交流伺服系统控制器在线参数自整定方法

伺服系统转动惯量的变化会影响伺服控制性能。为提高伺服性能需要对转动惯量进行在线辨识,实现伺服控制器参数的在线自整定。详细介绍了基于模型参考自适应的转动惯量在线辨识方法,提出了动态调整自适应增益和滤波器时间的方法,有效解决了自适应算法辨识速度和辨识精度的矛盾。根据转动惯量的辨识结果,利用对称优化法则,实时调整伺服控制器参数,以保证控制器动态性能的一致性和鲁棒性。仿真和试验验证了方法的有效性。     

主动磁轴承系统的自适应滑模控制

本文针对磁轴承系统的结构参数具有时变性,为典型的复杂非线性系统的特点,将自适应控制和滑模控制相结合,设计了一种能实时跟踪系统结构参数变化的自适应滑模控制器,并应用到单自由度磁悬浮轴承控制系统,使得系统的性能达到最优或近似最优。以单自由度磁轴承控制系统为研究对象,建立了基于自适应滑模控制的仿真模型,构建了以PC为上位机,TMS320F2812型DSP为下位机的实验平台。仿真与实验结果表明,自适应滑模控制器不但能削弱常规滑模控制中的固有抖振,而且具有更好的鲁棒性和快速性,满足磁轴承系统实时控制的要求。     

永磁交流伺服系统的抗扰动自适应控制

在某些高性能的驱动场合,惯量扰动的存在是造成伺服系统性能变差的重要原因。现提出一种具有抗扰动作用的自适应控制方法,采用梯度算法对系统的惯量进行在线辨识,并依据极点配置原则对速度控制器参数(速度环的比例系数Kpω和积分系数Kiω)进行实时调整,文中给出了理论分析。以一台面贴式永磁同步电机为对象进行了惯量辨识和自适应性能的仿真分析和实验验证,结果表明本文提出的基于梯度算法进行在线惯量辨识及调整的自适应算法是有效的。     

基于模型参考神经网络的异步电机鲁棒速度控制方法

提出一种新型的基于模型参考神经网络的异步电机驱动系统鲁棒速度控制方法。由带负载转矩观测器的两层神经网络对象辨识器(NNPI)对未知的电机动态参数进行实时的自适应辨识与估计。由双层神经网络PI控制器(NNC)对异步电机转子速度进行鲁棒控制。神经网络使用学习算法以自动调节NNPIC的参数并有效地降低系统对参数变化以及负载扰动的敏感度。仿真结果表明该方法对于参数变化和负载转矩扰动具有很强的自适应能力,能够提高异步电机的性能,并减小其对参数变化、非线性影响以及负载扰动的敏感度。     

机械弹性储能系统中永磁同步电机反推SVM-DTC控制

机械弹性储能系统在储能过程中驱动电机负载的转矩和转动惯量连续变化,情况复杂,需要一种能够快速跟踪其变化且抗干扰能力较强的控制系统。直接转矩控制响应快,能快速跟踪储能箱转矩,结合反推自适应控制算法,可以使其有较好的稳态和暂态性能。首先采用遗忘因子递推最小二乘算法辨识储能箱转矩和转动惯量,实时更新控制对象参数,结合辨识结果设计转角、转速、转矩和磁链反推控制器,并最终得到定子电压在两相静止坐标系下的分量,同时设计转矩和转动惯量自适应控制器消除辨识误差对控制性能的影响,进一步应用电压空间矢量调制方法产生频率恒定的开关信号,控制逆变器运行。实验结果表明永磁同步电机输出转矩能够快速跟踪负载转矩,且转矩转速脉动较小,储能过程平稳。     

基于自适应SFLA算法的LQR附加阻尼控制器设计

针对含有电压源型换流器高压直流(VSC-HVDC)的交直流系统愈发凸显的低频振荡问题,提出了一种基于改进自适应混合蛙跳(SFLA)算法优化的线性二次型最优方法来设计附加阻尼控制器,以此来抑制振荡。该方法采用最小二乘-旋转不变技术(TLS-ESPRIT),辨识出系统的对应振荡模态和降阶模型。针对该模型运用线性二次最优控制法并引入状态观测器,求出带观测器的控制器表达式。采用改进自适应SFLA算法对权矩阵进行优化,设计线性二次型最优控制器使其控制效果最优。在PSCAD验证。仿真结果显示,这种控制器相比于传统线性二次最优控制器具有更好的鲁棒性和控制效果,控制器也较易设计。     

UUV推进电机在线参数辨识自适应控制方法研究

针对水下无人航行器(UUV)的推进电机矢量控制系统中电流控制器性能因参数变化而下降的问题,提出一种基于智能在线参数辨识的电流环自适应控制方法。以离散型永磁同步推进电机动态模型作为被控对象,采用动态惯性权重粒子群算法对永磁同步推进电机的定子电阻和dq轴电感进行在线辨识,根据电流控制器工程设计方法,将辨识所得的电机参数实时用于计算电流控制器的PI值,实现电流环的自适应控制。最后,通过仿真实验验证所提方法的有效性,结果表明该方法可以有效地克服水下快速洋流对推进电机的负载扰动,进而实现永磁同步推进电机的快速、高精确度电流控制性能。     

PEMFC发电系统FFRLS在线辨识和实时最优温度广义预测控制方法

质子交换膜燃料电池(proton exchange membrane fuel cell,PEMFC)发电系统是21世纪最有前景的发电技术之一。针对空冷型PEMFC发电系统工作温度对输出性能影响问题,该文通过理论分析和实验手段研究发电系统输出性能的最优特性。根据空冷型PEMFC发电系统温度控制对象所具有的非线性、迟滞、时变等特点,提出基于FFRLS在线辨识的PEMFC发电系统实时最优温度广义预测控制。其中,FFRLS在线辨识算法用于对非线性控制对象进行建模和在线校正。在搭建的空冷型PEMFC测控实验平台上,通过实验研究控制器参数对于系统性能的影响。阶跃响应实验结果表明:提出的控制方法能够在不同负载条件下实现对电堆最优温度进行实时跟踪。与常规PID控制比较,系统的超调量减少了约32.3%,发电系统输出功率更平稳,有利于发电系统的长期稳定运行,延长电堆的使用寿命。     

基于在线辨识的实时最优励磁控制器

设计了一种在线实时最优励磁控制器（RTOEC）,其辨识器能够由发电机运行状态的变化采用非线性最小二乘法辨识法准确辨识出单机无穷大模型中的系统参数,从而可根据辨识结果及其系统所遭受的扰动,通过线性最优控制理论实时计算出线性最优励磁控制的反馈增益矩阵,以适应系统当前的运行点和所遭受的干扰。仿真结果表明,所设计的RTOEC能够适应系统运行状态的大范围变化,在大小扰动下均表现出良好的控制性能,有效提高了电力系统的静态和暂态稳定性。     

基于Prony和改进PSO算法的多机PSS参数优化

针对多机电力系统稳定器(PSS)的参数优化问题,提出了采用Prony算法辨识互联电力系统低频振荡的机电模式,利用基于T-S模型模糊自适应的改进微粒群优化(T-SPSO)算法协调PSS参数的控制策略.先采用基于Prony分析的留数法确定PSS的最优安装位置,然后通过对采样数据的Prony分析辨识系统振荡模式的特征值,最后利用所提T-SPSO算法协调优化多机PSS参数.T-SPSO算法根据当前种群最优适应值和惯性权重,自适应更新惯性权重取值,解决了PSO算法的早熟问题.针对IEEE 4机系统的仿真分析表明,基于T-SPSO算法优化后的多机PSS控制器,在2种典型运行方式下都具有更好的控制性能.     

Output‐feedback H∞ quadratic tracking control of linear systems using reinforcement learning

This paper presents an online learning algorithm based on integral reinforcement learning (IRL) to design an output‐feedback (OPFB) H_∞ tracking controller for partially unknown linear continuous‐time systems. Although reinforcement learning techniques have been successfully applied to find optimal state‐feedback controllers, in most control applications, it is not practical to measure the full system states. Therefore, it is desired to design OPFB controllers. To this end, a general bounded L₂ ‐gain tracking problem with a discounted performance function is used for the OPFB H_∞ tracking. A tracking game algebraic Riccati equation is then developed that gives a Nash equilibrium solution to the associated min‐max optimization problem. An IRL algorithm is then developed to solve the game algebraic Riccati equation online without requiring complete knowledge of the system dynamics. The proposed IRL‐based algorithm solves an IRL Bellman equation in each iteration online in real time to evaluate an OPFB policy and updates the OPFB gain using the information given by the evaluated policy. An adaptive observer is used to provide the knowledge of the full states for the IRL Bellman equation during learning. However, the observer is not needed after the learning process is finished. A simulation example is provided to verify the convergence of the proposed algorithm to a suboptimal OPFB solution and the performance of the proposed method.     

Convergence analysis of self-tuning Riccati equation for systems with correlation noises

For linear discrete time-invariant stochastic system with correlated noises, and with unknown state transition matrix and unknown noise statistics, substituting the online consistent estimators of the state transition matrix and noise statistics into steady-state optimal Riccati equation, a new self-tuning Riccati equation is presented. A dynamic variance error system analysis (DVESA) method is presented, which transforms the convergence problem of self-tuning Riccati equation into the stability problem of a time-varying Lyapunov equation. Two decision criterions of the stability for the Lyapunov equation are presented. Using the DVESA method and Kalman filtering stability theory, it proves that with probability 1, the solution of self-tuning Riccati equation converges to the solution of the steady-state optimal Riccati equation or time-varying optimal Riccati equation. The proposed method can be applied to design a new selftuning information fusion Kalman filter and will provide the theoretical basis for solving the convergence problem of self-tuning filters. A numerical simulation example shows the effectiveness of the proposed method.     

直流无刷电机系统的最佳控制器设计

将适应性基因演算法搜寻最佳化的模糊滑动控制模式参数,应用于直流无刷电机系统,以期达到位置控制的目的.由于传统计算机仿真的最佳控制器通常经手调才能应用于实际系统,为此用数字信号处理器建立计算机与无刷电动机之间的数据传输机构,根据实际响应来进行最佳控制器的设计,实现了最佳控制参数的搜寻.     

干扰因素下无线电能传输频率控制算法研究

为了提高无线电能传输WPT(wireless power transmission)负载端接收功率以及效率,增强传输的稳定性,研究无线电能传输系统过耦合干扰因素下无线电能传输频率控制算法,解决在该种干扰因素下频率分裂引起的传输功率下降问题。采用自适应频率跟踪WPT系统,依据DSP控制DDS(direct digital synthesis)自动调节输出频率,完成无线电能传输频率的自适应跟踪控制。采用改进粒子群优化算法,以进化因子和时间变动为依据进行自适应调整粒子的惯性权重和学习因子,提高粒子寻求最优解的速度和粒子算法的搜索力,获取无线电能传输系统功率和效率的最优值,增强频率跟踪的速度和精度。结合Zigbee,向DSP中植入改进粒子群优化算法,控制无线电能传输系统射频源频率,完成无线电能传输系统功率和效率同步频率跟踪,增强过耦合运行状态下无线电能传输负载端接收功率及效率。实验表明,该控制算法可在临界耦合点前提高无线电能传输系统整体功率,且能提高无线电能传输系统效率,系统发射端频率得到平稳控制,性能得到改善。     

基于SDRE的永磁同步电动机的近似最优控制

针对具有仿射非线性模型的永磁同步电动机,研究其非二次型性能指标的最优控制问题。非线性非二次型最优控制问题导致难以求解的HJB(Hamilton Jacobi Bellman)方程,为避免这一难题,首先采用SDRE(State-Depend-ent Riccati Equation)法将其转化成状态相关的Riccati方程求解问题。SDRE法需要在每一次采样时刻实时地求解一个代数Riccati方程,在高阶系统中巨大的运算耗时使得SDRE控制器难以实现,为此进一步采用一种有效求解SDRE控制器的方法 -θ-d法,它只需求解一次与初始状态相关的代数Riccati方程,控制器的其余参数均可离线通过矩阵的相关运算获得,从而大大地减少了在线计算量。另外,SDRE方法可以通过适当选取状态相关的加权矩阵,以较小的控制作用获得与常量权矩阵相近甚至更好的控制效果。最后,以永磁同步电动机为实例进行的仿真验证了本文方法的有效性与可行性。     

基于自适应遗传算法的无刷直流电机模糊滑模控制器设计

将自适应遗传算法(Adaptive Genetic Algorithm简称AGA)搜寻最佳化的模糊滑模控制器(Fuzzy Sliding Mode Controller,简称FSMC)参数,应用于无刷直流电机系统.传统计算仿真的最佳控制器通常经手调才能应用于实际系统.本文用DSP建立计算机与无刷直流电机之间的数据传输机构,根据实际响应进行控制器的设计,并讨论其性能.     

Experience replay–based output feedback Q‐learning scheme for optimal output tracking control of discrete‐time linear systems

This paper focuses on solving the adaptive optimal tracking control problem for discrete‐time linear systems with unknown system dynamics using output feedback. A Q‐learning‐based optimal adaptive control scheme is presented to learn the feedback and feedforward control parameters of the optimal tracking control law. The optimal feedback parameters are learned using the proposed output feedback Q‐learning Bellman equation, whereas the estimation of the optimal feedforward control parameters is achieved using an adaptive algorithm that guarantees convergence to zero of the tracking error. The proposed method has the advantage that it is not affected by the exploration noise bias problem and does not require a discounting factor, relieving the two bottlenecks in the past works in achieving stability guarantee and optimal asymptotic tracking. Furthermore, the proposed scheme employs the experience replay technique for data‐driven learning, which is data efficient and relaxes the persistence of excitation requirement in learning the feedback control parameters. It is shown that the learned feedback control parameters converge to the optimal solution of the Riccati equation and the feedforward control parameters converge to the solution of the Sylvester equation. Simulation studies on two practical systems have been carried out to show the effectiveness of the proposed scheme.     

Design of a near‐optimal adaptive filter in digital signal processor for active noise control

Adaptive filter has been applied in adaptive feedback and feedforward control systems, where the filter dimension is often determined by trial‐and‐error. The controller design based on a near‐optimal adaptive filter in digital signal processor (DSP) is developed in this paper for real‐time applications. The design integrates the adaptive filter and the experimental design such that their advantages in stability and robustness can be combined. The near‐optimal set of controller parameters, including the sampling rate, the dimension of system identification model, the dimension (order) of adaptive controller in the form of an FIR filter, and the convergence rate of adaptation is shown to achieve the best possible system performance. In addition, the sensitivity of each design parameter can be determined by analysis of means and analysis of variance. Effectiveness of the adaptive controller on a DSP is validated by an active noise control experiment. Copyright © 2007 John Wiley & Sons, Ltd.     

Optimal and self‐tuning weighted measurement fusion Kalman filters and their asymptotic global optimality

For the multisensor linear discrete time‐invariant stochastic systems with unknown noise variances, using the correlation method, the information fusion noise variance estimators with consistency are given by taking the average of the local noise variance estimators. Substituting them into two optimal weighted measurement fusion steady‐state Kalman filters, two new self‐tuning weighted measurement fusion Kalman filters with a self‐tuning Riccati equation are presented. By the dynamic variance error system analysis (DVESA) method, it is rigorously proved that the self‐tuning Riccati equation converges to the steady‐state optimal Riccati equation. Further, by the dynamic error system analysis (DESA) method, it is proved that the steady‐state optimal and self‐tuning Kalman fusers converge to the global optimal centralized Kalman fuser, so that they have the asymptotic global optimality. Compared with the centralized Kalman fuser, they can significantly reduce the computational burden. A simulation example for the target tracking systems shows their effectiveness. Copyright © 2010 John Wiley & Sons, Ltd.     

Digital predistortion linearizes wireless power amplifiers

When compared to other linearization methods, adaptive DP provides sufficient linearization with less complex RF hardware by depending primarily upon DSP rather than analog manipulation. The adaptive DP system may be implemented in EDA software along with interconnected test equipment (arbitrary RF signal source and vector signal analyzer). Through the use of a training signal, a simple adaptive algorithm may be employed to update the LUT coefficients until an optimum setting is achieved and used to predistort the input to the amplifier. This "connected solution" approach can provide key information to design engineers for optimizing the DSP architecture of a PA. For future work, other issues are taken into account, such as temperature and electrical memory effects.