Event-triggered model-free adaptive control for a class of surface vessels with time-delay and external disturbance via state observer

This paper provides an improved model-free adaptive control(IMFAC)strategy for solving the surface vessel trajectory tracking issue with time delay and restricted disturbance.Firstly,the original nonlinear time-delay system is transformed into a structure consisting of an unknown residual term and a parameter term with control inputs using a local compact form dynamic linearization(local-CFDL).To take advantage of the resulting structure,use a discrete-time extended state observer(DESO)to estimate the unknown residual factor.Then,according to the study,the inclusion of a time delay has no effect on the linearization structure,and an improved control approach is provided,in which DESO is used to adjust for uncertainties.Furthermore,a DESO-based event-triggered model-free adaptive control(ET-DESO-MFAC)is established by designing event-triggered conditions to assure Lyapunov stability.Only when the system’s indicator fulfills the provided event-triggered condition will the control input signal be updated;otherwise,the control input will stay the same as it is at the last trigger moment.A coordinate compensation approach is developed to reduce the steady-state inaccuracy of trajectory tracking.Finally,simulation experiments are used to assess the effectiveness of the proposed technique for trajectory tracking.

Event-Triggered Optimal Nonlinear Systems Control Based on State Observer and Neural Network

This paper develops a novel event-triggered optimal control approach based on state observer and neural network(NN)for nonlinear continuous-time systems.Firstly,the authors propose an online algorithm with critic and actor NNs to solve the optimal control problem and provide an event-triggered method to reduce communication and computation burdens.Moreover,the authors design weight estimation for critic and actor NNs based on gradient descent method and achieve uniformly ultimate boundednesss(UUB)estimation results.Furthermore,by using bounded NN weight estimation and dead-zone operator,the authors propose a triggering condition,prove the asymptotic stability of closed-loop system from Lyapunov stability perspective,and exclude the Zeno behavior.Finally,the authors provide a numerical example to illustrate the effectiveness of the proposed method.

Observer-Based Adaptive Fuzzy Tracking Control Using Integral Barrier Lyapunov Functionals for A Nonlinear System With Full State Constraints

A new fuzzy adaptive control method is proposed for a class of strict feedback nonlinear systems with immeasurable states and full constraints.The fuzzy logic system is used to design the approximator,which deals with uncertain and continuous functions in the process of backstepping design.The use of an integral barrier Lyapunov function not only ensures that all states are within the bounds of the constraint,but also mixes the states and errors to directly constrain the state,reducing the conservativeness of the constraint satisfaction condition.Considering that the states in most nonlinear systems are immeasurable,a fuzzy adaptive states observer is constructed to estimate the unknown states.Combined with adaptive backstepping technique,an adaptive fuzzy output feedback control method is proposed.The proposed control method ensures that all signals in the closed-loop system are bounded,and that the tracking error converges to a bounded tight set without violating the full state constraint.The simulation results prove the effectiveness of the proposed control scheme.

Optimal State Estimation and Fault Diagnosis for a Class of Nonlinear Systems

This study proposes a scheme for state estimation and,consequently,fault diagnosis in nonlinear systems.Initially,an optimal nonlinear observer is designed for nonlinear systems subject to an actuator or plant fault.By utilizing Lyapunov's direct method,the observer is proved to be optimal with respect to a performance function,including the magnitude of the observer gain and the convergence time.The observer gain is obtained by using approximation of Hamilton-Jacobi-Bellman(HJB)equation.The approximation is determined via an online trained neural network(NN).Next a class of affine nonlinear systems is considered which is subject to unknown disturbances in addition to fault signals.In this case,for each fault the original system is transformed to a new form in which the proposed optimal observer can be applied for state estimation and fault detection and isolation(FDI).Simulation results of a singlelink flexible joint robot(SLFJR)electric drive system show the effectiveness of the proposed methodology.

Output-Feedback Based Simplified Optimized Backstepping Control for Strict-Feedback Systems with Input and State Constraints

In this paper,an adaptive neural-network(NN)output feedback optimal control problem is studied for a class of strict-feedback nonlinear systems with unknown internal dynamics,input saturation and state constraints.Neural networks are used to approximate unknown internal dynamics and an adaptive NN state observer is developed to estimate immeasurable states.Under the framework of the backstepping design,by employing the actor-critic architecture and constructing the tan-type Barrier Lyapunov function(BLF),the virtual and actual optimal controllers are developed.In order to accomplish optimal control effectively,a simplified reinforcement learning(RL)algorithm is designed by deriving the updating laws from the negative gradient of a simple positive function,instead of employing existing optimal control methods.In addition,to ensure that all the signals in the closed-loop system are bounded and the output can follow the reference signal within a bounded error,all state variables are confined within their compact sets all times.Finally,a simulation example is given to illustrate the effectiveness of the proposed control strategy.

基于扩张状态观测器的永磁直线同步电机改进模型预测电流控制

针对永磁直线同步电机(PMLSM)模型预测电流控制(MPCC)中存在的电流脉动过大、在线计算复杂的问题,提出一种基于扩张状态观测器(ESO)的改进MPCC策略。利用最优电压矢量相角和重新划分后的电压矢量扇区,改进第一电压矢量的选取方式,减小第二电压矢量选择范围,以实现电压矢量的快速选取,进而降低电流脉动。同时,为了提高PMLSM抗负载扰动能力,通过ESO对扰动进行观测,将观测到的扰动转换为电流进行补偿,提高系统的鲁棒性,且补偿电流可起到进一步减小电流脉动的作用。仿真结果表明,所提出的控制方法切实可行,与MPCC相比,基于ESO的改进MPCC系统具有更好的控制性能、更小的电流脉动和较强的鲁棒性能。

基于延迟校正扩张状态观测器的内置式永磁同步电机电流控制策略

高速电驱动场合会受到开关频率的限制,低载波比特性凸显,由此引起的控制延迟问题影响了内置式永磁同步电机(IPMSM)矢量控制系统性能。基于扩展电动势的建模方案能够获得IPMSM的对称化模型,有利于离散化分析和设计,以提升低载波比运行性能。扩张状态观测器(ESO)能够实现扩展电动势和扰动的集总观测,然而低载波比延迟问题影响了估算性能。该文在离散域中分析延迟影响,设计和对比研究了几种延迟影响抑制方案,即延迟校正扩张状态观测器,提升了估计精度。基于此,设计了IPMSM离散域电流控制方案,显著提升了电流响应特性。通过电动汽车驱动实验平台验证了该文的分析和设计。

燃料电池-锂电池混合供电系统的无源控制策略及参数设计方法

多源混合供电技术可有效地利用不同供电单元性能,在电气化交通系统中得到广泛应用。然而,随着越来越多的恒功率特性及强脉动特性负载接入供电系统,系统的稳定性难以得到保证。针对传统线性控制方法难以保证系统的大信号稳定性,而现有非线性控制的研究仍缺乏兼顾稳定性和动态性能的控制参数设计方法的问题,该文对燃料电池-锂电池混合供电系统设计基于无源控制和扩张高增益状态观测器的复合控制方法,并对控制器和观测器参数进行了设计,有效地保证了系统的动静态性能。在实验室搭建一套额定功率为3 kW的燃料电池-锂电池混合供电系统实验平台进行测试,结果验证了所提控制策略和参数设计方法的有效性和正确性。

永磁同步电机驱动的机械臂能量优化与位置控制

为了降低永磁同步电机(permanent magnet synchronous motor, PMSM)驱动的机械臂能量损耗和提高位置控制精度,设计了基于反步非奇异快速终端滑模的能量优化控制。将机械臂及其驱动电机被视为一个整体系统来进行控制器设计和能量优化。设计高增益扩张状态观测器(extended state observer, ESO)估计并补偿集总扰动和建模误差。考虑到高增益ESO对测量噪声敏感,设计扩展卡尔曼滤波器(extended Kalman filter, EKF)与ESO结合来降低测量噪声的影响。研究结果表明,能量优化控制可以降低系统能量损耗和稳态误差,具有良好的控制性能。

泵控液压垫预加速阶段最优控制策略

针对国产液压垫位置控制精度差,生产效率低的问题,提出了一种基于S型曲线规划方法的最优控制策略。首先,分析了泵控液压垫预加速阶段高精度的控制要求,提出了S型曲线规划方法来减小系统运行过程中的冲击振动;其次,构建了最优控制二次型函数,并设计了基于速度位置复合控制要求的最优控制策略;最后,搭建了泵控液压垫模拟实验台,对提出的控制策略进行试验研究。结果表明,基于S型曲线规划的最优控制策略相比于传统的PID控制,速度和位置控制都可以获得较好的控制效果,将为电液伺服泵控液压拉伸垫的工程推广与应用奠定良好的基础。

卷到卷制程中基于降维观测器的优化状态反馈控制方法

卷到卷(roll-to-roll,R2R)制程普遍应用于柔性材料的量化生产中,其套色精度是产品质量的关键因素。R2R制程是一个多输入多输出、强耦合的复杂生产过程,实现高速高精度套色控制是其基本要求。首先,根据R2R印刷系统的状态空间模型,设计了基于降维观测器的状态反馈控制方法,同时通过粒子群优化算法对状态反馈的极点进行优化,实现了套色误差的高精度控制。将提出的套色控制方法与最新发表的全解耦比例微分(fully decoupled proportional-derivative,FDPD)套色控制方法进行对比,所提方法在套色精度上获得了50%~80%的提升,调节时间缩短了50%~65%,控制量绝对值之和仅为FDPD控制方法的46%~57%,这不仅减少了对执行机构的冲击,也节约了能量。对比结果证实了降维观测器的估值准确性,也表明所提方法能够明显提高控制性能。

Boiler Modelling and Optimal Control of Steam Temperature in Thermal Power Plants

Achieving accurate control of main steam temperature is a very difficult task in thermal power plants due to the large process lag （8 to 10 minutes） associated with the superheater system and there exists a deviation of ±10 ℃ in closed loop control. A control oriented boiler model and an appropriate optimal control strategy are the essential tools for improving the accuracy of this control system. This paper offers a comprehensive integrated 8th order mathematical model for the boiler and a Kalman Filter based state predictive controller for effectively controlling the main steam temperature within ± 2 ℃ and to enhance the efficiency of the boiler. It is proved through simulation that the predictive controller method with Kalman filter state estimator and predictor is the most appropriate one for the optimization of main steam temperature control as compared to other methods. This control system is under field implementation in a 210 MW boiler of a thermal power plant.

基于改进果蝇优化算法的塔机自适应滑模控制研究

针对部分工况下塔机负载摆角直接测量困难、系统滑模控制器抖振明显以及控制器参数调节复杂等问题,提出了一种基于改进果蝇优化算法的塔机自适应滑模控制方法。首先,根据拉格朗日方程,得到了塔机单摆系统的动力学模型。然后,设计了线性扩张状态观测器,用于观测塔机负载摆动状态,并将观测结果反馈到自适应滑模控制器中;在构造滑模面时,采用双曲正切函数代替常用的符号函数以增加其连续性,从而减小抖振。最后,改进了果蝇优化算法的寻优策略及搜索半径,并对自适应滑模控制器的参数进行了优化。结果表明,所设计的线性扩张状态观测器跟踪观测塔机负载摆角的收敛速度较快且跟踪误差小于1.3%;经改进果蝇优化算法优化后的自适应滑模控制器不仅对负载摆动有较好的抑制作用,而且具有较强的抗干扰性和鲁棒性。所提出的控制方法可在实现精确定位的同时有效避免塔机负载摆动带来的安全隐患,保障工人的安全和工程的顺利开展。

基于自适应时间尺度小波包和模糊控制的复合储能控制策略

对复合储能系统参与风电波动平抑进行了探讨和分析。首先,传统滤波方法在提取并网功率时以风电波动大的时段确定平滑程度,在不同风电波动场景整体平滑效果差异较大,提出自适应时间尺度小波包方法,能够根据风电波动情况自适应规划滤波时间尺度,同时在时间尺度内自适应确定分解层数,兼顾不同风电波动时段,提高了场景适应能力。然后,基于功率型、能量型储能出力充放电响应分界时间,用所提方法进行内部功率分配,并设计了计及复合储能荷电状态(SOC)优化和能量型、功率型储能协调控制的双目标模糊控制策略,在优化SOC的同时减小对并网功率的影响。最后,依托某风电场典型日数据在MATLAB中验证了所提方法的有效性。

线性自抗扰控制器的改进设计及应用

针对Ⅰ阶线性自抗扰控制器应用于火电机组大惯性过程控制时响应较慢的问题,提出了一种改进设计方法。基于预估模型信息设计补偿环节,将扩张状态观测器的等效观测对象近似为比例环节,并通过对总和扰动进行补偿,使得闭环系统的动态近似于预期响应,且兼具良好的抗扰能力和鲁棒性。通过分析,明确了该控制器各参数的物理意义及其整定策略,通过仿真验证了控制效果和鲁棒性,并在某二次再热机组高压加热器水位和过热汽温Ⅰ级喷水减温控制回路上对该设计方法进行了组态和测试。结果表明:改进的自抗扰控制器提高了系统的控制性能;该方法的实现和整定较简便,具有良好的工程应用潜力。

基于生物力学分析的人体意图预测实验设计

为实现人体运动意图预测,该文提出一种基于粒子群优化(particle swarm optimization,PSO)算法优化回声状态网络(echo state network,ESN)的模型预测控制(model predictive control,MPC)方法。首先,利用运动捕捉系统获得人体动作运动学信息,通过OpenSim软件反解算获取动力学信息;其次,以动力学信息为输入,运动学信息为输出,构建人体骨骼肌肉系统的ESN模型,并利用PSO算法优化ESN模型的关键参数;同时,将线性化后的ESN模型作为MPC控制对象,通过运动学信息,反优化MPC目标函数,求解目标结果,完成对人体运动意图的预测;最后,通过比较实验验证了所提方法的有效性。该方法对人体运动意图预测及穿戴式机器人控制算法设计等相关应用研究与教学实践具有实际意义。

基于改进ESO的两相混合式步进电机反步控制

两相混合式步进电机是一个由脉冲驱动的多变量、强耦合的非线性系统,针对步进电机运行易受内外界扰动的影响从而产生失步、抖振等问题,提出了一种基于扩张状态观测器(ESO)的反步控制系统,在传统ESO的基础上,引入了径向基函数神经网络(RBF),对ESO的参数进行实时调整,同时引入粒子群算法(PSO),对反步控制器参数进行优化。研究结果表明,与传统反步控制相比,采用改进ESO的反步控制系统能在空载及负载状态下有效的提高系统的跟踪精度,并提高系统的响应速度。

永磁同步电机的改进粒子群优化自抗扰控制

为解决永磁同步电机的高精度控制问题,提出了一种基于改进粒子群优化算法的离散型自抗扰控制方法。在建立永磁同步电机数学模型基础上,对粒子群优化算法进行改进,分别设计改进的二阶跟踪微分器、三阶扩展状态观测器和非线性状态误差反馈环节,构造新的非线性函数提高扩展状态观测器和非线性状态误差补偿的控制率。改进粒子群算法对状态观测器和非线性状态误差补偿进行参数整定。结果显示,与PI和传统自抗扰控制器相比较,改进粒子群优化自抗扰控制能够使永磁同步电机具有更快的响应速度、更好的跟踪精度和更强的抗干扰能力,显著提高了控制效果。

输配电线路跳闸保护装置的固态开关设计与优化

随着电力系统的快速发展和电力需求的增长,对输配电线路的可靠性和安全性要求越来越高。传统的跳闸保护装置存在响应时间长、可靠性低等问题,而固态开关技术作为一种新兴的技术手段,具有快速响应、可靠性高等优点,因此在跳闸保护装置中具有广泛的应用前景。研究输配电线路跳闸保护装置的固态开关设计与优化,通过对固态开关、跳闸保护以及相关控制策略和热管理技术的研究,提出一种优化方案。实验结果表明,该方案在提高装置性能的同时,具有较好的经济性和可行性。

北仑1和2号600MW单元机组协调控制系统的设计与应用

大容量单元机组机炉协调控制系统的设计与调试是一项综合性很强的技术工作,基于IFO-KΔx的状态反馈可以预测汽压的变化趋势,有效克服汽压过调或振荡现象,对锅炉汽压的稳定起到关键性作用。该文提出了“正踢”和“反踢”的设计概念,用以克服锅炉的大迟滞和大惯性,由基于IFO-KΔx的状态反馈、“正踢”和“反踢”以及PID调节器共同组成一个综合型协调控制系统,充分发挥了各自的特长。将上述方案实际推广应用于北仑1和2号600MW单元机组的机炉协调(CCS)和自动发电控制(AGC)系统中,取得了:①稳定工况下负荷控制偏差在±3MW之内,主汽压静态偏差在±0.2MPa;②定压段变负荷时,当负荷变化100MW时,实际负荷变化速率>12MW/min;③滑压段变负荷时,主汽压偏差在±0.5MPa,实际负荷变化速率>9MW/min的优良控制效果,为单元机组协调控制系统的成功应用提供了一个范例。此外,通过对北仑600MW单元机组仿真试验曲线与现场实时趋势曲线的比较,表明文中所选用的数学模型是正确和有效的。