基于SOC单片机的智能控制器设计

应用SoC单片机设计了新型高可靠智能控制器,通过故障自诊断、故障定位和冗余的设计方法,使得控制器具有容错功能;同时,控制器应用了模糊PID综合切换控制算法,提高了控制鲁棒性。     

卫星控制系统离散积分滑模容错控制

卫星控制系统是采样控制系统,对它来讲,不确定性离散系统容错控制方法非常重要.本文提出了一种离散积分滑模容错控制方案,用于调节动量轮有故障的卫星姿态,其主要部分是设计控制器和控制分配方法.本文对含有匹配和不匹配的不确定性的离散多输入多输出系统,设计了一种离散积分滑模控制器,分析了匹配的和不匹配的不确定性对闭环系统稳定性的影响,得出了系统状态的界.应用可达集方法确定控制分配方案时,本文改变可达集表面的搜索顺序,提高了求解速度.以五动量轮卫星模型为例,将本文方法应用于含多故障、健康因子不准确、匹配不确定性和不匹配不确定性的卫星控制系统中,理论和仿真的结果一致.     

执行器故障时变系统的PID迭代容错控制

对存在执行器故障的连续线性时变系统，给出了PID型迭代学习容错控制律的收敛条件。对连续时变故障系统设计了一种PID迭代学习容错控制律，在[λ]范数意义下给出了故障系统PID型迭代容错控制器收敛的充要条件；基于Schur补原理和不等式变换，将容错控制器收敛条件转换成线性矩阵不等式，当迭代学习收敛速度设定时，基于线性矩阵不等式能快速确定最优迭代控制增益，避免了迭代控制增益设置的盲目性。旋转控制系统的数值仿真，验证了PID迭代容错控制器优良的容错性能和跟踪性能。     

基于FAEKF算法的线控转向传感器的容错控制

传统线控转向汽车传感器容错控制策略忽视了可变噪声的影响，这会导致容错控制精度偏低，因此，基于模糊自适应扩展卡尔曼滤波(FAEKF)提出一种改进的主动容错方法。建立线控转向系统与汽车三自由度相结合的非线性动力学模型，并利用模糊控制器对传感器测量噪声的权重进行实时调整；结合残差理论和数据融合实现传感器的故障诊断与补偿；完成主要传感器的容错控制。仿真结果表明，当单个传感器发生故障时，该方法具有较好的抗噪性和逼近能力，容错控制精度明显提高。     

模糊PID矢量控制在异步电机调速中的应用

为了提高三相异步电机矢量控制的性能,在传统的转速PID控制器的基础上,建立模糊PID控制器,利用Matlab/Simulink搭建基于三相异步电动机转速控制的模糊PID系统,分别使用常规PID控制器与模糊PID控制器进行控制,并进行比较。仿真结果表明模糊控制能使系统取得较好的控制性能并具有较强的鲁棒性。     

无刷直流电机的模糊神经网络控制设计

在无刷直流电机(BLDCM)的控制上,传统PID等控制方法存在或多或少的不足.在模糊PID控制的基础上提出了一种模糊神经网络PI控制器的设计方法.该方法结合了模糊逻辑与神经网络,使得模糊控制器模拟了人的控制功能,不仅对环境变化有较强的适应能力,还拥有自学习能力.相比模糊PID控制,其具有计算量小、稳定性强等特点.对BLDCM进行建模与分析;在BLDCM数学模型的基础上,分别设计模糊PID控制器和模糊神经网络PI控制器;对设计的控制器进行仿真验证并分析.实验结果表明,模糊神经网络PI控制具有跟踪性能好、超调小、响应快、脉动小等优点,其动静态特性均优于模糊PID控制.     

液位传感器容错控制方法研究

传统的PID(比例-积分-微分)控制法在液位过程控制中占据主要地位,但由于液位控制系统,特别是其系统传感器容易发生故障,使得PID控制法具有抗故障能力不足的缺陷,对生产生活造成重大损失,因此需采用容错控制方法使得液位传感器发生故障时仍能较为正常运行,提出状态观测器估计传感器液位故障值的容错控制方法,使状态观测器对故障状态产生观测跟踪,将故障状态作用于原控制器,并使原控制器弃置已经存在故障的传感器信号,使得故障值被抵消。     

基于动态控制分配的卫星姿态控制系统容错控制北大核心CSCD

针对卫星姿态控制系统执行机构故障情况下的姿态跟踪问题,研究了一种基于动态控制分配的容错控制方法。首先,考虑由卫星转动惯量不确定性与外界干扰组成复合干扰,设计了基于干扰观测器的反步姿态跟踪控制器,利用干扰观测器对复合干扰进行估计,并且采用李雅普诺夫方法分析了闭环系统的稳定性;其次,针对发生乘性执行机构故障的卫星姿态控制系统,设计了基于动态控制分配的容错控制方法,该方法无需对控制律进行调整,而是利用故障信息调整目标函数,通过动态控制分配方法实现容错控制。仿真结果表明,该方法能够在执行机构发生故障情况下有效完成姿态跟踪。     

基于动态控制分配的卫星姿态控制系统容错控制

针对卫星姿态控制系统执行机构故障情况下的姿态跟踪问题,研究了一种基于动态控制分配的容错控制方法。首先,考虑由卫星转动惯量不确定性与外界干扰组成复合干扰,设计了基于干扰观测器的反步姿态跟踪控制器,利用干扰观测器对复合干扰进行估计,并且采用李雅普诺夫方法分析了闭环系统的稳定性;其次,针对发生乘性执行机构故障的卫星姿态控制系统,设计了基于动态控制分配的容错控制方法,该方法无需对控制律进行调整,而是利用故障信息调整目标函数,通过动态控制分配方法实现容错控制。仿真结果表明,该方法能够在执行机构发生故障情况下有效完成姿态跟踪。     

四旋翼无人机时延模糊自抗扰容错控制

针对四旋翼无人机系统执行器故障问题，为改善飞行控制系统性能，提出一种时延模糊自抗扰容错控制。首先，根据四旋翼无人机系统非线性数学模型和执行器故障模型，选择模糊自抗扰控制器作为基准控制器，在未发生执行器故障的情况下，使飞行控制系统保持稳定；其次，在发生执行器故障的情况下，利用时延控制技术估计故障信息，并与模糊自抗扰控制相结合，实现容错控制；最后，对所研究的容错控制算法进行数值仿真分析，仿真结果表明：把时延控制与模糊自抗扰控制相结合，能有效调节执行器故障，使飞行控制系统对故障产生的干扰具有良好的鲁棒性。     

Third‐order sliding mode fault‐tolerant control for satellites based on iterative learning observer

The attitude fault‐tolerant control problem for a satellite with reaction‐wheel failures, uncertainties, and unknown external disturbances is investigated in this paper. Firstly, an iterative learning observer (ILO) is proposed to achieve fault detection, isolation, and estimation. Secondly, based on the ILO, a third‐order sliding mode controller is proposed to stabilize the satellite attitude rapidly under unknown external disturbances and reaction‐wheel faults. Thirdly, the asymptotically stability of the ILO and the third‐order sliding mode controller is proved by using the Lyapunov stability theory. Finally, simulation results demonstrate that the proposed control scheme is more effective and feasible by comparing with other fault‐tolerant control approach.     

Fault tolerant control of multivariable processes using auto-tuning PID controller.

Fault tolerant control of dynamic processes is investigated in this paper using an auto-tuning PID controller. A fault tolerant control scheme is proposed composing an auto-tuning PID controller based on an adaptive neural network model. The model is trained online using the extended Kalman filter (EKF) algorithm to learn system post-fault dynamics. Based on this model, the PID controller adjusts its parameters to compensate the effects of the faults, so that the control performance is recovered from degradation. The auto-tuning algorithm for the PID controller is derived with the Lyapunov method and therefore, the model predicted tracking error is guaranteed to converge asymptotically. The method is applied to a simulated two-input two-output continuous stirred tank reactor (CSTR) with various faults, which demonstrate the applicability of the developed scheme to industrial processes.     

Fuzzy attitude control for a nanosatellite in low Earth orbit

In order to develop and introduce intelligent systems in the space field, an adaptive fuzzy logic controller is designed for a nanosatellite. Attitude determination and control subsystem (ADCS) and its performance and efficiency are compared with a traditional proportional integrative derivative (PID) controller. Fuzzy controllers have already been studied for satellite attitude control; however their performance has not been compared with the classical PID controllers typically being implemented on board spacecrafts currently. Both controllers have been designed and implemented in order to be tested on board a nanosatellite (QBITO) in a nearby mission (QB50), a constellation of 50 nanosatellites. Due to the requirements imposed by the mission, the orbit, and the significant limitations in the power available in these small spacecrafts, an efficient ADCS is required in order to fulfill the mission objectives. The comparison between the classical PID and the fuzzy controllers shows that the fuzzy controller is much more efficient in single maneuver (up to 65% less power required), achieving better precision in general than the PID. This shows that the use of this type of intelligent control systems is a great advantage over conventional control systems currently being used in satellite attitude control, and open new possibilities of application of intelligent controllers in the field of space technologies.     

Online tuning fuzzy PID controller using robust extended Kalman filter

Fuzzy PID controllers have been developed and applied to many fields for over a period of 30 years. However, there is no systematic method to design membership functions (MFs) for inputs and outputs of a fuzzy system. Then optimizing the MFs is considered as a system identification problem for a nonlinear dynamic system which makes control challenges. This paper presents a novel online method using a robust extended Kalman filter to optimize a Mamdani fuzzy PID controller. The robust extended Kalman filter (REKF) is used to adjust the controller parameters automatically during the operation process of any system applying the controller to minimize the control error. The fuzzy PID controller is tuned about the shape of MFs and rules to adapt with the working conditions and the control performance is improved significantly. The proposed method in this research is verified by its application to the force control problem of an electro-hydraulic actuator. Simulations and experimental results show that proposed method is effective for the online optimization of the fuzzy PID controller.     

A Learning-Based Fault Tolerant Tracking Control of an Unmanned Quadrotor Helicopter

This paper presents a novel learning-based fault tolerant tracking control approach by using an extended Kalman filter (EKF) to optimize a Mamdani fuzzy state-feedback tracking controller. First, a robust state-feedback tracking controller is designed as the baseline controller to guarantee the expected system performance in the fault-free condition. Then, the EKF is employed to regulate the shape of membership functions and rules of fuzzy controller to adapt with the working conditions automatically after the occurrence of actuator faults. Next, based on the modified fuzzy membership functions and rules, the baseline controller is readjusted to properly compensate the adverse effects of actuator faults and asymptotically stabilize the closed-loop system. Finally, in order to verify the effectiveness of the proposed method, several groups of numerical simulations are carried out by comparing the performance of a tracking control scheme and the presented technique. Simulation results demonstrate that the proposed method is effective for optimizing the fuzzy tracking controller on-line and counteracting the side effects of actuator faults, and the control performance is significantly improved as well.     

时滞系统模糊整定PID控制的仿真研究

文中引入1／1pade级数逼近纯滞后环节，根据两步法设计内模控制器。由等效式可以看出，单位反馈控制器可以由带滤波器的PID控制器文现，内模调节即转化成为PID控制器的参数整定。采用模糊自调整控制方案整定PID参数，同时结合针对时滞系统的模糊控制规则和一般二维模糊控制器的通用规则，设计模糊控制器，充分利用模糊控制、内模控制、PID控制的优点。将所设计的系统应用于具有大时滞、大惯性特点的电厂主汽温控制系统检验其性能，仿真试验证明了这一方法具有很好的鲁棒性、控制精度和抗干扰能力。     

非对称模糊PID控制在二质量伺服系统中的应用

针对伺服系统在低速情况下具有较强的摩擦现象,是一个强非线性系统,本文将模糊控制与PID控制手段结合起来,提出采用非对称模糊PID控制方法,将其引入伺服系统的位置环中,同时建立了基于MATLAB/SIMULINK的系统仿真图。通过仿真试验,验证了非对称模糊PID控制方法具有较好的控制精度和稳定性,该控制方法可使系统的性能有所提高。     

Fault detection and identification—A filter investigation

This paper develops a new active fault‐tolerant control system based on the concept of analytical redundancy. The novel design presented here consists of an observation filter–based fault detection and identification system integrated with a nonlinear model predictive controller. A number of observation filters are designed, integrated with the nonlinear controller, and tested before reaching the final design, which comprises an unscented Kalman filter for fault detection and identification together with a nonlinear model predictive controller to form an active fault‐tolerant control system.     

MR-SAS and electric power steering variable universe fuzzy PID integrated control

In order to solve the problem of MR-SAS and electric power steering (EPS) integrated control, the suspension and steering system integrated dynamic model was established, and the variable universe fuzzy PID integrated controller was designed. Due to the difficulty of self-adaptive and the superiority of compound control of fuzzy control, the variable universe fuzzy PID compound controller based on fuzzy inference was designed. The fuzzy on–off control based on trapezoidal membership function was used to the fuzzy control and PID control. And the random road input and steering wheel angle step input simulation experiment was carried out in the integrated system. Compared with the passive system and separate control, experimental results show that the integrated control system has obvious superiority.