汽包锅炉蒸汽温度的组合混合自适应预估控制器的设计

针对汽包锅炉蒸汽温度的特点，设计了一种具有预估功能的组合混合自适应控制器，它包括几个混合自适应控制器，且这几个控制器并联工作，控制信号由这几个控制器加权获得。理论分析和现场实验结果都表明此方案对解决火电厂热工过程的非线性和大时滞有显著效果，且有快速的适应性和强的鲁棒性，为自适应技术在火电厂中的进一步应用进行了尝试。     

一种组合自适应控制器的设计及应用

设计了一种组合自适应控制器,其中包含几组并联工作的具有前馈-反馈环节的单一自适应控制器,每组单一的自适应控制器针对对象的特定运行状态而设计.控制信号由这几组控制器加权获得.应用于汽包锅炉燃烧系统的控制中,理论分析和实验结果都表明该方案对解决燃烧系统的强干扰及强耦合有显著效果,且有快速的适应性和较强的鲁棒性.     

自适应逆控制在过热汽温控制中的应用

针对火电厂过热汽温被控对象大迟延、大惯性以及参数时变的特点，提出了基于自适应逆控制的过热汽温控制策略，采用FIR滤波器进行了自适应建模并设计了相应的逆控制器及扰动消除器。仿真结果表明该方法具有较好的控制品质以及优于常规串级PID控制的鲁棒性和抗干扰性。     

汽车电子节气门的模型参考-扰动观测复合控制

为了提高电子节气门在外界扰动和参数不确定情况下的控制精度，提出了模型参考自适应控制与扰动观测器结合的复合控制方法。分析了电子节气门控制系统的工作原理，建立了电子节气门数学模型。以典型二阶系统为参考模型，给出了控制律构造方法，使用Lyapunov稳定性推导了控制律参数的自适应律，因而设计了模型参考自适应控制器。为了消除外界扰动和系统参数不确定性，设计了扰动观测器对扰动和参数进行实时估计。将模型参考自适应控制器与扰动观测器结合，提出了复合控制器的构造方法。经仿真和实验验证，存在外界扰动和参数不确定性时，复合控制器的控制精度和控制速度均优于模型参考自适应控制器，说明了复合控制器在电子节气门控制中的有效性，且复合控制器具有较强的鲁棒性。     

基于神经网络的火电厂脱硫控制系统研究

以某火电厂 ２×３００MW 机组为研究对象,设计了以石灰石 石膏湿法为工艺基础的烟气脱硫控制系统.对脱硫控制系统中吸收塔的浆液pH值的控制进行了改进,设计了基于神经网络自适应控制器.该控制器能有效提高浆液pH值的控制精度与稳定性.     

自适应线性自抗扰控制器的设计

自抗扰控制器对于抑制不确定的扰动有良好的效果,但其控制器参数较多且整定困难。为了实现自适应的线性自抗扰控制器,对线性自抗扰控制器的参数整定策略展开了研究。首先,设计了基于观测误差的线性扩张观测器参数自适应整定算法。接着,设计了自抗扰控制器线性反馈环节的参数的自适应整定算法。最后,利用李雅普诺夫方法,证明上述自适应整定算法得到的参数可以保证扩张状态观测器的观测误差和被控系统最终输出误差都收敛至零。实验结果表明:精密气浮运动平台低速工况下,自适应线性自抗扰控制器的参数在0.8s内即可迅速完成整定计算;线性扩张观测器观测误差绝对值小于2nm;被控精密气浮运动平台的速度波动不大于5%。自适应线性自抗扰控制器实现了控制器参数在线整定,控制器的性能表现满足要求。     

输入受限的非完整移动机器人的自适应模糊控制

提出了一种非完整移动机器人饱和自适应模糊轨迹跟踪控制方法,该方法基于反演技术分别设计了系统的运动学控制器和动力学控制器。运动学控制器通过引入分流控制技术解决了初始速度跳变引起的控制量突变问题,动力学控制器利用饱和函数和受限控制参数实现了其有界力矩控制。自适应模糊控制器将模糊逻辑系统与自适应方法相结合,有效消除了常规方法难以解决的系统未知不确定性对系统的影响。通过Lyapunov直接法证明了该系统是收敛且渐进稳定的。仿真结果验证了所设计控制器的良好控制性能和强鲁棒性。     

MATLAB环境下的单神经元自适应实时控制系统

利用神经元模型和自学习功能构成自适应PID控制器，在Madab实时开发环境xPC Target下建立液位实时控制系统。给出了单神经元自适应PID控制器的结构和控制算法，介绍了基于xPC Target的快速原型设计方法，即通过拖拉Simulink模块搭建被控对象的实时原型，同时以神经元自适应PID控制器完成系统的实时控制。为检验控制效果还采用了PID控制器进行液位实时控制。最终结果表明：单神经元自适应控制能克服传统PID控制器不稳定的缺点，使系统具有较好的稳定性。     

单神经元自适应PID算法在气动人工肌肉位置控制中的应用研究

文章以单支气动人工肌肉为研究对象，分别构造了PID控制器及单神经元自适应PID控制器进行位置控制。实验证明：运用单神经元自适应PID控制器，系统响应时间快，稳态精度高，有较好的控制效果。     

自校正PID控制器在疲劳试验机中的应用研究

针对常规ＰＩＤ控制器及自校正零极点配置的优缺点，提出了一种新的自校正零极点配置ＰＩＤ控制器，该控制器能根据参考模型实时地调节ＰＩＤ参数且运算量较小。仿真结果证明，该控制器具有良好的控制性能及自适应能力     

New hybrid adaptive neuro-fuzzy algorithms for manipulator control with uncertainties–Comparative study

Control of an industrial robot includes nonlinearities, uncertainties and external perturbations that should be considered in the design of control laws. In this paper, some new hybrid adaptive neuro-fuzzy control algorithms (ANFIS) have been proposed for manipulator control with uncertainties. These hybrid controllers consist of adaptive neuro-fuzzy controllers and conventional controllers. The outputs of these controllers are applied to produce the final actuation signal based on current position and velocity errors. Numerical simulation using the dynamic model of six DOF puma robot arm with uncertainties shows the effectiveness of the approach in trajectory tracking problems. Performance indices of RMS error, maximum error are used for comparison. It is observed that the hybrid adaptive neuro-fuzzy controllers perform better than only conventional/adaptive controllers and in particular hybrid controller structure consisting of adaptive neuro-fuzzy controller and critically damped inverse dynamics controller.     

Two simple and novel SISO controllers for induction motors based on adaptive passivity

The design of two single-input single-output (SISO) controllers for induction motors based on adaptive passivity is presented in this paper. The two controllers work together with a field orientation block. Because of the adaptive nature of the proposed controllers, the knowledge of the set motor-load parameters is not needed and robustness under variations of such parameters is guaranteed. Simple proportional controllers for the torque, rotor flux and stator current control loops are used, due to the control simplification given by the use of feedback passive equivalence. A new principle called the "Torque-Flux Control Principle" is also stated in this article, which considerably simplifies the controller design, diminishing the control efforts and avoiding also rotor flux estimation.     

Adaptive robust control of a class of non-affine variable-speed variable-pitch wind turbines with unmodeled dynamics

In this paper, a novel synthesis of Nussbaum-type functions, and an adaptive radial-basis function neural network is proposed to design controllers for variable-speed, variable-pitch wind turbines. Dynamic equations of the wind turbine are highly nonlinear, uncertain, and affected by unknown disturbance sources. Furthermore, the dynamic equations are non-affine with respect to the pitch angle, which is a control input. To address these problems, a Nussbaum-type function, along with a dynamic control law are adopted to resolve the non-affine nature of the equations. Moreover, an adaptive radial-basis function neural network is designed to approximate non-parametric uncertainties. Further, the closed-loop system is made robust to unknown disturbance sources, where no prior knowledge of disturbance bound is assumed in advance. Finally, the Lyapunov stability analysis is conducted to show the stability of the entire closed-loop system. In order to verify analytical results, a simulation is presented and the results are compared to both a PI and an existing adaptive controllers.     

Controller designs for constant cutting force turning machine control

A simulation study of a constant cutting force metal turning process is investigated. The process is a challenging control problem due to its nonlinear and time varying dynamics. Simulated implementations of PID, adaptive and non-adaptive sliding mode and model reference adaptive controllers were developed. Tests of the closed loop systems were performed for a range of cutting conditions including specific machined part contours that are presented here to verify the force tracking capability and flexibility of each control scheme. Results indicate that careful design and use of a fixed-gain sliding mode controller with output feedback can give roughly equivalent performance to that of more complex adaptive controllers.     

Multi-model direct adaptive decoupling control with application to the wind tunnel system

In this paper, a new multi-model direct adaptive decoupling controller is presented for multivariable processes, which includes multiple fixed optimal controllers, one free-running adaptive controller, and one re-initialized adaptive controller. The fixed controllers provide initial control to the process if its model lies in the corresponding region. For each controller selected, the re-initialized adaptive controller uses the values of this particular controller to improve the adaptation speed. This controller may replace the fixed controller at a later stage according to the switching criterion which is to select the best one among all controllers. A free-running adaptive controller is also added to guarantee the overall system stability. Different from the multiple models adaptive control structure proposed in Narendra, Balakrishnan, and Ciliz [Adaptation and learning using multiple models, switching, and tuning. IEEE Control Syst. Mag. 15, 37-51 (1995)], the method not only is applicable to the multi-input multi-output processes but also identifies the decoupling controller parameters directly, which reduces both the computational burden and the chances of a singular matrix during the process of determining controller parameters. Several examples for a wind tunnel process are given to demonstrate the effectiveness and practicality of the proposed method.     

State and output feedback fuzzy variable structure controllers for multivariable nonlinear systems subject to input nonlinearities

This paper presents three fuzzy adaptive controllers for a class of uncertain multivariable nonlinear systems with both sector nonlinearities and dead zones: two first controllers are state feedbacks and the last controller is an output feedback. The design of the first controller concerns systems with symmetric and positive definite control–gain matrix, while the second control design is extended to the case of nonsymmetric control–gain matrix thanks to an appropriate decomposition, namely the product of a symmetric positive definite matrix, a diagonal matrix with diagonal entries +1 or ?1, and a unity upper triangular matrix. The third controller is an output feedback extension of the second controller. In this controller, a high-gain observer is incorporated to estimate the unmeasurable states. An appropriate adaptive fuzzy logic system is used to reasonably approximate the uncertain functions. A Lyapunov approach is adopted to derive the parameter adaptation laws and prove the stability of those control systems as well as the exponential convergence of their underlying tracking errors within an adjustable region. The effectiveness of the proposed fuzzy adaptive controllers is illustrated through simulation results.     

Robust control of feedback linearizable large-scale systems

The design of decentralized controllers for a class of uncertain interconnected nonlinear systems is considered. The uncertainty considered here is time-varying and appears at each subsystem and interconnections. Two control techniques are explored. The first one, namely, the feedback linearization control, involves a known and autonomous nonlinear system. The second one, namely, the robust control, is especially suitable if any uncertainty and/or time-varying factors are involved in the nonlinear dynamics. These two controllers are combined to stabilize a class of large-scale nonlinear uncertain systems. Two decentralized robust controllers, nonadaptive and adaptive, are proposed and those results are proved.     

异步电动机自适应控制的研究

介绍了一种异步电动机的自适应控制,在Matlab/Simulink中对异步电动机控制系统的各个部分进行了仿真建模,完成了该控制系统的仿真.该方法用自适应方法将系统中莱一信号作为虚拟控制信号,进而使系统简化,再应用李亚普诺夫稳定性理论实现该虚拟控制信号,达到控制要求.谊设计方法的仿真结果表明,用该方法设计的异步电动机自适应控制系统能够获得较好的转速跟踪和磁链跟踪性能.     

Experimental evaluation of adaptive three-tank level control

Liquid level control through regulation of mass flow rates is an important application in various areas of the power industry. Very often a PID controller is used for these applications. This paper compares a nonconventional PID controller and three different types of adaptive controller, a direct model reference adaptive controller (MRAC), an indirect MRAC with Lyapunov estimation, and an indirect MRAC with recursive least-squares (RLS) updating estimation, for liquid level control. By implementing all four controllers on a three-tank system, the performances of each are compared. All controllers track a sinusoidal input very well and overall exhibit somewhat varying performance. The direct MRAC and the indirect MRAC with RLS estimation give the best performance. With Lyapunov estimation and RLS estimation, all the system parameter estimates converge to the reference model values. However, RLS estimation has a much faster convergence. It is concluded that adaptive liquid level control is an improvement over traditional liquid level control when precise level control in three coupled tanks is desired.