自适应逆控制方法研究及其应用
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摘要
自适应逆控制将传统的控制理论与信号处理理论相结合,成为研究控制问题的一种新颖方法,其实现的基础就是自适应滤波技术及控制结构设计。本文以基本的自适应逆控制方法为出发点,根据不同对象设计了相应的自适应逆控制方法,在改进线性系统和非线性系统自适应逆控制方法、自适应建模方法、多变量自适应逆控制方法的结构设计等方面进行了深入的研究并提出了一些新的思路,丰富和扩展了原有的自适应逆控制方法。
1.基于自适应逆控制方法设计了主汽温系统的双回路扰动消除系统。采用基于神经网络的自适应预测器解决扰动消除回路的延迟,实现了主汽温系统内外回路输出端扰动的瞬时消除。
2.提出使用Givens变换简化Volterra泛函级数建模维数的解决方案,使得改进的Volterra基函数网络运算量大为减少,提高了系统运算速度。将改进Volterra基函数网络应用于自适应前馈控制系统及具有不确定性的非线性自适应逆控制系统。仿真结果表明改进的Volterra基函数网络组成的自适应逆控制系统在网络规模较小的前提下,仍具有令人满意的控制精度和较强的鲁棒性。
3.提出了一种基于自适应逆控制方法的二阶神经网络执行机构死区逆补偿方法,实现了对未知非线性死区环节的动态补偿。这种方法用于死区补偿不需要任何死区非线性和约束性假设,实现了由神经网络估计器和神经网络补偿器组成的补偿策略,为非线性补偿提供了新的解决方法。此外,本文使用了BP网络和RBF网络进行神经网络在非线性系统自适应逆控制中的研究。
4.提出了基于支持向量回归的自适应逆控制方法。采用支持向量回归在线辨识算法作为建模方法建立被控对象的逆模型。由于支持向量回归具有更好的推广性,从而使整个系统具有更好的控制性能。
5.提出了基于多变量内模控制的多输入多输出系统(MIMO)自适应逆控制方法。将多变量内模控制的控制器结构设计与MIMO系统自适应逆控制的多变量建模方法相结合,应用于电厂单元机组负荷系统、钢球磨煤机中间储仓制粉系统。并且针对存在时滞的多变量对象,采用对角矩阵补偿和加入时间预测器来解决,应用于钢球磨煤机中间储仓制粉系统,仿真结果表明控制方法的有效性。
The traditional control theory is combined with the signal processing theory to form a novel method for the control problems--Adaptive inverse control. Its realization is based on adaptive filtering technology and design of control configuration. In this paper, based on the elementary adaptive inverse control methods, different methods are designed according to the various objects. Improvements on the methods of adaptive inverse control for linear systems and nonlinear systems, adaptive modeling method and configuration design of adaptive inverse control for multivariable objects are deeply researched and some new ideas are proposed, which enriches and extends the original methods of adaptive inverse control.
1. Based on the methods of adaptive inverse control, double loops of disturbance canceling system are designed for the main steam temperature system. Adaptive predictor is based on neural network to solve the delay for the loops of disturbance canceling, and then the output disturbance is instantaneously cancelled of double loops for the main steam temperature system.
2. Givens transform is proposed to solve the dimentions problem of Volterra series modeling, which greatly reduces the calculation of the network composed of volterra basic polynomial functions. This improved network is applied to adaptive feedforward control system and adaptive inverse control system with some uncertainties. Simulation results showed that this improved adaptive inverse control system still has satisfying control precision and strong robustness.
3. A deadzone inverse compensation of second-order neural network method based on adaptive inverse control is proposed for implemented organization, which realized the dynamic compensation for unknown nonlinear deadzone. This method doesn’t require any hypothesis of nonlinear and constraint for deadzone, and realized the compesation strategy composed of estimator and compensator using neural network, which supplies a new solution for nonlinear compensation. Moreover, BP and RBF neural network are used to research on the adaptive inverse control for nonlinear systems.
4. The support vector regression is introduced to adaptive inverse control systems. Online identification algorithm of support vector regression is used to build the inverse model for the plant. Due to its favorable generalization, the total system has fine control performance.
5. Adaptive inverse control method for Multi-input and multi-output (MIMO) plant based on multi-variable internal model control is proposed in this paper. The configuration design of controller in multi-variable internal model control is combined with multi-variable modeling in MIMO Adaptive inverse control method to be applied into units load system and ball mill system. For the delay multi-variable plant, diagonal matrix compensator and time predictor are used to ball miss system, and the result shows that this method is effective.
1.基于自适应逆控制方法设计了主汽温系统的双回路扰动消除系统。采用基于神经网络的自适应预测器解决扰动消除回路的延迟,实现了主汽温系统内外回路输出端扰动的瞬时消除。
2.提出使用Givens变换简化Volterra泛函级数建模维数的解决方案,使得改进的Volterra基函数网络运算量大为减少,提高了系统运算速度。将改进Volterra基函数网络应用于自适应前馈控制系统及具有不确定性的非线性自适应逆控制系统。仿真结果表明改进的Volterra基函数网络组成的自适应逆控制系统在网络规模较小的前提下,仍具有令人满意的控制精度和较强的鲁棒性。
3.提出了一种基于自适应逆控制方法的二阶神经网络执行机构死区逆补偿方法,实现了对未知非线性死区环节的动态补偿。这种方法用于死区补偿不需要任何死区非线性和约束性假设,实现了由神经网络估计器和神经网络补偿器组成的补偿策略,为非线性补偿提供了新的解决方法。此外,本文使用了BP网络和RBF网络进行神经网络在非线性系统自适应逆控制中的研究。
4.提出了基于支持向量回归的自适应逆控制方法。采用支持向量回归在线辨识算法作为建模方法建立被控对象的逆模型。由于支持向量回归具有更好的推广性,从而使整个系统具有更好的控制性能。
5.提出了基于多变量内模控制的多输入多输出系统(MIMO)自适应逆控制方法。将多变量内模控制的控制器结构设计与MIMO系统自适应逆控制的多变量建模方法相结合,应用于电厂单元机组负荷系统、钢球磨煤机中间储仓制粉系统。并且针对存在时滞的多变量对象,采用对角矩阵补偿和加入时间预测器来解决,应用于钢球磨煤机中间储仓制粉系统,仿真结果表明控制方法的有效性。
The traditional control theory is combined with the signal processing theory to form a novel method for the control problems--Adaptive inverse control. Its realization is based on adaptive filtering technology and design of control configuration. In this paper, based on the elementary adaptive inverse control methods, different methods are designed according to the various objects. Improvements on the methods of adaptive inverse control for linear systems and nonlinear systems, adaptive modeling method and configuration design of adaptive inverse control for multivariable objects are deeply researched and some new ideas are proposed, which enriches and extends the original methods of adaptive inverse control.
1. Based on the methods of adaptive inverse control, double loops of disturbance canceling system are designed for the main steam temperature system. Adaptive predictor is based on neural network to solve the delay for the loops of disturbance canceling, and then the output disturbance is instantaneously cancelled of double loops for the main steam temperature system.
2. Givens transform is proposed to solve the dimentions problem of Volterra series modeling, which greatly reduces the calculation of the network composed of volterra basic polynomial functions. This improved network is applied to adaptive feedforward control system and adaptive inverse control system with some uncertainties. Simulation results showed that this improved adaptive inverse control system still has satisfying control precision and strong robustness.
3. A deadzone inverse compensation of second-order neural network method based on adaptive inverse control is proposed for implemented organization, which realized the dynamic compensation for unknown nonlinear deadzone. This method doesn’t require any hypothesis of nonlinear and constraint for deadzone, and realized the compesation strategy composed of estimator and compensator using neural network, which supplies a new solution for nonlinear compensation. Moreover, BP and RBF neural network are used to research on the adaptive inverse control for nonlinear systems.
4. The support vector regression is introduced to adaptive inverse control systems. Online identification algorithm of support vector regression is used to build the inverse model for the plant. Due to its favorable generalization, the total system has fine control performance.
5. Adaptive inverse control method for Multi-input and multi-output (MIMO) plant based on multi-variable internal model control is proposed in this paper. The configuration design of controller in multi-variable internal model control is combined with multi-variable modeling in MIMO Adaptive inverse control method to be applied into units load system and ball mill system. For the delay multi-variable plant, diagonal matrix compensator and time predictor are used to ball miss system, and the result shows that this method is effective.
引文
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