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Adaptive output feedback control of flexible systems.

机译:灵活的系统的自适应输出反馈控制。

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Neural network-based adaptive output feedback approaches that augment a linear control design are described in this thesis, and emphasis is placed on their real-time implementation with flexible systems. Two different control architectures that are robust to parametric uncertainties and unmodelled dynamics are presented. The unmodelled effects can consist of minimum phase internal dynamics of the system together with external disturbance process. Within this context, adaptive compensation for external disturbances is addressed.; In the first approach, internal model-following control, adaptive elements are designed using feedback inversion. The effect of an actuator limit is treated using control hedging, and the effect of other actuation nonlinearities, such as dead zone and backlash, is mitigated by a disturbance observer-based control design. The effectiveness of the approach is illustrated through simulation and experimental testing with a three-disk torsional system, which is subjected to control voltage limit and stiction.; While the internal model-following control is limited to minimum phase systems, the second approach, external model-following control, does not involve feedback linearization and can be applied to non-minimum phase systems. The unstable zero dynamics are assumed to have been modelled in the design of the existing linear controller. The laboratory tests for this method include a three-disk torsional pendulum, an inverted pendulum, and a flexible-base robot manipulator.; The external model-following control architecture is further extended in three ways. The first extension is an approach for control of multivariable nonlinear systems. The second extension is a decentralized adaptive control approach for large-scale interconnected systems. The third extension is to make use of an adaptive observer to augment a linear observer-based controller. In this extension, augmenting terms for the adaptive observer can be used to achieve adaptation in both the observer and the controller. Simulations to illustrate these approaches include an inverted pendulum with its cart serially attached to two carts (one unmodelled), three spring-coupled inverted pendulums, and an inverted pendulum with its initial condition in a range in which a linear controller is destabilizing.
机译:本文描述了一种基于神经网络的自适应输出反馈方法,该方法增强了线性控制设计,并着重于其在灵活系统上的实时实现。提出了两种对参数不确定性和未建模动力学具有鲁棒性的控制架构。未建模的影响可能包括系统的最小相位内部动力学以及外部干扰过程。在这种情况下,解决了对外部干扰的自适应补偿。第一种方法是内部模型跟踪控制,使用反馈反演来设计自适应元素。致动器极限的影响可通过控制套期进行处理,其他致动非线性(如死区和间隙)的影响可通过基于干扰观测器的控制设计得到缓解。通过三盘扭转系统的仿真和实验测试,说明了该方法的有效性,该系统受控制电压限制和静摩擦的影响。虽然内部模型跟随控制仅限于最小相位系统,但第二种方法外部模型跟随控制不涉及反馈线性化,可以应用于非最小相位系统。假定不稳定的零动力学已在现有线性控制器的设计中建模。该方法的实验室测试包括三盘扭转摆,倒立摆和柔性基座机器人操纵器。外部模型跟随控制体系结构通过三种方式进一步扩展。第一个扩展是一种用于控制多变量非线性系统的方法。第二个扩展是用于大型互连系统的分散式自适应控制方法。第三扩展是利用自适应观察器来增强基于线性观察器的控制器。在此扩展中,可将自适应观察者的扩充项用于在观察者和控制器中均实现自适应。为说明这些方法而进行的仿真包括:一个倒置摆锤,其手推车与两个手推车串联(一个未建模);三个弹簧耦合倒置摆锤;一个倒置摆锤,其初始状态在线性控制器不稳定的范围内。

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