考虑延迟的线控转向二自由度内模控制

针对线控转向系统中由于信号传输、机械间隙和摩擦等因素引起的响应延迟问题，设计了二自由度内模控制策略以提高转角跟踪精度。将延迟模型与线控转向系统模型相结合，构建新的名义模型，为避免引入非最小相位项，采用全极点近似方法将延迟环节线性化，求解跟踪控制器和抗干扰控制器。与名义模型不含延迟环节的二自由度内模控制以及经典PID控制相比较，通过MATLAB/Simulink仿真给出了延迟量对三种方法跟踪性能的影响。比较了延迟量对采用全极点近似、Taylor近似和Padé近似的二自由度内模控制跟踪性能的影响。经线控转向台架试验验证，采用全极点近似的二自由度内模控制跟踪精度较高，对延迟的适应性较好。     

非最小相位系统自适应模型逆技术研究

针对非最小相位系统中的轨迹跟踪控制问题,基于非最小相位系统的闭环注入体系结构,对非最小相位系统的前馈控制器设计方法、模型逆技术以及自适应控制进行了研究。首先,采用非最小相位零点忽略技术、零相位误差跟踪控制技术和零幅度误差跟踪控制技术设计了系统的前馈控制器,并对3种模型逆技术进行了分析;在此基础上针对系统中非最小相位零点的偏移问题,采用遗忘因子最小二乘法实现了前馈控制器的自适应;最后进行了仿真和试验。研究结果表明:相对于使用零相位误差跟踪控制技术和非最小相位零点忽略技术,采用零幅度误差跟踪控制技术设计的前馈控制器能够更有效地提高非最小相位系统的跟踪精度,系统的轨迹跟踪误差分别减少了61.45%和56.27%;使用自适应算法能够实现对系统参数变化的自适应控制,提高了系统的抗干扰性能。     

消除参数不确定柔性系统的残留振荡鲁棒控制

基于残留振荡能量最小的优化思想，在系统参数变化范围内设计鲁棒时滞滤波器。滤波器在柔性系统极点附近配置三重零点，使其对系统参数误差不敏感，具有很强的鲁棒性。将这一控制技术用于起重机的消摆控制系统中，可以有效抑制载荷的残留摆动。     

加工过程的复合自适应模糊控制

设计了一种自适应模糊控制器,采用了模糊规则在线自调整和输出比例因子在线自适应估计相结合的策略,应用于铣削加工过程的仿真结果表明,该控制器可适用于非最小相位系统,为加强过程的约束型控制提供一条有效途径。     

基于改进遗传算法的模糊PID控制器设计

针对具有右半复平面零点的非最小相位系统,设计了模糊控制和PID控制相结合的双模控制器.应用改进的遗传算法优化其参数,并将其应用在非最小相位系统中,仿真结果表明该方法可行有效.     

电控旋翼桨距自校正控制仿真研究

针对电控旋翼的桨距控制问题,采用基于极点配置法的自校正控制方法进行仿真研究。以某电控旋翼试验系统为研究对象,采用最小二乘法对系统进行参数识别,利用极点配置法设计自适应控制器,并利用MATLAB将控制算法予以实现,对该电控旋翼系统进行充分的桨距控制仿真。仿真结果表明,基于极点配置法的自校正控制方法可对电控旋翼总距和周期变距实现有效准确的控制,并对系统模型的变化具有良好的适应性。     

基于极点配置自校正的气动位置系统控制策略研究

为了提高比例流量阀控摆动气缸旋转位置系统性能,提出了基于极点配置自校正的控制算法。建立了被控系统数学模型,该系统存在严重的非线性,系统参数随工作点的变化而变化。通过带固定遗忘因子的最小二乘辨识算法在线辨识系统参数,使用极点配置自校正的控制算法将被控系统的极点配置到希望的位置,实现实时的自适应控制。实验研究表明,基于该控制策略的气动旋转位置系统具有较好的动态性能和跟踪能力。     

电控旋翼桨距自校正控制仿真研究

针对电控旋翼的桨距控制问题,采用基于极点配置法的自校正控制方法进行仿真研究。以某电控旋翼试验系统为研究对象,采用最小二乘法对系统进行参数识别,利用极点配置法设计自适应控制器,并利用MATLAB将控制算法予以实现,对该电控旋翼系统进行充分的桨距控制仿真。仿真结果表明,基于极点配置法的自校正控制方法可对电控旋翼总距和周期变距实现有效准确的控制,并对系统模型的变化具有良好的适应性。     

一种新的PID参数自整定方法

一种新的ＰＩＤ参数自整定方法翟海斌，邹卫，王树青（浙江大学工业控制研究所）０引言ＰＩＤ参数自整定一般包括两个内容：一是过程特性的获取，二是确定相应的控制器参数。文献［１］提出了利用继电器特性的非线性环节实现自整定，但此法仅限于最小相位系统。文献［２］...     

电火花加工自适应模糊控制器的研制

针对典型的非线性参数对时变系统－电火花加工过程，提出了一种参数自适应模糊控制系统的设计方法，给出了控制器的设计步骤，以实现电火花加工过程的自适应控制，实验表明了这种自适应模糊控制系统的有效性。     

On the performance enhancement of self-tuning adaptive control for time-varying machining processes

This paper investigates efficient approaches to improve the performance of a self-tuning adaptive control system for time-varying machining processes. The milling process is a typical time-varying system because of variations of the cutting conditions, e.g., the change of cutting depths and variation of the cutting materials. On the other hand, the milling processes are considered as typically non-minimum phases since one or more zeros of discrete-time models of milling processes may be located outside the unit circle. In this paper, an efficient method, using the pole assignment method, is presented to design a self-tuning adaptive controller for time-varying and non-minimum phase milling processes, and an effective method to select appropriate design parameters in order to improve its performance has been proposed. Its effectiveness has been verified in experiments on controlling milling processes with varying cutting depth. The experimental results illustrate that improved control performances can be obtained using the appropriate design parameters selected according to the principles and criteria presented here.     

非线性液压伺服系统高性能自适应控制器的仿真研究和实时分析

本文研究了一种新的自适应控制规律,即比例加积分控制规律和继电控制规律的综合控制。还研究了一些目前国际上自适应控制领域中没有很好解决的问题：如非线性自适应控制系统;应用滤波输入和输出以避免测量对象高阶导数问题;高阶对象和低阶模型的跟踪问题;非最小相位系统的稳定控制;利用前馈传递函数以改善系统动态特性问题。本文给出了若干数字仿真和数摸混合实时分析的结果数据和图形。     

自适应加工系统的多模态并行控制

提出了一种适应复杂过程控制的多模态并行控制方案，它将传统ＰＩＤ控制与模糊控制相结合，对非最小相位、时变性的复杂加工过程控制性能进行了研究，实践结果表明，能有效提高加工系统控制的自适应性。本文受国家自然科学基金、广东省自然科学基金、华南理工大学基金资助。     

自适应制造过程的神经网络控制

分析神经网络应用于自适应控制的有效途径。提出一种基于过程增益估计的神经网络特殊学习自适应控制方案,并实现对时变加工过程的有效控制。     

RBFNDOB-based neural network inverse control for non-minimum phase MIMO system with disturbances

An adaptive control strategy combining neural network inverse controller (NNIC) with RBFN disturbance observer (RBFNDOB) is developed for a multi-input-multi-output (MIMO) system with non-minimum phase, internal and external disturbances in this paper. Since the inverse model of system is unstable due to the non-minimum phase, a pseudo-plant is constructed, then the RBFN is used to identify the inverse model of pseudo-plant, which can track the parameter variations of system. By copying the structure and parameters of the identifier, the NNIC is obtained. Cascading the NNIC with the original plant, the MIMO system can be decoupled and linearized into independent SISO systems. For the independent decoupled system, the RBFNDOB employs a RBFN to observe the external disturbances and this estimate value is used as a feed-forward compensation term in controller. The case study on ball mill grinding circuit is presented. The effectiveness of the proposed method is demonstrated by simulation results and comparisons.     

Adaptive Control of Multivariable Process Using Recurrent Neural Networks

Abstract

Industrial processes are naturally multivariable in nature, which also exhibit non-linear behavior and complex dynamic properties. The multivariable four-tank system has attracted recent attention, as it illustrates many concepts in multivariable control, particularly interaction, transmission zero, and non-minimum phase characteristics that emerge from a simple cascade of tanks. So, the multivariable laboratory process of four interconnected water tanks is considered for modeling and control. For processes which show nonlinear and multivariable characteristics, classical control strategies like PIDs have performance limitations. Hence, intelligent approaches like Neural Networks (NN) is an important term in this juncture. The use of Recurrent Neural Network (RNN) is apt for modeling and control of nonlinear dynamic processes as it contains the past information about the process. The objective of the current study is to design and implement an adaptive control system using RNN for a nonlinear multivariable process.

The proposed adaptive design comprises an estimator based on RNN, which adapts online and predicts one step ahead output. A Recursive Least Square (RLS) based back propagation algorithm is used for training the network. The controller used is also a RNN, which minimizes the difference between the predicted output and reference trajectory. The objective function is minimized using a steepest descent algorithm which gives the optimum control input. Desired performance of the system is ensured by the parallel operation of both. The proposed control strategy is implemented in a laboratory scale four tank system. The trajectory tracking and disturbance rejection response obtained are compared with the response obtained by using a well designed decoupled, decentralized IMC controller.     

Robust adaptive cruise control of high speed trains

The cruise control problem of high speed trains in the presence of unknown parameters and external disturbances is considered. In particular a Lyapunov-based robust adaptive controller is presented to achieve asymptotic tracking and disturbance rejection. The system under consideration is nonlinear, MIMO and non-minimum phase. To deal with the limitations arising from the unstable zero-dynamics we do an output redefinition such that the zero-dynamics with respect to new outputs becomes stable. Rigorous stability analyses are presented which establish the boundedness of all the internal states and simultaneously asymptotic stability of the tracking error dynamics. The results are presented for two common configurations of high speed trains, i.e. the DD and PPD designs, based on the multi-body model and are verified by several numerical simulations.     

Design of RTDA controller for industrial process using SOPDT model with minimum or non-minimum zero

This research paper focuses on the design and development of simplified RTDA control law computation formulae for SOPDT process with minimum or non-minimum zero. The design of RTDA control scheme consists of three main components namely process output prediction, model prediction update and control action computation. The systematic approach for computation of the above three components for SOPDT process with minimum or non-minimum zero is developed in this paper. The design, implementation and performance evaluation of the developed controller is demonstrated via simulation examples. The closed loop equation, block diagram representation and theoretical stability derivation for RTDA controller are developed. The performance of proposed controller is compared with IMC, SPC, MPC and PID controller and it is demonstrated on Industrial non-linear CSTR process.     

Trade-off performance analysis of LTI system with channel energy constraint

In this paper, the trade-off performance between tracking error, control input energy and channel input power is studied. By modelling the communication channel as the additive coloured Gaussian noise channel (ACGN) with limited bandwidth, a new performance index is proposed and minimized over all stabilizing two-degree-of-freedom controllers. The results show that the trade-off performance is correlated to the intrinsic characteristics of the plant, including the locations and directions of the unstable pole, non-minimum phase zero. However it is unrelated to the non-minimum phase zeros of filter because of the two-degree-of-freedom controller. We also demonstrated that ACGN may degenerate the tracking performance. Finally, a typical example is given to validate the theoretical results.     

高阶时滞系统的降阶IMC-PID控制研究

针对一类存在非最小相特性的二阶纯滞后对象,采用一阶Pade逼近,研究基于IMC的先进控制方法,得到一种新型IMC-PID参数整定方法。对于典型高阶时滞对象,先采用最小二乘法将其拟合为工业常见的二阶时滞过程模型,然后采用此IMC-PID方法进行控制器设计研究。仿真结果表明,对于此类高阶时滞对象,该控制方法较经典的PID控制方法有更好的动、静态特性。