一种新型的智能非线性PID控制器

在分析总结标准PID控制器、各种变形PID控制器、非线性PID控制器特点的基础上，提出了一种智能非线性PID(INPID)型变结构控制器。将该INPID控制器应用于典型带延迟一阶对象和二阶对象控制，并与传统PID进行了对比，证明该控制器具有良好的控制效果、广泛的对象适应性和很强的鲁棒性。仿真结果表明．在控制量受限越强和系统参数变化范围越大的情况下，其优势越突出。     

某型发动机数控系统的相似参数自适应控制

针对航空发动机在全飞行包线非线性和时变的特点,提出了参数自适应PID控制器设计方法,研究了用最小二乘法和相似参数法两种参数估计器进行在线参数辨识和参数整定的问题。通过与发动机线性模型和部件级非线性模型的仿真,对两种控制算法进行了比较,确定了相似参数法自适应PID控制器具有稳定性好,计算简单,适用于全包线等优点。将相似参数法自适应PID控制器用于某型航空发动机全权限数控系统,通过试车和试飞,验证了该方法的优点。      

基于滑模控制方法的航空发动机控制系统改进设计

为改善传统基于线性控制方法(PID控制)设计航空发动机控制系统在极限保护方面的不足,提出利用非线性控制理论——滑模控制取代原有控制系统中的线性控制器,设计了发动机稳态控制器与基于max-min控制逻辑的极限保护器的综合系统。与传统PID控制方法的控制效果相比较,滑模控制方法可在保证发动机不超限的情况下充分发挥发动机潜能。讨论了边界层厚度等因素对滑模控制抖动的影响。采用滑模方法设计的控制器在硬件在回路平台(HIL)上通过了仿真验证,满足实时性要求。     

航空发动机神经网络自学习PID控制

将神经网络与传统的PID控制相结合,构成神经网络自学习PID控制,用神经网络在线整定PID控制器的比例、积分及微分三个参数,使被控对象跟踪理想参考模型的输出。该系统具有自学习能力,能适用于非线性、时变的被控对象。将神经网络自学习PID控制方法用于航空发动机全包线控制以及蜕化发动机的控制,进行了数字仿真,验证了该方法的有效性。     

基于遗传算法的航空发动机PI控制器参数优化方法

P ID控制器参数整定与优化一直是自动控制领域研究的问题。采用遗传算法进行的PID参数整定与优化是一种全局最优且与初值无关的优化方法。本文结合某型航空发动机,利用遗传算法对发动机单变量及双变量P I控制器参数进行优化。仿真结果表明:该方法对PI参数整定具有比较好的综合控制性能,不失为一种具有较好实用价值的航空发动机P I控制器参数整定与优化方法。      

基于人工蜂群算法优化的燃气发生器压强自适应模糊免疫PID控制

固冲发动机燃气流量控制系统因具有较强的非线性和时变性,导致其控制问题较难解决。为了实现对燃气压强的精确闭环控制,设计了基于人工蜂群算法优化的自适应模糊免疫PID (ABC-AFI-PID)控制器。控制器的比例系数由模糊免疫控制器在线修正,积分和微分系数由自适应模糊控制器实时调整,并应用人工蜂群算法对控制器的设计参数进行鲁棒优化。采用ABC-AFI-PID控制器、自适应模糊PID (AF-PID)控制器和传统PID控制器分别对某滑盘阀式流量控制系统的线性和非线性模型进行仿真,来验证控制器在设计工作点(7.24MPa)附近以及全压强调节范围内的动态性能和稳态性能。结果表明:在不同的工况下,ABC-AFI-PID控制器体现出良好的品质。相比于AF-PID控制器可将压强响应速度提高约1.8～2.5倍,相比于传统的PID控制器可将压强响应速度提高约4.6～5.1倍,并且其超调量也被控制在7.14%以内。该控制器在快速性、稳定性和鲁棒性上均展现出了巨大优势,显著地提高了燃气流量控制系统的性能。     

CMAC-PID控制在飞机防滑刹车中的应用

提出了一种基于CMAC神经网络与PID相结合的控制方法,利用传统的PID控制器实现反馈控制,保证系统的稳定性并抑制扰动;利用CMAC神经网络控制器实现前馈控制,保证系统的控制精度和响应速度。以某型飞机为例,针对不同的跑道(干、湿、冰)情况,将该方法和传统的PID控制方法在MATLAB环境下进行了数字仿真。仿真结果表明:基于CMAC-PID控制方法较传统的PID控制方法,可以大大地提高飞机防滑刹车效率,具有更好的刹车控制效果,并具有较强的鲁棒性,为飞机防滑刹车系统的控制提供一条新的思路。     

鲁棒控制用于涡喷发动机控制器设计

基于LMI(LinearMatrixInequality,线性矩阵不等式)的方法,将PID控制律与鲁棒技术交叉结合,考虑了控制受限条件,提出了一种基于LMI的输出反馈PI控制器分段设计的切换方法。这一技术应用于某型涡喷发动机全功能的数字式电子控制器的稳态控制中,并介绍了该型涡喷发动机控制系统的组成,包括硬件、软件、步进电机执行机构及燃油计量装置。设计的电子控制器在发动机半实物仿真平台上进行了鲁棒性能的验证,实现了电子控制器的等转速调节功能和逻辑监控保护功能,满足涡喷发动机多功能FADEC控制要求。      

自适应PID控制系统仿真

针对常规PID控制器不能满足非线性、时变系统的控制要求的问题,本文将自适应控制思想与PID控制器相结合,合成一种自适应PID控制解决方案,并设计了一个自适应PID控制器。该方案运用专家控制策略,自动整定PID参数。仿真结论表明,在系统参数不确定或者发生变化时,该控制器的控制效果明显优于常规PID控制器。     

基于MRP的全局稳定的PID刚体姿态控制

针对刚性航天器姿态控制问题,建立了由修正Rodrigues参数(MRP)表示的混杂姿态模型,并基于此模型设计了一种具有迟滞特性的非线性比例-积分-微分(PID)切换控制器.该控制器包含一个对克里奥利力矩和期望机动力矩的前馈补偿项和一个用于消除轨迹跟踪误差的PID反馈项.通过一个特别的Lyapunov函数分析得到了全局渐...     

Adaptive recurrent-neural-network control for linear inductionmotor

In this study an adaptive recurrent-neural-network controller (ARNNC) is proposed to control a linear induction motor (LIM) servo drive. First, the secondary flux of the LIM is estimated with an adaptive flux observer on the stationary reference frame and the feedback linearization theory is used to decouple the thrust force and the flux amplitude of the LIM. Then, an ARNNC is proposed to control the mover of the LIM for periodic motion. In the proposed controller, the LIM servo drive system is identified by a recurrent-neural-network identifier (RNNI) to provide the sensitivity information of the drive system to an adaptive controller. The backpropagation algorithm is used to train the RNNI on line. Moreover, to guarantee the convergence of identification and tracking errors, analytical methods based on a discrete-type Lyapunov function are proposed to determine the varied learning rates of the RNNI and the optimal learning rate of the adaptive controller. The effectiveness of the proposed control scheme is verified by both the simulated and experimental results. Furthermore, the advantages of the proposed control system are indicated in comparison with the sliding mode control system     

一种基于LMI的航空发动机输出反馈PI控制

基于LMI(LinearMatrixInequality,线性矩阵不等式),提出一种航空发动机PI输出反馈控制器设计方法。利用被控对象中控制向量2范数上界的限制,提出了一种改进ILMI(IterativeLMI)算法,用于解决控制受约束的航空发动机PI控制器的一种LMI描述求解问题。以某型双转子涡喷发动机为被控对象,基于LMI进行了PI输出反馈控制器的设计,并分区域在飞行包线进行了航空发动机控制系统仿真验证。结果表明,所得控制器满足预期设计要求,同时具有一定鲁棒性。     

电液力伺服系统的神经网络并行自适应预测 PI 控制

考虑电液伺服系统的复杂非线性和不确定性特性,提出一类基于神经网络的并行自适应预测PI控制结构,该结构使控制参数的调整和系统的实时控制操作可并行进行,不仅做到了神经模型和控制器的在线辨识和设计,而且避免了神经网络方法通常存在的实时控制的困难,使复杂系统的在线学习控制成为可能。仿真结果表明该控制器具有良好的适应性和鲁棒性。      

On-line gain-tuning IP controller using RFNN

In this study an integral-proportional (IP) controller with on-line gain-tuning using a recurrent fuzzy neural network (RFNN) is proposed to control the mover position of a permanent magnet linear synchronous motor (PMLSM) servo drive system. The structure and operating principle of the PMLSM are first described in detail. A field-oriented control PMLSM servo drive is then introduced. After that, an IP controller with on-line gain tuning using an RFNN is proposed to control the mover of the PMLSM for achieving high-precision position control with robustness. The backpropagation algorithm is used to train the RFNN on line. Moreover to guarantee the convergence of tracking error for the periodic step-command tracking, analytical methods based on a discrete-type Lyapunov function are proposed to determine the varied learning rates of the RFNN. Furthermore, the proposed control system is implemented in a PC-based computer control system, Finally, the effectiveness of the proposed PMLSM servo drive system is demonstrated by some simulated and experimental results. Accurate tracking response and superior dynamic performance can be obtained due to the powerful on-line learning capability of the RFNN. In addition, the proposed on-line gain-tuning servo drive system is robust with regard to parameter variations and external disturbances     

Inherent Overload Protection for the Series Resonant Converter

The overload characteristics of the full bridge series resonant power converter are considered. This includes analyses of the two most common control methods presently in use. The first of these uses a current zero crossing detector to synchronize the control signals and is referred to as the ? controller. The second is driven by a voltage controlled oscillator and is referred to as the ? controller. It is shown that the ? controller has certain reliability advantages in that it can be designed with inherent short circuit protection. Experimental results are included for an 86 kHz converter using power metal-oxide-semiconductor field-effect transistors (MOSFETs).     

Robust wavelet neuro control for linear brushless motors

Design, simulation and experimental implementation of a wavelet basis function network learning controller for linear brushless dc motors (LBDCM) are considered. Stability robustness with position tracking is the primary concern. The proposed controller deals mainly with external disturbances, e.g. nonlinear friction force and payload variation in motion control of linear motors. It consists of two parts, one is a state feedback component, and the other one is a learning feedback component. The state feedback controller is designed on the basis of a simple linear model, and the learning feedback component is a wavelet neural controller. The attenuation effect of wavelet neural networks on friction force is first verified by the numerical method. The learning effect of wavelet neural networks on friction force is also shown in the numerical results. Then, a wavelet neural network is applied on a real LBDCM to on-line suppress the friction force, which may be variable due to the different lubrication. The effectiveness of the proposed control schemes is demonstrated by simulated and experimental results.     

A fault-tolerant neural aided controller for aircraft auto-landing

This paper presents a neural-aided controller that enhances the fault tolerant capabilities of a high performance fighter aircraft during the landing phase when subjected to severe winds and failures such as stuck control surfaces. The controller architecture uses a neural controller aiding an existing conventional controller. The neural controller uses a feedback error learning mechanism and employs a dynamic Radial Basis Function neural network called Extended Minimal Resource Allocating Network (EMRAN), which uses only on-line learning and does not need a priori training. The conventional controller is designed using a classical design approach to achieve the desired autonomous landing profile with tight touchdown dispersions called herein as the pillbox. This design is carried out for no failure conditions but with the aircraft being subjected to winds. The failure scenarios considered in this study are: (i) Single faults of either aileron or elevator stuck at certain deflections, and (ii) double fault cases where both the aileron and elevator are stuck at different deflections. Simulation studies indicate that the designed conventional controller has only a limited failure handling ability. However, neural controller augmentation considerably improves the ability to handle large faults and meet the strict touchdown dispersion requirements, thus enlarging the fault-tolerance envelope.     

机械手的神经网络自适应滑动模控制器设计

 针对多关节机械手的鲁棒跟随控制器设计问题，提出了一种新的机械手神经网络自适应滑动模控制器设计方法，机械手的动力学非线性假设是完全未知的。在提出的控制结构中，高斯径向基函数神经网络用于在线补偿机械手的动力学非线性，参数学习律由稳定性理论得到。给出了系统稳定性和参数收敛性的证明。最后提出方法的可行性通过仿真得到验证。     

基于模仿强化学习的固定翼飞机姿态控制器

研究了基于模仿强化学习的飞机姿态控制器。首先,建立专家经验数据集,并利用行为克隆对控制网络参数初始化;而后,控制网络利用强化学习和监督学习混合模式训练,通过奖励函数塑形和经验数据集监督学习引导强化学习算法快速收敛,使姿态控制器姿态响应优化的同时符合专家经验。控制网络输入为飞机姿态角误差、角速度等状态变量,输出控制增稳系统指令。实验表明,模仿强化学习控制器能够实现不同初始条件下飞机姿态角快速响应并与经验数据相符。     

临近空间动能拦截器神经反演姿态控制器设计

为满足临近空间动能拦截器姿态控制快速性、准确性和鲁棒性的要求,设计了一种自适应神经反演姿态控制器。首先,建立了姿控发动机侧喷干扰模型,并推导了包含质心漂移、参数摄动和外界干扰的三通道强耦合模型;其次,设计了自适应神经反演姿态控制器,为提高控制精度,采用径向基函数（RBF）神经网络对各个通道的不确定项进行估计和补偿,并基于最小学习参数的思想,将神经网络学习参数拟合为一个参数,提高了RBF计算效率,保证了估计的实时性。最后,采用伪速率（PSR）脉冲调制器将设计的连续控制律转化为脉冲控制律,实现了拦截器的变推力控制,并克服了脉冲脉宽调制（PWPF）调制器相位滞后问题。数字仿真表明,所设计的控制器收敛速度快,控制精度高,对强扰动具有鲁棒性。