汽包锅炉蒸汽温度的组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适应控制器，它包括几个自适应控制器，且这几个控制器并联工作，稳定控制信号由这几个控制器加权获得。理论分析和仿真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.     

使用低阶参考模型的HMRACS设计方法

利用高阶被控制对象跟随低价参考模型的方法来设计ＭＰＡＣＳ是自适应控制领域的研究课题之一。本文对文献［１］进行了改进，采用混合自适应控制器，从而提高了使用低阶参考模型的ＭＲＡＣＳ的鲁棒性。     

自适应模糊神经网络在炉温控制中的应用

针对复杂工业过程系统的特点,提出了一种自适应模糊神经网络控制器,这种控制器由模糊神经网络控制器(FNNC)和模型网络(MNN)组成,具有自适应学习能力.着重讨论了自适应模糊神经网络的混合学习算法,给出了适于非线形时滞电阻加热炉的控制方案.经仿真和加热炉温控实验表明,控制性能良好.     

不确定加工过程的模糊变结构控制

针对加工过程的不确定性和非线性影响控制系统的鲁棒性和稳定性，设计了模糊变结构控制器（FVSC）以及自适应调节切换函数的参数，有效地削弱了不确定加工过程系统的抖振，并保持了变蛄构控制（VSC）的强鲁棒性能。仿真结果表明，这类混合控制器对不确定加工过程的控制效果是明显的。     

车辆纵向运动跟车间距控制的研究

采用一种可以实施混合学习算法的自适应神经一模糊推理系统(ANFIS)结构，对车辆纵向运动跟车间距控制问题进行了研究，设计了基于ANFIS结构的跟车间距控制器，通过仿真对ANFIS结构控制器中的各参数进行了优化，结果表明，运用ANFIS结构进行跟车间距控制是可行的，控制器可取得较为满意的控制效果。     

车用燃料电池混合动力系统自适应控制研究

在MATLAB/Simulink中建立了燃料电池/锂电池混合动力系统仿真模型,利用自适应反演滑模控制理论,设计了基于状态机能量管理策略的电流/电压控制器,使燃料电池尽可能工作在高效稳定区域,实现了混合动力系统在不同汽车运行工况下驱动与制动的可持续性。仿真结果表明,该控制器对燃料电池电流的追踪响应速度快,追踪精度高,抗干扰能力强,且混合动力系统在不同工况下燃料电池功率输出稳定、切换平滑,提高了燃料电池的耐久性。     

Supervisory hybrid control of piezoelectric actuators utilized in tracking piecewise continuous trajectories

This article presents a supervisory hybrid control design for piezoelectric actuators utilized in tracking trajectories with intermittent jump discontinuities. We use a previously developed robust adaptive controller and a standard PID controller to construct this hybrid control strategy. We show that when the sub-controllers are used for step tracking, while primarily tuned for continuous trajectory tracking, large undesirable oscillations occur. Conversely, when the controllers are retuned for step tracking, their performance degrades in tracking high-frequency continuous trajectories. Thus, a supervisory hybrid controller is developed to track desired trajectories with occasional discontinuities, using both the robust adaptive and the PID controllers. The robust adaptive controller performs as the primary controller for tracking the continuous segments of the desired trajectory, while the PID controller is activated when the steps occur. Results indicate that the proposed supervisory hybrid controller outperforms both sub-controllers in tracking high-frequency trajectories with intermittent discontinuities.     

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.     

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.     

Robust fault detection of turbofan engines subject to adaptive controllers via a Total Measurable Fault Information Residual (ToMFIR) technique

This paper provides a new design of robust fault detection for turbofan engines with adaptive controllers. The critical issue is that the adaptive controllers can depress the faulty effects such that the actual system outputs remain the pre-specified values, making it difficult to detect faults/failures. To solve this problem, a Total Measurable Fault Information Residual (ToMFIR) technique with the aid of system transformation is adopted to detect faults in turbofan engines with adaptive controllers. This design is a ToMFIR-redundancy-based robust fault detection. The ToMFIR is first introduced and existing results are also summarized. The Detailed design process of the ToMFIRs is presented and a turbofan engine model is simulated to verify the effectiveness of the proposed ToMFIR-based fault-detection strategy.     

电气传动系统PID自适应控制研究

本文利用神经网络组成的PID自适应控制器，对一大变负载电气伺服系统进行控制，仿真与实验表明：该控制器优于一般伺服控制器的性能并具有较强的鲁棒性。     

Ultra-precise tracking control of piezoelectric actuators via a fuzzy hysteresis model

In this paper, a novel Takagi-Sugeno (T-S) fuzzy system based model is proposed for hysteresis in piezoelectric actuators. The antecedent and consequent structures of the fuzzy hysteresis model (FHM) can be, respectively, identified on-line through uniform partition approach and recursive least squares (RLS) algorithm. With respect to controller design, the inverse of FHM is used to develop a feedforward controller to cancel out the hysteresis effect. Then a hybrid controller is designed for high-performance tracking. It combines the feedforward controller with a proportional integral differential (PID) controller favourable for stabilization and disturbance compensation. To achieve nanometer-scale tracking precision, the enhanced adaptive hybrid controller is further developed. It uses real-time input and output data to update FHM, thus changing the feedforward controller to suit the on-site hysteresis character of the piezoelectric actuator. Finally, as to 3 cases of 50 Hz sinusoidal, multiple frequency sinusoidal and 50 Hz triangular trajectories tracking, experimental results demonstrate the efficiency of the proposed controllers. Especially, being only 0.35% of the maximum desired displacement, the maximum error of 50 Hz sinusoidal tracking is greatly reduced to 5.8 nm, which clearly shows the ultra-precise nanometer-scale tracking performance of the developed adaptive hybrid controller.     

基于CMAC网络的主动隔振控制技术

针对主动隔振系统存在非线性,提出一种基于CMAC神经网络控制器的主动控制方法。由于系统的次通道使得控制器参数无法直接利用振动误差信号来调整,提出一种改进的同时扰动自适应算法,并证明该算法在数学期望上是一种随机梯度法。针对系统的非线性情况进行了仿真和试验研究,结果表明:与传统的FXLMS自适应前馈控制方法相比,所提出的方法对于存在非线性的周期性振动的隔振效果明显。     

Adaptive fuzzy sliding-mode controller of uncertain nonlinear systems

This paper deals with the design of adaptive fuzzy sliding-mode controllers for the T-S fuzzy model based on the Lyapunov function. It is shown that the Lyapunov function can be used to establish fuzzy sliding surfaces by solving a set of linear matrix inequalities (LMIs). The design of the fuzzy sliding surfaces and the adaptive fuzzy sliding-mode controllers is proposed. The adaptive mechanism is also used to deal with unknown parameter perturbations and external disturbances. Two examples illustrate the feasibility of the proposed methods.     

Robust adaptive vibration control of a flexible structure

Different types of L1 adaptive control systems show that using robust theories with adaptive control approaches has produced high performance controllers. In this study, a model reference adaptive control scheme considering robust theories is used to propose a practical control system for vibration suppression of a flexible launch vehicle (FLV). In this method, control input of the system is shaped from the dynamic model of the vehicle and components of the control input are adaptively constructed by estimating the undesirable vibration frequencies. Robust stability of the adaptive vibration control system is guaranteed by using the L1 small gain theorem. Simulation results of the robust adaptive vibration control strategy confirm that the effects of vibration on the vehicle performance considerably decrease without the loss of the phase margin of the system.