Space-charge and injection limited current in organic diodes: A unified model

Space-charge and injection limited currents in organic diodes have been analyzed using a unified model. Both currents have been modeled using the transport equations combined with a proper value of the free charge density at the metal–organic interface. The method has been applied to diodes with different organic materials, metal contacts and lengths of the organic materials. This unified model accurately reproduces published current–voltage curves for a variety of diode structures operating at different temperatures and voltages. The results of the method, in the injection regime, have been compared with the results of pure injection models developed by other authors. The reduction of the computational time and the number of parameters in our model are important advantages of our procedure. Moreover, it is an alternative where the injection models start to fail: at low applied voltages, close to the Ohmic regime, and for low heights of the energy barrier at the interface. The treatment is complemented with a compact model that relates the current density j with the free charge-carrier density at the interface p_f(0): p_f(0) = K₁j^m + K₂, where the parameter K₁ depends on the barrier height at the interface, m depends on the organic material and K₂ controls the flat zone at low currents to include the dependence with thermal carriers and impurities.     

严格反馈非线性时变系统的迭代学习控制

针对一类含未知时变参数的严格反馈非线性系统, 提出一种实现有限作业区间轨迹跟踪控制的迭代学习算法. 基于Lyapunov-like方法设计控制器, 回避了常规迭代学习控制中受控系统非线性特性需满足全局Lipschitz连续条件的要求. 以反推设计(Backstepping)方法设计控制器, 为使得虚拟控制项可导, 引入一级数收敛序列; 将时变参数展开为有限项多项式形式, 在控制器设计中采取双曲正切函数处理余项对于系统跟踪性能的影响. 理论分析表明, 闭环系统所有信号有界, 并能够实现系统输出完全收敛于理想轨迹.     

含多状态时滞的连续时间迭代学习控制系统稳定性分析

探讨了含多状态时滞连续时间迭代学习控制系统的稳定性分析问题, 尤其是当系统参数带有多面体不确定性时的鲁棒稳定性分析问题. 通过引入一个扩展算子, 利用迭代学习控制中的二维分析方法给出了时滞系统整个学习动态过程的连续离散Roesser系统描述. 基于所得的Roesser系统, 首先利用二维系统理论给出了保证迭代学习控制系统渐近稳定的充要条件, 然后结合鲁棒H∞控制理论提出了以线性矩阵不等式形式描述的充分条件来保证迭代学习控制系统的单调收敛性. 结果表明, 通过求解线性矩阵不等式确定的学习增益可以使控制输入误差随着迭代次数的增加单调收敛于零. 仿真结果表明, 通过增加满足一组线性矩阵不等式条件的P型学习增益能够使得一个鲁棒渐近稳定的迭代学习控制方案变为鲁棒单调收敛的, 同时还可以大大提高收敛速率.     

H∞ iterative learning controller design for a class of discrete-time systems with data dropouts

In this paper, the issue of H_∞ iterative learning controller design is considered for a class of discrete-time systems with data dropouts. With the super-vector formulation of iterative learning control (ILC), such a system can be formulated as a linear discrete-time stochastic system in the iteration domain, and then a sufficient condition guaranteeing both stability of the ILC process and the desired H_∞ performance in the iteration domain is presented. The condition can be derived in terms of linear matrix inequalities that can be solved by using existing numerical techniques. A numerical simulation example is also included to validate the theoretical results.     

Stability of iterative learning control with data dropouts via asynchronous dynamical system

In this paper, the stability of iterative learning control with data dropouts is discussed. By the super vector formulation, an iterative learning control (ILC) system with data dropouts can be modeled as an asynchronous dynamical system with rate constraints on events in the iteration domain. The stability condition is provided in the form of linear matrix inequalities (LMIS) depending on the stability of asynchronous dynamical systems. The analysis is supported by simulations.     

Industrial robot accurate trajectory generation by nested loop iterative learning control

Common error sources of industrial robot manipulators include joint servoing error, imprecise kinematics, mechanical compliance, and transmission error. In this work we present a nested loop iterative learning control (ILC) feedforward structure: an inner loop that compensates for motor dynamics, and an outer loop that corrects the deviation along the path tracked, that features practically efficient implementation. Taking advantage of industrial robot’s speed reduction transmission, single-input-single-output method is demonstrated effective for the nonlinear coupled robot dynamics. Data-based inversion technique that incorporates motion constraint is used for fast inner loop convergence. The outer loop utilizes inverse Jacobian matrix for joint reference modification. For nonlinear static friction that is difficult to be compensated for with only joint command, notch filtering is utilized in the learning process to avoid exciting vibration inherently exists in the robot. The proposed nested loop ILC requires only the nominal kinematic parameters from the robot manufacturer, and can be readily implemented without modifying the existing robot controllers. The effectiveness of the proposed method is experimentally verified on a six degree-of-freedom robot manipulator.     

基于自抗扰迭代学习控制的双定子磁场调制电机转矩脉动抑制策略

与传统单层气隙电机相比,双定子磁场调制（FMDS）电机存在较大的转矩脉动。转矩脉动随转子位置的变化而周期性变化,从而导致速度脉动。针对周期性转矩脉动、快速性与超调性的矛盾以及外部不确定干扰的存在,提出一种基于自抗扰迭代学习（ILC-ADRC）的转速外环控制器。通过设定期望转速的过渡过程,避免了阶跃输入引起过大转速超调,降低了迭代学习的初始条件要求。设计线性扩张状态观测器(LESO)以简化参数调节。加入过去周期性的转速误差信息,以补偿转矩脉动。最后,利用状态误差反馈控制律生成控制信号。仿真结果表明,该系统响应快速、无超调,有效地降低了电机的转矩脉动。     

A Practical Iterative Learning Path‐Following Control Of An Omni‐Directional Vehicle

A new practical iterative learning control (ILC) updating law is proposed to improve the path following accuracy for an omni‐directional autonomous mobile robot. The ILC scheme is applied as a feedforward controller to the existing feedback controller. By using the local symmetrical double‐integral of the feedback control signal of the previous iteration, the ILC updating law takes a simple form with only two design parameters: the learning gain and the range of local integration. Convergence analysis is presented together with a design procedure. Simulation results on a difficult maneuver are presented to illustrate the effectiveness of the proposed simple and yet practical scheme. The simulation is based on the model of a novel robotic platform, the Utah State University (USU) Omni‐Directional Vehicle (ODV), which uses multiple “smart wheels,” whose speed and direction can be independently controlled through dedicated processors for each wheel.     

A graph-theoretic approach to fault detection and isolation for structured bilinear systems

In this paper, using a graph-theoretic approach, we address some issues related to the fault detection and isolation for structured bilinear systems. Considering a structured bilinear system submitted to faults and disturbances, we give necessary and sufficient conditions to the solvability of the so-called bilinear fundamental problem of residual generation. We also treat the cases where the system is submitted to multiple failures occurring simultaneously or only one at a time. One of the main advantages of the proposed analysis tool is that all the given conditions are easy to check because they deal with finding paths in a digraph. This makes our approach well suited to studying large scale systems.     

并联有源电力滤波器的自学习控制

简述了迭代自学习控制（ILC）的基本原理，提出了一种实用的无相移提取信号基波分量的方法，将ILC和这种方法应用于并联有源电力滤波器（APF）的补偿控制策略中，建立了计算机仿真模型并进行了实验研究，结果表明，对于同时检测负荷侧和系统侧谐波电流的并联APF，采用该控制策略可减少实际装置的测量信号数目，大大简化控制系统的复杂性，与传统的PID控制相比，ILC控制原理简单，实现方便，具有更好的跟踪谐波和抗干扰能力。