. Finally, the feasibility of the proposed algorithm is demonstrated by simulation.

The hexapod robot is widely used for outdoor missions due to its superior traversability. At present, hexapod robots use external sensors to obtain environmental information, but external sensors are susceptible to natural factors such as illumination, which will lead to sensor failure in serious cases. This paper investigates the normal movement of hexapod robot in unstructured environment solely relying on internal sensors. The information obtained from internal sensors meets minimum requirements for normal locomotion of hexapod robot, just like walking with closed eyes for human beings. This method uses deep reinforcement learning to train the locomotion strategy of hexapod robot in simulation environment, which enables hexapod robot to walk steadily at a fixed speed in an unstructured environment and has certain obstacle avoidance ability.

An iterative learning fault-tolerant control method is designed for an actuator fault intermittent process with simultaneous uncertainties for the system parameters. First, an intermittent fault tolerance controller is designed using 2D system theory, and the iterative learning control (ILC) intermittent process is transformed into a 2D Roesser model. Secondly, sufficient conditions for the controller’s existence are analyzed using the linear matrix inequality (LMI) technique, and the control gain matrices are obtained by convex optimization with LMI constraints. Under these conditions for all additive uncertainties for the system parameters and admissible failures, the controller can ensure closed-loop fault-tolerant performance in both the time and batch directions, and it can also meet the H∞ robust performance level against outside disturbances. Eventually, the algorithm’s computational complexity is analyzed, and the effectiveness of the algorithm is verified by simulation with respect to an injection molding machine model. Compared with traditional ILC laws, which do not consider actuator faults, the proposed algorithm has a better convergence speed and stability when the time-invariant and time-variant actuator faults occur during implementation.

The current research on iterative learning control focuses on the condition where the system relative degree is equal to 1, while the condition where the system relative degree is equal to 0 or greater than 1 is not considered. Therefore, this paper studies the monotonic convergence of the corresponding dynamic iterative learning controller systematically for discrete linear repetitive processes with different relative degrees. First, a 2D discrete Roesser model of the iterative learning control system is presented by means of 2D systems theory. Then, the monotonic convergence condition of the controlled system is analyzed according to the stability theory of linear repetitive process. Furthermore, the sufficient conditions of the controller existence are given in linear matrix inequality format under different relative degrees, which guarantees the system dynamic performance. Finally, through comparison with static controllers under different relative degrees, the simulation results show that the designed schemes are effective and feasible.

针对不同相对度的离散线性重复过程,研究有限频域范围的动态迭代学习控制问题.对于零相对度和高相对度的控制对象,结合二维(2D)系统理论,分别设计有限频域的动态迭代学习控制器;然后,运用广义Kalman-Yakubovich-Popov(KYP)引理,以线性矩阵不等式(LMI)的形式给出控制器存在的充分条件以及控制器的增益矩阵;最后,在弹簧阻尼系统和桁架机器人模型的仿真中,与静态迭代学习控制算法进行比较,验证所提算法的优越性和可行性.

This paper develops the systematic procedure for designing of iterative learning control (ILC) algorithms through the differential repetitive process setting. This means that the proposed approach can be directly applied to plants with differential dynamics and allows to satisfy the additional requirements on the resulting dynamics. In particular, the proposed design procedure enforces a required frequency attenuation over a finite frequency range and includes regional pole constraints. Additionally, an important result extension to the plants with relative degree greater than unity is presented. The sufficient conditions for the existence of the controllers are derived in terms of linear matrix inequalities, which are immediately extended to deal with time varying uncertainties. Finally, the simulations for a typical actuator of tracking servo system prove that the design is effective and brings some advantages when compared to the existing alternatives.