Realization of simple adaptive control by using parallel feedforward compensator

A problem is dealt with concerning a practical design scheme for a multi-input/multi-output simple adaptive control (SAC) system. The SAC method which was first derived as a sort of the direct model following adaptive control based on the command generator tracker (CGT) technique was fundamentally applicable to plants satisfying the so-called almost strictly positive real (ASPR) condition. However, the method can be applied to non-ASPR plants by using a parallel feedforward compensator (PFC), which makes the augmented plant including PFC be ASPR. Such a technique was proposed by Bar-Kana for plants satisfying the output feedback stabilizable condition. As an extension, the authors have proposed a simple and concrete PFC design procedure for SISO non-ASPR plants. The special feature of this proposal is that the method can be applicable to non-ASPR plants as the minimum phase system. The main aim of the paper is to give a practical design procedure of PFC for MIMO plants as a natural extension of the SISO case, so that an actual realization of MIMO SAC can be attained for a broad class of MIMO plants. The effectiveness of the proposed method is also confirmed through numerical simulations.     

Adaptive Output Feedback based Output Tracking Control for a Time‐Delay System with a PFC

This output tracking control system for time‐delay systems with a parallel feedforward compensator (PFC). It has been clarified that one can design an adaptive output tracking control system for systems with almost strictly positive real (ASPR) properties. However, the robust stability for non‐ASPR time‐delay systems with a PFC introduced for making the resulting augmented system ASPR has not been analyzed. In this paper, it will be clarified that one can design a stable adaptive output tracking control system for time‐delay systems with a PFC, even if the resulting augmented system is not exactly ASPR.     

Design and Experimental Evaluation of a Discrete‐time ASPR‐based Adaptive Output Feedback Control System Using FRIT

This paper describes the design of an adaptive output feedback control system in discrete‐time, based on almost strictly positive real (ASPR)‐ness with a feedforward input. It is well‐known that an adaptive output feedback control system based on ASPR conditions can achieve asymptotic stability via a constant feedback gain. Unfortunately, most realistic systems are not ASPR because of the severe conditions. The introduction of a parallel feedforward compensator (PFC) is an efficient way to alleviate such restrictions. However, the problem remains that there exists a steady state error between the output of the augmented system and the output of the original system. The proposed scheme provides a strategy wherein the feedforward input is utilized such that the steady state error is removed. Furthermore, the fictitious reference iterative tuning (FRIT) approach is employed to determine the control parameters using one‐shot input/output experimental data directly, without prior information about the control system. This paper explains how the FRIT approach is applied in designing an adaptive output feedback control system. The effectiveness of the proposed scheme is confirmed experimentally, by using a motor application.     

Output feedback strict passivity of discrete-time nonlinear systems and adaptive control system design with a PFC

In this paper, a passivity-based adaptive output feedback control for discrete-time nonlinear systems is considered. Output Feedback Strictly Passive (OFSP) conditions in order to design a stable adaptive output control system will be established. Further, a design scheme of a parallel feedforward compensator (PFC), which is introduced in order to realize an OFSP controlled system, will be provided and an adaptive output feedback control system design scheme with a PFC will be proposed.     

Fractional Order Modeling of Human Operator Behavior with Second Order Controlled Plant and Experiment Research

Modeling human operator's dynamics plays a very important role in the manual closed-loop control system, and it is an active research area for several decades. Based on the characteristics of human brain and behavior, a new kind of fractional order mathematical model for human operator in single-input single-output (SISO) systems is proposed. Compared with the traditional models based on the commonly used quasilinear transfer function method or the optimal control theory method, the proposed fractional order model has simpler structure with only few parameters, and each parameter has explicit physical meanings. The actual data and experiment results with the second-order controlled plant illustrate the effectiveness of the proposed method.      

Fuzzy‐Neuron Intelligent Coordination Control Fora Unit Power Plant

A novel fuzzy‐neuron intelligent coordination control method for a unit power plant is proposed in this paper. Based on the complementarity between a fuzzy controller and a neuron model‐free controller, a fuzzy‐neuron compound control method for Single‐In‐Single‐Out (SISO) systems is presented to enhance the robustness and precision of the control system. In this new intelligent control system, the fuzzy logic controller is used to speed up the transient response, and the adaptive neuron controller is used to eliminate the steady state error of the system. For the multivariable control system, the multivariable controlled plant is decoupled statically, and then the fuzzy‐neuron intelligent controller is used in each input‐output path of the decoupled plant. To the complex unit power plant, the structure of this new intelligent coordination controller is very simple and the simulation test results show that good performances such as strong robustness and adaptability, etc. are obtained. One of the outstanding advantages is that the proposed method can separate the controller design procedure and control signals from the plant model. It can be used in practice very conveniently.     

一类非线性大滞后系统自适应积分滑模控制

针对一类非线性大滞后系统, 基于伪偏导数概念的动态线性化非线性系统模型, 利用离散时间预测器技术, 实现了系统原始表达式中滞后环节的隐性表达, 并结合离散积分滑模控制(discrete integral sliding mode control, DISMC)方法, 提出了一种新的无模型自适应离散积分滑模控制(model-free adaptive discrete integral sliding mode control, MFA-DISMC)方案. 该方法的主要特点是控制器设计仅取决于被控对象的输入和输出测量数据. 通过理论分析证明了算法的稳定性, 仿真研究表明, 相比于无模型自适应控制(model-free adaptive control, MFAC)、Smith预估控制、改进的MFAC控制以及比例–积分–微分(proportional-integral-derivative, PID) 控制方法, 本文方法具有更快的响应速度和更强的鲁棒性. 最后, 通过双容水箱液位控制系统的实验研究, 验证了所提出方法的有效性.     

非仿射系统的自学习滑模抗扰控制

针对一类单输入单输出(single-input single-output,SISO)非仿射非线性系统的控制问题,提出了一种自学习滑模抗扰控制方法.该方法用非线性光滑函数设计扩张状态观测器,实现SISO非仿射非线性系统内部不确定性和外部扰动的扩张状态估计,并将扩张状态观测器(extended state observer,ESO)与自学习滑模控制技术融为一体,实现SISO非仿射非线性系统的自学习滑模抗扰控制.该方法不依赖受控对象的数学模型,可以快速跟踪任意给定的参考信号.数值仿真试验表明了该方法响应速度快、控制精度高,具有很强的抗扰动能力,因而是一种鲁棒稳定性很强的控制方法,在SISO非仿射非线性系统控制领域具有重要作用.     

A STABLE ON‐LINE SELF‐TUNING OPTIMAL PID CONTROLLER FOR A CLASS OF UNKNOWN SYSTEMS

In this paper, a novel self‐tuning method of optimal PID control laws is proposed for both continuous‐time systems and discrete‐time systems. The controlled plant is assumed to be unknown except the system order (or system delay) and the direction of transmitting control input. Through the minimization of PID gains subject to the Lyapunov stability based reaching condition, the tuning of the three PID control gains is transformed to solve the inequality constraint optimization problem. An unknown SISO nonlinear system subject to a unit step input, and the tracking control problem of the piezoelectric actuator (PZA) with unknown dynamics are simulated. The simulation results show that the excellent tracking performance can be achieved.     

Stability and analysis of iterative learning system using frequency domain method

This paper adopts a reinforcement learning control using frequency domain method for a SISO system with friction and input-saturation process. Based on the two-dimensional system theory, the proposed method investigates the robust stability criteria of the SISO iterative learning control system (ILCS) using frequency domain methods. The restrictive conditions for the stability of the ILCS are also derived. A design procedure for the ILCS is outlined. A numerical simulation example is given to demonstrate the utilization of obtained results.     

使用低阶参考模型的离散多变量MRACS

本文提出了一种高阶被控对象跟随低阶参考模型的离散多变量模型参考自适应控制系统(MRACS)的设计方法,它利用数字滤波器将系统降阶,通过求解p个一次代数方程而确定的自适应控制输入信号,能够使多变量被控对象的每个多输入单输出(MISO)子系统的输出渐近收敛到对应的单输入单输出(SISO)低阶参考模型的输出,提出的自适应控制器不仅避免了引进正反馈和辅助信号,而且实现了自适应解耦控制,仿真结果表明该控制器具有较好的控制性能。     

二维直线电机的多入多出无模型自适应轮廓控制

针对传统的单入单出控制器无法解决二维直线电机存在的非线性,不确定性以及强耦合作用等问题,依据无模型自适应控制不依赖于被控系统精确数学模型,仅需受控系统输入输出数据便能实现自适应控制这一特点,采用多入多出的紧格式动态线性化无模型自适应控制算法对二维直线电机XY轴进行整体控制器设计.同时,针对二维直线电机这种含有纯二阶积分环节的非自平衡系统,提出了多入多出无模型自适应控制改良方法,并进行严格的稳定性和收敛性证明.为了提高二维直线电机的轮廓精度,在多入多出无模型自适应控制改良方法的基础上,加入交叉耦合控制器,与传统的交叉耦合控制方法相比较,提高了跟踪精度和轮廓精度.最后通过仿真和实物实验证明了所提方案的有效性.     

一种H∞混合灵敏度控制器设计方法

传统的输出反馈H∞优化控制设计比较复杂,由于控制器的阶数要远远高于实际系统的阶数,在实际工业控制过程中不容易实现。从次优控制的角度出发,利用量子粒子群算法解决特定结构的H∞混合灵敏度优化控制设计。对带有超前/滞后环节控制器的SISO锁相闭环摩托速度系统和PI 型控制器的MIMO超级机动F18/harv 战斗机系统使用量子粒子群算法进行了分析,并和GA算法进行了比较。实验仿真显示了量子粒子群算法的有效性。     

The Design of a Coprime‐Factorized Predictive Functional Controller for Unstable Fractional Order Systems

In this paper, a new approach, called coprime‐factorized predictive functional control method (CFPFC‐F) is proposed to control unstable fractional order linear time invariant systems. To design the controller, first, a prediction model should be synthesized. For this purpose, coprime‐factorized representation is extended for unstable fractional order systems via a reduced approximated model of unstable fractional order (FO) system. That is, an approximated integer model of fractional order system is derived via the well‐known Oustaloup method. Then, the high order approximated model is reduced to a lower one via a balanced truncation model order reduction method. Next, the equivalent coprime‐factorized model of the unstable fractional‐order plant is employed to predict the output of the system. Then, a predictive functional controller (PFC) is designed to control the unstable plant. Finally, the robust stability of the closed‐loop system is analyzed via small gain theorem. The performance of the proposed control is investigated via simulations for the control of an unstable non‐laminated electromagnetic suspension system as our simulation test system.     

离散时间非线性系统的数据驱动无模型自适应迭代学习控制

本文基于迭代域的动态线性化方法,提出了一类单入单出离散时间非线性系统的数据驱动无模型自适应迭代学习控制方案.无模型自适应迭代学习控制本质上属于一种数据驱动控制方法,仅利用被控对象的输入输出数据即可实现控制方案的设计.理论分析表明无模型自适应迭代学习控制方案可以保证最大学习误差的单调收敛性.数值仿真和快速路交通控制应用验证了无模型自适应迭代学习控制方案的有效性.     

Quantitative design for SISO non-minimum-phase unstable plants by the singular-G method

It has been shown how a single-input-single-output (SISO) plant can be converted by several independent samplings of the output, into an equivalent multiple-input-multiple-output one, with respect to feedback system stability. The singular-G method may then be used to stabilize the system despite plant uncertainty. Only qualitative properties were previously presented. This paper presents a quantitative design procedure for such uncertain SISO plants with right half-plane poles and zeros, and some of the costs thereby incurred. The inherently large loop sensitivity is transferred from the plant to the compensators. Plant disturbances are greatly amplified rather than attenuated. But for a large class of such uncertain plants, stabilization and reasonable command response are possible.     

基于神经网络的聚氯乙烯汽提过程自适应解耦控制

聚氯乙烯汽提过程具有高度非线性和时变性等特点,是一类复杂的非线性工业过程.首先基于动态模糊神经网络建立了数据驱动的聚氯乙烯树脂（PVC）汽提过程的被控对象模型;然后采用一种神经网络分散式解耦控制器对汽提过程进行解耦,得到浆料流量-塔顶温度和蒸汽流量-塔底温度两个单变量系统;最后采用BP神经网络PID控制器对系统进行控制.仿真实验结果验证了所提出集成控制策略的有效性.     

Stability and analysis of iterative learning system using frequency domain method

This paper adopts a reinforcement learning control using the frequency domain method for a SISO system with friction and input saturation process. The proposed method, which is based on two-dimensional system theory, investigates the robust stability criteria of the SISO iterative learning control system ({ILCS}) using frequency domain methods. The restrictive conditions for the stability of the {ILCS} are also derived. A design procedure for the {ILCS} is outlined. A numerical simulation example is given to demonstrate the utilization of obtained results.     

A robust output-feedback adaptive dynamic surface control for linear systems with input disturbance

In this paper, a robust output-feedback adaptive control is proposed for linear time-invariant (LTI) singleinput single-output (SISO) plants with unmeasurable input disturbance. Using dynamic surface control (DSC) technique, it is shown that the explosion of complexity problem in backstepping control can be eliminated. Furthermore, the proposed adaptive DSC scheme has the following merits: 1) by introducing an initialization technique, the L∞ performance of system tracking error can be guaranteed even if the plant high-frequency gain is unknown and the input disturbance exists, and 2) the adaptive law is necessary only at the first design step, which significantly reduces the design procedure. It is proved that with the proposed scheme, all the closed-loop signals are semiglobally uniformly ultimately bounded. Simulation results are presented to demonstrate the effectiveness of the proposed scheme.     

一种基于滑模的自适应预测函数控制新算法

提出了一种应用带时变遗忘因子的基于滑模的自适应预测函数控制新算法。该算法采用带时变遗忘因子的递推最小二乘算法在线辨识模型参数,将滑模控制与预测函数控制(PFC)相结合对系统进行控制。与其他模型预测控制不同,预测函数控制可以克服其他模型预测控制可能出现规律不明的控制输入问题,具有良好的跟踪能力和较强的鲁棒性,离散滑模控制中的滑动模态对干扰具有不变性;最后分析了控制系统的闭环渐近稳定性。仿真结果验证了该方法的有效性。