Decentralized nonlinear adaptive control for multimachine power systems via high-gain perturbation observer

This paper presents two novel decentralized nonlinear adaptive controllers (DNAC) for large-scale interconnected power systems, via state-feedback and output-feedback strategies respectively. In the both controllers, system perturbation, which includes all subsystem nonlinearities and interactions between subsystems, is estimated by a high-gain observer and then involved the decentralized adaptive feedback linearizing control law. For the first DNAC, when all subsystem states are available, a second-order high-gain perturbation observer is designed to estimate the system perturbation, which leads to a decentralized nonlinear adaptive state-feedback controller. For the second, a decentralized nonlinear adaptive output-feedback controller is designed using a high-gain states and perturbation observer, when only one subsystem state is measured. The stability of the closed-loop controller/observer system is analyzed by the Lyapunov direct method. Both the controllers have been evaluated in a simulation study based on a three-machine power system. The results show that with a simple structure, both the controllers have robust performance of improving the transient stability and damping of multimode oscillations, under different power-system operation and fault conditions.     

基于干扰观测器的导弹自动驾驶仪的反演设计

在分析喷流干扰的基础上,建立了含有干扰放大因子的复合控制的弹体模型.针对导弹气动参数的不确定性及外界干扰对导弹控制性能的影响,利用非线性干扰观测器对干扰的逼近特性,结合反演控制设计了非线性导弹自动驾驶仪,并基于Lyapunov方法,给出了整个系统的稳定性证明.仿真结果表明,设计的控制方案不仅提高了系统响应速度,而且能够保证系统具有良好的鲁棒性.     

Disturbance observer based path tracking control of robot manipulator considering torque saturation

This paper proposes a path-tracking algorithm to compensate for path deviation due to torque limits. The algorithm uses a disturbance observer with an additional saturation element at each joint of n degrees of freedom (DOF) manipulator to obtain a simple equivalent robot dynamic (SERD) model. This model is represented as an n independent double integrator system and is designed to ensure stability under input saturation. For an arbitrary trajectory generated for a given path in Cartesian space whenever any of the actuators is saturated, the desired acceleration of the nominal trajectory in Cartesian space is modified on-line by using SERD. An integral action with respect to the difference between the nominal and modified trajectories is utilized in the nonsaturated region of actuators to reduce the path error. To verify the effectiveness of the proposed algorithms, real experiments were performed for a two DOF SCARA-type direct-drive arm.     

Disturbance observer based fully digital controlled PWM inverterfor CVCF operation

A new approach for digital feedback control of a PWM inverter is proposed, in which an output DB (deadbeat) control is achieved combined with a disturbance observer. In the proposed scheme, the pole placements of the state observer and the disturbance observer are chosen separately. When the two observers employed the same pole placements, the experimental setup had a tendency to become unstable due to the detection error. By selection of the different pole placements, the disturbance observer quickly estimates the disturbance and feedforward disturbance cancellation is achieved. Then the state observer estimates the state variables at the next sampling instant, and the deadbeat controller is applied to the nominal system. This scheme has advantages in robustness in the practical application. From the view point of UPS applications, the advantages and disadvantages are discussed through simulations and experiments. Compared with other digital control laws, the superiority of the proposed control law is verified     

基于非线性干扰观测器的主动队列管理

针对主动队列管理系统存在UDP流干扰的拥塞问题,本文基于非线性干扰观测器提出了一种主动队列管理算法。采用非线性干扰观测器对UDP流干扰进行估计,从而减小了主动队列管理系统的干扰。然后利用设计的非线性干扰观测器,使用反步滑模方法设计了一种主动队列管理算法。由于观测误差的界未知,设计了一种自适应律来避免必须事先知道观测误差的界。仿真结果表明,该算法对UDP流干扰具有较强的鲁棒性,能使队列长度较好地稳定在期望的队列长度附近。     

CHAOTIC CONTROL OF NONLINEAR SYSTEMS BASED ON IMPROVED CORRELATIVITY

Chaotic sequences are basically ergodic random esquences.By improving correlativity of a chaotic signal,the chaotic dynamic system can be controlled to converge to its equilibrium point and,more significantly,to its multi-periodic orbits.Mathematical theory analysis is carried out and some computer simulation results are provided to support such controllability of the chaotic Henon system and the discrete coupled map lattice.     

基于改善相关性的混沌控制

通过改善混沌信号的相关性,离散混沌系统能以较快的速度收敛到它的平衡点或多周期轨道,文中给出了数学分析,和以Henon和耦合映象格子为例的仿真实验.     

A nonlinear state observer for the sensorless control of apermanent-magnet AC machine

This paper presents a sensorless speed regulation scheme for a permanent-magnet synchronous motor (PMSM) based solely on the motor line currents measurements. The proposed scheme combines an exact linearization-based controller with a nonlinear state observer which estimates the rotor position and speed. Moreover, the stability of the closed-loop system, including the observer, is demonstrated through Lyapunov stability theory. The proposed observer has the advantage of being insensitive to rotation direction. It is shown how a singularity at zero velocity appears in the scheme and how it can be avoided by switching smoothly from the observer-based closed-loop control to an open-loop control at low velocity. The system performance is tested with an experimental setup consisting of a PMSM servo drive and a digital-signal-processor-based controller for both unidirectional and bidirectional speed regulation     

Actively adaptive control for nonlinear stochastic systems

An adaptive control method can be classified into two categories, actively adaptive or passively adaptive, according to how the available information is being utilized in the on-line calculation of the control. An actively adaptive controller utilizes, in addition to the , available real-time information, the knowledge that future observations will be made, and regulates its adaptation (learning). This is done by anticipating how future estimation will be beneficial to the control objective. On the other hand, a passively adaptive controller, while utilizing the available real-time measurements, does not account for the fact that future observations will be made. Thus any learning in such a case will occur in an accidental manner. This paper summarizes a recent research effort in the development of an actively adaptive control method for nonlinear stochastic systems. A new and simpler set of equations for the original algorithm is given that provides further insight into the concepts of probing and caution in adaptive control. Several examples are chosen to illustrate the actively adaptive control method.     

Output feedback nonlinear control for electro-hydraulic systems

In this paper we present an output feedback nonlinear control for position tracking of electro-hydraulic systems (EHSs). Although previous nonlinear control methods improved the position tracking performance of EHS, all of the methods require full state feedback. However, due to cost and space limitations, it is not always possible to measure the full state of the EHS. The proposed method consists of a high gain observer and a passivity-based controller. The high gain observer is designed to estimate the full state, and the passivity-based control is implemented for position tracking. In order to design the passivity-based controller with the high gain observer, a defined Lyapunov condition guarantee that the origin of the tacking error dynamics is exponentially stable by selecting the controller gain. The stability of the closed-loop is studied using the singular perturbation theorem. The performance of the proposed method is validated through simulations and experiments.     

基于神经网络的一类非线性系统自适应反步控制

针对传统自适应控制需要满足匹配条件、激发信号存在以及逼近误差有界等条件,提出一种新的基于神经网络的一类非线性系统自适应反步控制器设计方案.使用三层神经网络逼近系统的非线性特性,通过网络权系数自适应调整来不断的在线估计未知的逼近误差上界,采用有σ修正项的自适应律以放松持续激励条件.给出了基于Lyapunov意义上的闭环系统稳定性分析,证明跟踪误差收敛于原点的一个ε领域内.仿真结果表明了所提反步控制器的正确性.     

基于干扰观测器的解耦控制算法

针对多变量系统之间的耦合问题,设计了一种干扰观测器与PID控制器相结合的多变量解耦控制器。而且干扰观测器可以对实际对象与名义模型之间的差异在控制中进行等量的补偿。用MATLAB软件对一个耦合系统模型进行仿真实验,结果表明加入干扰观测器的控制算法对抑制噪声和其他变量的耦合干扰有显著效果。     

Discrete-time quasi-sliding-mode control for a class of nonlinear control systems

A quasi-sliding-mode control law is proposed for a class of discretetime nonlinear control systems concerning system stabilisation and chatter alleviation. An illustrative example is also given to demonstrate the use and benefits of the scheme.     

Multi-objective control for uncertain nonlinear active suspension systems

Performance requirements for vehicle active suspensions include: (a) ride comfort, which means to isolate the body as far as possible from road-induced shocks and vibrations to provide comfort for passengers; (b) road holding, which requires to suppress the hop of the wheels for the uninterrupted contact between wheels and road; and (c) suspension movement limitation, which is restricted by the mechanical structure. In view of such situations, plus the parametric uncertainties, this paper suggests a constrained adaptive backstepping control scheme for active suspensions to achieve the multi-objective control, such that the resulting closed-loop systems can improve ride comfort and at the same time satisfy the performance constraints in the presence of parametric uncertainties. Compared with the classic Quadratic Lyapunov Function (QLF), the barrier Lyapunov function employed in this paper can achieve a less conservatism in controller design. Finally, a design example is shown to illustrate the effectiveness of the proposed control law, where different initial state values are considered in order to verify the proposed approach in detail.     

A nonlinear disturbance observer for robotic manipulators

A new nonlinear disturbance observer (NDO) for robotic manipulators is derived in this paper. The global exponential stability of the proposed disturbance observer (DO) is guaranteed by selecting design parameters, which depend on the maximum velocity and physical parameters of robotic manipulators. This new observer overcomes the disadvantages of existing DOs, which are designed or analyzed by linear system techniques. It can be applied in robotic manipulators for various purposes such as friction compensation, independent joint control, sensorless torque control and fault diagnosis. The performance of the proposed observer is demonstrated by the friction estimation and compensation for a two-link robotic manipulator. Both simulation and experimental results show the NDO works well     

基于状态观测器的混沌动态系统跟踪控制

针对一类连续混沌动态系统,提出一种基于状态观测器的跟踪控制方法来进行混沌控制.在引入状态观测器观测混沌动力学系统状态变量的基础上,采用反馈线性化方法将非线性混沌系统转换为线性系统,再针对反馈线性化后的线性系统设计轨迹跟踪控制器,实现被控混沌系统的跟踪控制.仿真结果进一步验证了该方法的有效性.     

基于观测器的混沌投影同步

研究一种基于状态观测器的混沌投影同步,得到驱动系统和响应系统对应变量成比例.该设计方案从系统稳定性出发,通过极点配置和设计驱动系统的状态观测器,得到控制器,实现了一类离散混沌系统的投影同步.在混沌控制过程,改变奴役系统的控制器使比例因子增大或减小.因此,根据需要可以任意改变比例因子,实现相空间自由地拉伸和收缩,这一特性在保密通信方面有一定的应用前景.通过对Henon混沌系统和超混沌广义Henon映射的数值仿真,表明理论分析的正确性.     

基于改进型滑模观测器的永磁同步电机控制

传统基于反电动势的滑模观测器采用Sign函数来作为开关函数,因为Sign函数的开关特性,使系统存在抖振大的问题。在传统滑模观测器的基础上,通过采用Sigmoid函数代替Sign函数,改善了开关特性,大大地减少了抖振,并去掉了滤波器和转角补偿器,简化了系统,提高了反电动势的估算精度,从而得到更精确的转速和转角位置信息。在Matlab/Simulink平台基础上建立控制系统仿真模型,对该方案做了验证。仿真结果表明:该方法提高了滑模观测器的精度,降低了系统的抖振,提高了系统的性能。     

Variable neural networks for adaptive control of nonlinear systems

This paper is concerned with the adaptive control of continuous-time nonlinear dynamical systems using neural networks. A novel neural network architecture, referred to as a variable neural network, is proposed and shown to be useful in approximating the unknown nonlinearities of dynamical systems. In the variable neural networks, the number of basis functions can be either increased or decreased with time, according to specified design strategies, so that the network will not overfit or underfit the data set. Based on the Gaussian radial basis function (GRBF) variable neural network, an adaptive control scheme is presented. The location of the centers and the determination of the widths of the GRBFs in the variable neural network are analyzed to make a compromise between orthogonality and smoothness. The weight-adaptive laws developed using the Lyapunov synthesis approach guarantee the stability of the overall control scheme, even in the presence of modeling error(s). The tracking errors converge to the required accuracy through the adaptive control algorithm derived by combining the variable neural network and Lyapunov synthesis techniques. The operation of an adaptive control scheme using the variable neural network is demonstrated using two simulated examples