非线性广义系统的变结构控制设计

从线性定常广义系统出发，研究非线性广义系统的变结构控制设计问题。其主要思想为：选取一具有指定性能的线性定常广义系统作为参考模型，根据所控系统与参考模型的误差方程设计变结构控制，使系统的状态（输出）向参考模型的状态（输出）逼近，由参考模型的性能即得所研究的非线性广义系统所希望的性能。仿真例子验证了所建立方法的有效性。     

力矩受控机器人手臂非线性控制器的计算机辅助设计

本文扩展了一种用于力矩受控机器人手臂非线性控制器设计的设计方法。控制器由非线性前置补偿器构成,该补偿器使机器人手臂表现为一组线性二阶系统(每个自由度为一个线性系统)和一个线性反馈控制器。非线性控制器用希望的线性闭环传递函数确定参数,这就自动达到通常系统的稳定性。然后调整控制器的参数(诸如希望的闭环传递函数的带宽之类的参数)使之满足工程上的约束。譬如控制硬件约束(状态的非线性函数)、机器人性能和机器人稳定性(任凭机器人手臂之摩擦、质量等参数的变化,保持机器人性能和稳定性)。阐述了近来提出的以最佳化为基础的(Optim-isation-based)设计法的内容。通过一个例子的讨论和仿真结果,以说明此设计步骤。     

多变量非线性时滞系统的内模控制

本文将单入单出非线性内模控制的设计方法推广到了含多步时滞的多入多出的非线性系统控制器设计中，对于不含纯滞后的过程，所设计的控制器能够实现所期望的常规性能。其中通过非线性滤波器的加入，能够获得当过程和模型存在失配时的鲁棒性，并使控制器结构得以实现。     

基于模糊PID的磁悬浮球控制系统研究

针对磁悬浮球系统的本质不稳定性,设计模糊PID算法实现系统的稳定控制,并使之动态性能及稳态性能满足要求;文中首先分析了磁悬浮球系统的基本原理,并进行力学分析,建立系统的数学模型,并对其中的非线性部分进行了平衡点处的线性化,而后采用用PID控制设计,PID可以实现系统的稳定控制,且控制精度较高,但对于动态性能的改善不足,且当模型中的参数改变时,PID参数的适应性较差;因此在PID参数的基础上,采用模糊PID控制,使得系统既可以满足三性要求,又可以使其具有参数变化的适应能力。     

不对称信息理论与非线性鲁棒控制算法

针对非线性船舶航向保持系统，通过设计其精确反馈线性化控制器，给出一种物理意义更明显的解释，即非线性精确反馈线性化控制器由非线性函数项和常规线性控制器组成．将简化的精确反馈线性化法与闭环增益成形算法相结合，设计其非线性鲁棒控制器，增加了信息的传递量，在提高系统鲁棒性能的基础上，使该算法成为实际工程上可用的算法．仿真结果表明，当信息传递充分时，航向保持效果良好．     

基于支持向量机的可分离非线性动态系统辨识

针对状态变量和控制变量可分离的非线性动态系统模型,通过引入两个非线性核函数重新设计了标准支持向量机的回归估计模型,使之适用于非线性动态系统的辨识. 它包含两个分别关于状态变量和控制变量的非线性函数,用于辨识可分离变量非线性动态系统中的两个非线性函数.文中的仿真实验验证了我们算法用于非线性动态系统辨识的有效性.     

基于状态变换的非线性鲁棒控制器设计

本文对具有特定结构的非线性系统,介绍了一种非线性变换使之线性化,并利用线性鲁棒 控制器的设计方法设计非线性鲁棒控制器,从而提高了闭环系统对参数变动的鲁棒性.文中 还通过一个CSTR反应器模型的仿真说明该方法的有效性.     

基于逆系统方法的汽轮发电机组汽门开度非线性控制

电力系统中，汔轮发电机组的汽门开度控制是一个典型的非线性控制问题，本文应用逆系统方法设计出了便于实现的非线性控制规律。经仿真研究表明，所设计的控制律可以显著改善系统的动态性能，达到良好的控制效果。     

具有死区预补偿的非线性预测函数控制

死区特性是一类典型的非线性，它对系统最直接的影响是造成稳态误差。针对工业控制系统中广泛存在的死区非线性特性，通过引入死区非线性预补偿器，使系统转化为广义线性系统，完全消除了死区非线性特性对系统的影响，然后针对此广义线性系统进行预测函数控制算法的设计。利用Matlab软件仿真表明，该非线性预测函数控制方法是一种计算简单、跟踪性能好、误差较小的有效的控制方法。     

轧机速度系统积分反步控制器的研究

本文给出了轧机速度系统的积分反步控制器设计方法，此方法通过逐步修正算法设计镇定控制器，实现系统的全局调节或跟踪．在每一步把状态坐标变换和一个已知Lyapunov函数的虚拟控制系统的镇定函数等联系起来，最终得到一个控制李稚普诺夫函数(clf).然后．基于上述方法设计了直流电机驱动的轧机速度系统非线性反馈控制器，用来说明积分反步法也适用于线性系统、仿真研究结果表明．本文所设计的反馈控制器使闭环系统稳定，且使系统具有良好的跟踪性能。     

Multi machine power system excitation control design via theories of feedback linearization control and nonlinear robust control

The dynamics of a large-scale power system are both nonlinear and interconnected. The equilibrium of such a system is typically unknown and uncertain, and the controllers within are also subject to physical limitations. In this paper, a new application of nonlinear robust control is presented for power system control design. It is assumed that the controllers are designed as a part of generator excitation system design. First, a customized exact feedback linearization scheme is developed for the power system under investigation. This new linearization scheme allows one to transform the power system with a single-axis system model into a linear uncertain system with an unknown equilibrium. Based on the latest development of nonlinear robust control theory, a novel control design is then applied to stabilize the resulting linearized uncertain system. Finally, a nonlinear decentralized excitation control is obtained by the inverse transformation. Compared with existing control schemes, the proposed control is free from such common deficiencies of power system nonlinear controllers as network dependence and equilibrium dependence. Detailed stability analysis and engineering judgment in the control design are provided. The results of simulation studies are presented.     

On near optimal neural control of multiple-input nonlinear systems

It has been a common consensus that general techniques for stabilization of nonlinear systems are available only for some special classes of nonlinear systems. Control design for nonlinear systems with uncertain components is usually carried out on a per system basis, especially when physical control constraints, and certain control performance measures such as optimum time control are imposed. Elegant adaptive control techniques are difficult to apply to this type of problems. A new neural network based control design is proposed and presented in this paper to deal with a special class of uncertain nonlinear systems with multiple inputs. The desired system dynamics are analyzed and utilized in the process of the proposed intelligent control design. The theoretical results are provided to justify the design procedures. The simulation study is conducted on a second-order bilinear system with two inputs and uncertainties on its parameters. The simulation results indicate that the proposed design approach is effective.     

Framework of Combined Adaptive and Non-adaptive Attitude Control System for a Helicopter Experimental System

This paper presents a framework of a combined adaptive and non-adaptive attitude control system for a helicopter experimental system. The design method is based on a combination of adaptive nonlinear control and non-adaptive nonlinear control. With regard to detailed attitude control system design, two schemes are shown for different application cases.     

电液伺服系统多项式非线性建模与控制一体化设计

常规电液伺服系统PID控制无法克服非线性因素影响,存在跟踪准确性和鲁棒性问题.因此,本文提出电液伺服系统多项式非线性H_∞控制律设计方法,改进电液伺服系统的控制性能与鲁棒性.首先利用多项式非线性模型对电液伺服系统进行系统辨识,得到以误差作为状态变量的多项式非线性模型;然后设计多项式非线性控制律,证明所提出控制律可以保证系统从干扰至控制输出L₂增益小于等于设定值,并且在系统干扰为零时保证误差全局渐进收敛,同时给出了控制律的求解方法.最后对提出的控制律进行实验验证.实验结果表明:相较于常规PID控制,多项式非线性控制律能够改善实验台伺服缸控制过程的瞬态响应,具有更好的抗干扰能力.本文提出的设计方法为非线性H_∞控制在电液伺服系统控制领域的实际应用提供了可行方案.     

Input linearization of nonlinear systems via pulse-width control

In this note, it is shown that a general nonlinear system can be transformed to an affine nonlinear system by virtue of the use of pulsewidth control. Therefore, the combined system from the pulse width control input to the nonlinear system output behaves as an affine (linear-in-control) system. By this way, a cumbersome nonlinear system model can be transformed to a simpler linear-in-control form without increasing the system dimension; this in turn enables simpler control design. Furthermore, using this methodology, some control design methods developed only for affine systems can be adapted to general nonlinear systems.     

Control of nonlinear plants with state-dependent coefficients

The theoretical fundamentals for solving the linear quadratic problems may be sometimes used to design the control actions for the nonlinear systems. The method relying on the Riccati equation with state-dependent coefficients is one of the promising and rapidly developing tools for design of the nonlinear controllers. The set of possible suboptimal solutions is generated by the ambiguous representation of the nonlinear system as a linearly structured system with state-dependent coefficients and the lack of sufficiently universal algorithms to solve the Riccati equation also having state-dependent coefficients. The paper proposed a method to design a guaranteed control for the uncertain nonlinear plant with state-dependent parameters. An example of designing the controller for an uncertain nonlinear system was presented.     

Robustness design of nonlinear dynamic systems via fuzzy linearcontrol

This study introduces a fuzzy linear control design method for nonlinear systems with optimal H^∞ robustness performance. First, the Takagi and Sugeno fuzzy linear model (1985) is employed to approximate a nonlinear system. Next, based on the fuzzy linear model, a fuzzy controller is developed to stabilize the nonlinear system, and at the same time the effect of external disturbance on control performance is attenuated to a minimum level. Thus based on the fuzzy linear model, H^∞ performance design can be achieved in nonlinear control systems. In the proposed fuzzy linear control method, the fuzzy linear model provides rough control to approximate the nonlinear control system, while the H^∞ scheme provides precise control to achieve the optimal robustness performance. Linear matrix inequality (LMI) techniques are employed to solve this robust fuzzy control problem. In the case that state variables are unavailable, a fuzzy observer-based H^∞ control is also proposed to achieve a robust optimization design for nonlinear systems. A simulation example is given to illustrate the performance of the proposed design method     

Nonlinear multivariable control of nuclear power plants based onthe unknown-but-bounded disturbance model

A nonlinear control system that is based entirely on the time-domain representation of dynamic systems is proposed for the control of a simplified pressurized-water-reactor (PWR) nuclear power plant model. The initial stage consists of designing several linear control systems using plant models linearized at preselected operating points. A set-theoretic algorithm is used that explicitly treats the control, control rate, and state constraints. The control objective is to design a system that uses only the available control, at the available rate, in the presence of an input disturbance, without violating the prespecified state constraints. The final stage of the design process uses the gain-scheduling technique to implement the linear control systems a global nonlinear control system. The final design is evaluated through transient response simulations using a simplified nonlinear model of a PWR-type nuclear power plant, with encouraging results     

Neural network-based control design: an LMI approach.

We address a neural network-based control design for a discrete-time nonlinear system. Our design approach is to approximate the nonlinear system with a multilayer perceptron of which the activation functions are of the sigmoid type symmetric to the origin. A linear difference inclusion representation is then established for this class of approximating neural networks and is used to design a state feedback control law for the nonlinear system based on the certainty equivalence principle. The control design equations are shown to be a set of linear matrix inequalities where a convex optimization algorithm can be applied to determine the control signal. Further, the stability of the closed-loop is guaranteed in the sense that there exists a unique global attraction region in the neighborhood of the origin to which every trajectory of the closed-loop system converges. Finally, a simple example is presented so as to illustrate our control design procedure.     

随机部分可积拟哈密顿系统的概率密度追踪控制

目前非线性随机系统的控制方法存在设计复杂,计算成本高,以及缺乏稳定性或收敛性证明等缺点,针对这些问题,本文在作者前期研究的基础上发展了一种全新的针对部分可积的非线性随机系统的反馈控制,使得受控系统输出的稳态概率密度逼近事先给定的目标概率密度,并利用Lyapunov函数法证明受控系统的收敛性.数学仿真结果证明了这种方法的可行性和正确性.