Buck变换器非线性控制器设计

针对Buck变换器的非线性特性,提出了采用反步法和反步滑模法控制非线性系统的控制策略。基于对Buck变换器数学模型的分析,论述了反步法控制器的设计,实现了当Buck变换器参数发生变化时,电容电压和电感电流对给定参考信号较好的跟踪;将反步法与滑模控制相结合,设计了反步滑模控制器,进一步改善了系统的跟踪性能。使用System Generator在Matlab环境下进行系统建模及仿真研究,结果表明,反步法和反步滑模法的非线性系统控制器效果良好。     

轧机液压伺服位置系统的自适应反步滑模控制

针对轧机液压伺服位置系统存在非线性特性、参数不确定性以及控制输入前具有不确定系数的问题,提出了一种自适应反步滑模控制方法.通过对系统非线性模型的等价变换和选择合适的Lyapunov函数,有效解决了由于控制输入前具有不确定系数导致的所设计的控制量与自适应律互相嵌套的难题.把自适应反步法和滑模控制方法相结合,有效克服了系统...     

基于反步法的燃气轮机功率控制器设计

为使燃气轮机快速平稳地跟踪负荷设定值,提出了基于反步法的燃气轮机功率控制器;对燃气轮机-发电机系统非线性模型进行简化及坐标转换,通过反步法设计Lyapunov函数和中间虚拟控制量,计算得到控制律,并采用Simulink软件仿真验证本文设计的燃气轮机功率控制器的可行性和有效性。结果表明:与传统PI控制器相比,采用反步法的燃气轮机功率控制器提高了燃气轮机跟踪负荷设定值的稳定性和快速性,该方法有效、可行。     

基于反步法的单管正激变换器控制

为了提高单管正激变换器非线性系统的控制性能,提出将反步法应用于系统中.首先建立了具有参数严格反馈形式的二阶非线性系统数学模型.对此模型应用反步设计方法时,通过选择跟踪误差和虚拟控制量,并构造适当的李雅普诺夫函数,使电容电压和电感电流状态分量具有适当的渐进特性,实现整个系统在大扰动下的全局渐进稳定性,进而得到反馈控制律的表达式.通过相应的实验验证了方法的有效性和正确性.系统具有良好的静态性能和动态性能,验证了控制策略的正确性和可行性.     

基于反步法的异步电机位置控制器的设计

基于静止d-q坐标系下的数学模型,利用非线性反步法设计了异步电机的全状态位置跟踪控制器,通过磁链和转矩的跟踪来达到位置跟踪.在Matlab/Simulink中搭建异步电机位置控制系统的仿真模型.仿真结果表明:用反步法设计的异步电机控制系统能获得较好的位置跟踪控制性能,为异步电机控制系统的设计提供了新思路.     

基于ESO-backstepping的多机电力系统非线性鲁棒励磁解耦控制

通过构造扩张状态观测器(extended state observer,ESO)对发电机励磁系统模型误差和不确定外扰进行动态补偿,并运用反步法对补偿后的模型设计非线性解耦控制律。由于该方法对系统模型的非线性部分有选择地利用ESO进行动态补偿,既避免了运用反步法设计时自适应参数的引入,又能充分利用系统的特性,减轻ESO的估计负担,有效地兼顾了控制器鲁棒性及控制精度这两方面的要求。仿真表明：该方法鲁棒性强,参数易于选取,与比例–积分–微分(proportion-integration- differentiation,PID)+电力系统稳定(power system stabilizer,PSS)控制器励磁控制器相比较,具有更为优秀的动态品质,方法简单有效。     

单端反激式开关电源反馈回路的补偿控制

针对单端反激式开关电源系统工作的稳定性问题,提出了一种单端反激式开关电源反馈回路的补偿设计方法,设计了基于UC3844电流控制方式的反馈回路和零点一极点补偿电路,并进行了仿真实验.仿真结果表明,本文设计的反馈回路和补偿电路在电网波动和负载变化时仍然可以保证整个系统的稳定性.     

330 MW火力发电机组协调控制系统改进

大型火力发电厂的协调控制系统CCS(Coordinated Control System)是一个复杂的多变量耦合控制系统.研究了反演算函数在大机组CCS中的设计与实现问题.分析了达拉特发电厂1号机组CCS的特点,为解决火电单元机组CCS的动态非线性和耦合特性,设计了压力、功率偏差反演算函数的解耦CCS控制回路,引入反演算函数算法用于动态解耦运算,改善了CCS的强耦合及动态非线性特性,改进了CCS适应外界负荷能力差的现状.为补偿锅炉的惯性和迟延,提出了根据前馈的微分作用快速调节燃料量的方法:加入动态超调措施,使一次风风量提前动作.通过现场试投,验证了新协调系统的有效性.系统实际运行曲线验证了控制系统的良好品质.     

基于反步法的电流型PWM整流器控制策略

电流型PWM整流器的状态空间平均模型具有非线性、强耦合特性,使得控制器的设计较为困难.通过前馈解耦方法,将电流型PWM整流器在同步坐标系下的多输入多输出非线性模型分解为两个单输入单输出的非线性模型,对每个单输入单输出模型,分别采用反步法设计了全局渐进稳定的非线性控制器,实现了对直流电流和功率因数的稳定控制.仿真和实验结果验证了该控制策略的有效性.     

永磁同步电机动态面模糊离散速度调节控制

针对永磁同步电机(PMSM)高阶非线性和外部负载扰动的问题,以反步法为基础,提出了动态面模糊离散速度调节控制方法。通过欧拉公式将PMSM连续模型离散化,得到离散模型;运用动态面方法,处理子系统中的虚拟控制函数,解决传统反步法中"计算爆炸"的问题;利用模糊逻辑系统逼近系统中的非线性部分,并给出模糊离散控制器。仿真结果表明:控制器能够有效地调节电机转速,对外部负载扰动具有良好的鲁棒性。     

Adaptive asymptotical tracking controller design for uncertain nonaffine nonlinear system with high‐order mismatched disturbances

In this paper, the problem of anti‐disturbance asymptotical tracking control is studied for nonaffine systems with high‐order mismatched disturbances. The disturbances can be described as polynomial functions, which are first estimated by constructing generalized extended state filter. The nonaffine system is changed into an augmented affine system via introducing an auxiliary integrator. A novel adaptive anti‐disturbance tracking controller is recursively designed, where the disturbance estimation is used for feedforward compensation at each step. A sliding mode differentiator is applied to reduce the computational burden taken by the backstepping method. The boundedness of the closed‐loop system is proved based on Lyapunov stability theory and zero error tracking performance is ensured. Finally, a numerical example is provided to show the effectiveness of the proposed scheme.     

Adaptive backstepping neural control of state‐delayed nonlinear systems with full‐state constraints and unmodeled dynamics

In this paper, the problem of adaptive neural control is discussed for a class of strict‐feedback time‐varying delays nonlinear systems with full‐state constraints and unmodeled dynamics, as well as distributed time‐varying delays. The considered nonlinear system with full‐state constraints is transformed into a nonlinear system without state constraints by introducing a one‐to‐one asymmetric nonlinear mapping. Based on modified backstepping design and using radial basis function neural networks to approximate the unknown smooth nonlinear function and using a dynamic signal to handle dynamic uncertainties, a novel adaptive backstepping control is developed for the transformed system without state constraints. The uncertain terms produced by state time delays and distributed time delays are compensated for by constructing appropriate Lyapunov‐Krasovskii functionals. All signals in the closed‐loop system are proved to be semiglobally uniformly ultimately bounded. A numerical example is provided to illustrate the effectiveness of the proposed design scheme.     

Fixed-time fault tolerant control for a class of switched nonlinear systems

This work addresses the fixed-time fault tolerant control problem for nonstrict feedback switched nonlinear systems whose subsystems have chained integrators with positive odd rational powers. Besides, the powers of different channels and switched subsystems can be also possibly different. The fixed-time fault tolerant control law is formulated after using the adding one power integrator method, homogeneous system theory, backstepping technique, nested saturation control approach, which is capable of guaranteeing the global fixed-time stability for the resulting closed-loop stability. A numerical simulation example is given to validate the feasibility and effectiveness of the developed control strategy.     

An improved adaptive backstepping approach on static var compensator controller of nonlinear power systems

The transient stability problem of a single‐machine infinite‐bus system with static var compensator is solved in this paper, where the static var compensator controller is designed by an improved backstepping method combining error compensation, adaptive backstepping control, and sliding mode variable structure control. Crucially, the error compensation term, which chooses in the step of virtual control by the adaptive backstepping method, is introduced to ensure that the system states are bounded, maintaining the nonlinearity of the power systems while also improving the speed of parameter identification. Meanwhile, the Lyapunov function is constituted step by step to achieve stability of the subsystem. In addition, a parameter updating law and a nonlinear control law are explicitly given to asymptotically stabilize the closed‐loop system. Finally, a simulation is used to illustrate the effectiveness and the practicality of the proposed control approach.     

Adaptive neural network command filtered backstepping control of pure‐feedback systems in presence of full state constraints

An adaptive neural network (NN) command filtered backstepping control is proposed for the pure‐feedback system subjected to time‐varying output/stated constraints. By introducing a one‐to‐one nonlinear mapping, the obstacle caused by full stated constraints is conquered. The adaptive control law is constructed by command filtered backstepping technology and radial basis function NNs, where only one learning parameter needs to be updated online. The stability analysis via nonlinear small‐gain theorem shows that all the signals in closed‐loop system are semiglobal uniformly ultimately bounded. The simulation examples demonstrate the effectiveness of the proposed control scheme.     

A small‐gain approach for adaptive output‐feedback NN control of switched pure‐feedback nonlinear systems

This paper investigates the problem of adaptive output‐feedback neural network (NN) control for a class of switched pure‐feedback uncertain nonlinear systems. A switched observer is first constructed to estimate the unmeasurable states. Next, with the help of an NN to approximate the unknown nonlinear terms, a switched small‐gain technique‐based adaptive output‐feedback NN control scheme is developed by exploiting the backstepping recursive design scheme, input‐to‐state stability analysis, the common Lyapunov function method, and the average dwell time (ADT) method. In the recursive design, the difficulty of constructing an overall Lyapunov function for the switched closed‐loop system is dealt with by decomposing the switched closed‐loop system into two interconnected switched systems and constructing two Lyapunov functions for two interconnected switched systems, respectively. The proposed controllers for individual subsystems guarantee that all signals in the closed‐loop system are semiglobally, uniformly, and ultimately bounded under a class of switching signals with ADT, and finally, two examples illustrate the effectiveness of theoretical results, which include a switched RLC circuit system.     

Adaptive homo‐backstepping tracking control for strict‐feedback systems in presence of unknown dead‐zones

An adaptive homo‐backstepping control for nonlinear strict‐feedback systems subjected to unknown actuator dead‐zone and disturbance is investigated. A sliding‐mode‐based integral filter is constructed and used to approximate the desired feedback control in the backstepping‐like recursive design technique. Subsequently, the problem of “explosion of complexity” is solved by obviating the analytic derivatives deduction for virtual control in the conventional backstepping technology. The actuator dead‐zone dynamic is modeled as the combination of a line and a disturbance‐like term, which makes the controller design simpler. The interconnected control module and filter module in the resulting closed‐loop system satisfy the input‐to‐state practically stability‐modularity condition, provided that the small‐gain theorem is exploited to ensure the stability of closed‐loop system. The proposed approach cannot only mitigate the effect of dead‐zone but also solve the problem of explosion of complexity in the previous literature. Numerical simulations performed on a manipulator with a brushed DC motor are introduced to illustrate the effectiveness of underlying control scheme.     

Fuzzy approximation-based adaptive control of nonstrict feedback stochastic nonlinear systems with time-varying state constraints

This article develops an approximation-based fuzzy control scheme for nonstrict feedback stochastic nonlinear systems (NFSNS) with time-varying state constraints. The difficulty in constructing controller is how to conquer the algebraic loop problem caused by nonstrict feedback structure, as well as prevent the state constraints from violating. To dispose the time-varying state constraints, time-varying barrier Lyapunov function is incorporated into the backstepping design framework. The lumped uncertainties of NFSNS are approximated by the fuzzy logic systems. By virtue of fuzzy basis function, the algebraic loop problem is effectively handled. Theoretical analysis shows that the predefined state constraints are not violated and all signals of the closed-loop systems are bounded. Finally, simulation results substantiate the validity of the devised method.     

Observer‐based nonlinear feedback decentralized neural adaptive dynamic surface control for large‐scale nonlinear systems

This paper presents a nonlinear gain feedback technique for observer‐based decentralized neural adaptive dynamic surface control of a class of large‐scale nonlinear systems with immeasurable states and uncertain interconnections among subsystems. Neural networks are used in the observer design to estimate the immeasurable states and thus facilitate the control design. Besides avoiding the complexity problem in traditional backstepping, the new nonlinear feedback gain method endows an automatic regulation ability into the pioneering dynamic surface control design and improvement in dynamic performance. Novel Lyapunov function is designed and rigorous stability analysis is given to show that all the closed‐loop signals are kept semiglobally uniformly ultimately bounded, and the output tracking errors can be guaranteed to converge to sufficient area around zero, with the bound values characterized by design parameters in an explicit manner. Simulation and comparative results are shown to verify effectiveness.     

Output‐feedback adaptive tracking control of stochastic nonlinear systems using multi‐dimensional Taylor network

In this paper, an adaptive multi‐dimensional Taylor network (MTN) control scheme based on the backstepping and dynamic surface control (DSC) is developed to solve the tracking control problem for the stochastic nonlinear system with immeasurable states. The MTNs are used to approximate the unknown nonlinearities, and then based on the multivariable analog of circle criterion, an observer is first introduced to estimate the immeasurable states. By combining the adaptive backstepping technique and the DSC technique, an adaptive MTN output‐feedback backstepping DSC approach is developed. It is shown that the proposed controller ensures that all signals of the closed‐loop system are remain bounded in probability, and the tracking error converges to an arbitrarily small neighborhood around the origin in the sense of probability. Finally, the effectiveness of the design approach is illustrated by simulation results.