Adaptive Dynamic Surface Control for Generator Excitation Control System

For the generator excitation control system which is equipped with static var compensator (SVC) and unknown parameters, a novel adaptive dynamic surface control scheme is proposed based on neural network and tracking error transformed function with the following features: (1) the transformation of the excitation generator model to the linear systems is omitted; (2) the prespecified performance of the tracking error can be guaranteed by combining with the tracking error transformed function; (3) the computational burden is greatly reduced by estimating the norm of the weighted vector of neural network instead of the weighted vector itself; therefore, it is more suitable for the real time control; and (4) the explosion of complicity problem inherent in the backstepping control can be eliminated. It is proved that the new scheme can make the system semiglobally uniformly ultimately bounded. Simulation results show the effectiveness of this control scheme

An approach to neural control of a class of strict-feedback nonlinear systems

研究一类不确定严反馈非线性系统的跟踪控制问题.通过采用单一神经网络逼近系统的所有未知部分,提出一种新的鲁棒自适应控制设计方法.该方法能直接给出实际控制律和自适应律,有效地解决现有方法中存在的控制设计复杂和计算负担重等问题.稳定性分析表明,闭环系统所有信号是半全局一致最终有界的,并且通过调整控制参数可使跟踪误差任意小.仿真结果验证了所提出方法的有效性.The problem of tracking control is studied for a class of uncertain strict-feedback nonlinear systems.A new robust adaptive control design approach is presented by approximating all the unknown parts of the system with a single neural network.By using this approach,the actual control law and the adaptive law of the controller can be given directly,and the problems,such as control design complexity and high computational burden,are dealt with effectively.The stability analysis shows that the closed-loop system signals are semi-globally uniformly ultimately bounded,and the tracking error can be made arbitrary small by choosing control parameters.Simulation results show the effectiveness of the proposed approach.国家自然科学基金项目(61074017;61074004;61273137;51209026); 辽宁省高等学校优秀人才支持计划项目(2009R06); 中央高校基本科研业务费项目(017004

Unknown dynamics estimator-based output-feedback control for nonlinear pure-feedback systems

Most existing adaptive control designs for nonlinear pure-feedback systems have been derived based on backstepping or dynamic surface control (DSC) methods, requiring full system states to be measurable. The neural networks (NNs) or fuzzy logic systems (FLSs) used to accommodate uncertainties also impose demanding computational cost and sluggish convergence. To address these issues, this paper proposes a new output-feedback control for uncertain pure-feedback systems without using backstepping and function approximator. A coordinate transform is first used to represent the pure-feedback system in a canonical form to evade using the backstepping or DSC scheme. Then the Levant's differentiator is used to reconstruct the unknown states of the derived canonical system. Finally, a new unknown system dynamics estimator with only one tuning parameter is developed to compensate for the lumped unknown dynamics in the feedback control. This leads to an alternative, simple approximation-free control method for pure-feedback systems, where only the system output needs to be measured. The stability of the closed-loop control system, including the unknown dynamics estimator and the feedback control is proved. Comparative simulations and experiments based on a PMSM test-rig are carried out to test and validate the effectiveness of the proposed method

Adaptive neural control for a class of uncertain nonlinear systems in pure-feedback form with hysteresis input

10.1109/TSMCB.2008.2006368IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics392431-443ITSC

A Survey of Deep Learning Applications to Autonomous Vehicle Control

Designing a controller for autonomous vehicles capable of providing adequate performance in all driving scenarios is challenging due to the highly complex environment and inability to test the system in the wide variety of scenarios which it may encounter after deployment. However, deep learning methods have shown great promise in not only providing excellent performance for complex and non-linear control problems, but also in generalising previously learned rules to new scenarios. For these reasons, the use of deep learning for vehicle control is becoming increasingly popular. Although important advancements have been achieved in this field, these works have not been fully summarised. This paper surveys a wide range of research works reported in the literature which aim to control a vehicle through deep learning methods. Although there exists overlap between control and perception, the focus of this paper is on vehicle control, rather than the wider perception problem which includes tasks such as semantic segmentation and object detection. The paper identifies the strengths and limitations of available deep learning methods through comparative analysis and discusses the research challenges in terms of computation, architecture selection, goal specification, generalisation, verification and validation, as well as safety. Overall, this survey brings timely and topical information to a rapidly evolving field relevant to intelligent transportation systems.Comment: 23 pages, 3 figures, Accepted in IEEE Transactions on Intelligent Transportation System