【作者】 张懿；

【导师】 刘国海；

【作者基本信息】 江苏大学 ， 控制理论与控制工程， 2011， 博士

【摘要】 两电机变频调速系统在现代工业领域中应用非常广泛,其高性能控制对于提高生产质量和效率具有重要的意义。近年来,逆系统方法已在一般形式的非线性系统上建立起比较完整的设计理论,物理意义清晰,结构简单。将逆系统方法与最小二乘支持向量机(LSSVM)结合,提出的LSSVM逆系统方法,突破了传统逆系统方法在实际工程应用中解析表达式难以获取的瓶颈。针对两电机变频调速系统所具有的高阶、强耦合、非线性的特点,基于LSSVM逆系统理论,本文研究了系统的模型辨识、控制器设计等问题,论文的主要内容如下：(1)阐述了逆系统理论的基本概念、系统可逆性原理,将逆系统与原系统串联构成伪线性复合系统,可实现系统的线性化与解耦。继而,概述了LSSVM回归算法,用以实现对逆系统非线性映射的逼近。对课题研究所用的两电机变频调速系统实验平台的软、硬件组成及工作原理作了简要介绍,为控制策略的理论分析、实验验证提供了必需的知识准备和条件支持。(2)构建了两电机变频调速系统的数学模型,对矢量控制下的数学模型进行了右逆存在性分析,证明系统是右可逆的。采用逆系统的扩展结构,提出了LSSVM自抗扰逆控制策略。引入自抗扰控制器作为伪线性复合系统的附加控制器,并将扩张状态观测器的扰动观测结果用于LSSVM逆系统的构造,在实现系统线性化和解耦控制的前提下,使整个系统具有良好的鲁棒性和抗干扰能力。(3)为解决因张力传感器的安装而引起的系统成本增加、可靠性降低等缺陷,提出了张力LSSVM左逆辨识策略。分析了张力软测量的左可逆性,利用容易测取的速度信号构造张力“内含传感器”左逆软测量模型,实现了对张力的辨识。该软测量方案不依赖系统模型和具体参数,计算量小,实现简单。(4)借助于张力左逆软测量方法,提出广义联合逆控制器设计方法。基于广义逆系统理论,将左逆软测量和右逆系统相融合,形成具有整体形式的广义联合逆控制器,并对其中的非线性映射采用LSSVM回归逼近。将该联合逆控制器应用于两电机变频调速系统,实现系统的无张力传感器运行。(5)分析了按转子磁场定向矢量控制的两电机变频调速系统特点,利用数据驱动原理,对输入输出样本数据进行二次AP聚类,对二次聚类后的各子类数据建立局部LSSVM模型,并对各个局部模型加权综合,得到的张力和速度的全局模型能准确拟合两电机变频调速系统的非线性特性。(6)针对现有逆模型辨识方法存在的不足,提出了两电机变频调速系统基于改进RLS算法的LSSVM逆模型辨识方法。依据多模型思想,将系统输出空间进行划分,使用LSSVM拟合获得局部逆模型,并对各局部逆模型加权综合得到系统初始逆模型。根据逆模型输出与系统输入的误差,利用改进RLS算法可在线调整局部LSSVM逆模型的权值,降低了逆模型在线调整的难度。最后,在总结全文的基础上,提出了课题下一步研究的重点。更多还原

【Abstract】 Two-motor variable frequency speed-regulating system is widely used in the modern industrial manufacturing plants. In order to improve quality and quantity of the products, the high-performance control system is needed. In recent years, the inverse system method has been developed to a complete control theory for the general form of nonlinear systems. It is easy in practical use and strict in theory. Since least squares support vector machines (LSSVM) has a strong potential to approximate a nonlinear function, a new LSSVM inverse model is obtained by introducing the LSSVM into the inverse system. The LSSVM inverse model can overcome the difficulty in implementing the inverse system by analytic means and break through the bottleneck in engineering applications. According to the characteristics of multi-variables, high nonlinearity and strong-coupling, the model identification and the controller design for the two-motor system are investigated based on the LSSVM inverse system. The main contents and fruits of this thesis are as follows:1) The fundamental concept of the inverse system theory and the system reversible principle are introduced. The inverse system includes the left and the right inverse system. By cascading the inverse system with the original system, the whole system is decoupled to a pseudo-linear combined system in order to implement linearization and decoupling control. The LSSVM regression algorithm, which is adopted to approximate nonlinear mapping of the inverse system, is depicted. Experimental platform of two-motor variable frequency speed-regulating system is presented, which provides necessary support.2) Inversibility of the two-motor system in vector control mode is verified. By using the extended structure of the inverse system, the LSSVM inverse control strategy based on active disturbances rejection control (ADRC) is proposed. A pseudo-linear system is completed by combining the LSSVM inverse model with the two-motor system. ADRC is used as a closed-loop controller for the pseudo-linear system, and the uncertainty estimated by the extended state observer is used to construct the LSSVM inverse model. The proposed method can reduce the interference caused by load disturbance and modeling error. The good decoupling applicability and strong robustness can be achieved meanwhile.3) To solve the problems such as high cost and poor reliability caused by tension sensors, tension identification based on the LSSVM left inverse is proposed. The left-invertibility of the tension model is proved. Speed signal which is relatively easy to be measured has been used to build the "assumed inherent sensor" left inverse soft-sensing model for tension identification. The proposed method can identify the actual tension quickly and accurately, independent of dynamic model and specific parameters of the two-motor system.4) Taking full advantage the left inverse soft-sensing model in estimating the system state, a new generalized combined inverse controller is proposed. Based on the generalized inverse theory, the combination of the left inverse and the right inverse forms a whole combined inverse controller. LSSVM is used to implement it, resulting in the LSSVM combined inverse controller. Sensorless operation of the two-motor system can be achieved through the LSSVM combined inverse controller.5) The characteristics of the two-motor system by the rotor magnetic field-oriented vector control are analyzed. A new modeling method is proposed based on the data-driven principle. The affinity propagation (AP) clustering algorithm is successively applied to group the input and the output data into clusters. LSSVM is used to construct local models. The estimation of every LSSVM local model is fused to build the speed model and the tension model. The proposed method can fit the nonlinear characteristics of the system with high precision and good generalization.6) To mend the defects associated with conventional inverse model identification methods for two-motor system, a multi-model LSSVM modeling method based on the modified recursive least-squares (RLS) algorithm is proposed. The initial, off-line, inverse model of the system is deduced by computing the weighted sum of every LSSVM local model. The modified RLS algorithm is adopted to adjust the weights according to system deviation adaptively. The proposed method is feasible, effective, and suitable for two-motor system.Finally, some conclusions and further research projects are raised.更多还原