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针对一类非线性多变量离散时间动态系统,提出了基于神经网络与多模型的非线性自适应广义预测解耦控制方法.该控制方法由线性鲁棒广义预测解耦控制器和神经网络非线性广义预测解耦控制器以及切换机构组成.线性鲁棒广义预测解耦控制器用于保证闭环系统输入输出信号有界,神经网络非线性广义预测解耦控制器能够改善系统性能.切换策略通过对上述两种控制器的切换,保证系统稳定的同时,改善系统性能.同时本文给出了所提自适应解耦控制方法的稳定性和收敛性分析.最后,通过仿真实例验证了该方法的有效性. 相似文献
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基于神经网络与多模型的非线性自适应广义预测解耦控制 总被引:1,自引:0,他引:1
针对一类非线性多变量离散时间动态系统,提出了基于神经网络与多模型的非线性自适应广义预测解耦控制方法.该控制方法由线性鲁棒广义预测解耦控制器和神经网络非线性广义预测解耦控制器以及切换机构组成.线性鲁棒广义预测解耦控制器用于保证闭环系统输入输出信号有界,神经网络非线性广义预测解耦控制器能够改善系统性能.切换策略通过对上述两种控制器的切换,保证系统稳定的同时,改善系统性能.同时本文给出了所提自适应解耦控制方法的稳定性和收敛性分析.最后,通过仿真实例验证了该方法的有效性. 相似文献
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基于神经网络与多模型的非线性自适应广义预测控制 总被引:9,自引:0,他引:9
针对一类不确定非线性离散时间动态系统, 提出了基于神经网络与多模型的非线性广义预测自适应控制方法. 该自适应控制方法由线性鲁棒广义预测自适应控制器, 神经网络非线性广义预测自适应控制器和切换机制三部分构成. 线性鲁棒广义预测自适应控制器保证闭环系统的输入输出信号有界, 神经网络非线性广义预测自适应控制器能够改善系统的性能. 切换策略通过对上述两种控制器的切换, 保证系统稳定的同时, 改善系统性能. 给出了所提自适应方法的稳定性和收敛性分析. 最后通过仿真实例验证了所提方法的有效性. 相似文献
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对一类未知多变量非线性系统提出了直接自适应模糊预测控制方法,此方法首先对被控对象提出了线性时变子模型加非线性子模型的预测模型,然后直接用模糊逻辑系统组成的向量来设计预测控制器,并基于时变死区函数对控制器中的未知向量和广义误差估计值中的未知矩阵进行自适应调整.文中证明了此方法可使广义误差向量估计值收敛到原点的一个邻域内. 相似文献
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用多模型的广义预测控制器对复杂的非线性液位系统进行仿真控制。通过在覆盖工况的若干个平衡点采用最小二乘法离线辨识建立多个线性模型,形成非线性系统的多模型表示,然后对各个子模型分别设计子控制器,采用基于相对残差的方法来实现控制增量的加权以获取控制增量。通过对单容液位系统的仿真,表明该方法的有效性。 相似文献
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针对广义预测控制(GPC)算法稳定性分析困难,对参数未知非线性系统提出一种稳定广义预测控制(DGPC)方法。该方法首先将非线性系统转换为时变线性系统,然后利用三次样条基函数逼近时变系统中的系数,通过带时变遗忘因子的递推最小二乘算法辨识系数获得对象模型。基于模型通过性能指标中的前馈增益设计来保证控制系统稳定,仿真结果验证了该方法的有效性。 相似文献
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提出了基于小波变换的非线性广义预测控制算法。预测模型采用Hammerstein模型,对于其静态非线性部分采用小波网络来辨识,动态线性部分用最小二乘法来辨识。这种辨识方法比传统的多项式拟合的模型误差要小得多。基于这种预测模型广义预测控制器弥补了传统广义预测控制的模型失配问题。以CSTR为例对所设计的控制器进行仿真研究,结果表明控制器能够取得良好的控制效果。 相似文献
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A nonlinear predictive generalised minimum variance control algorithm is introduced for the control of nonlinear discrete-time multivariable systems. The plant model is represented by the combination of a very general nonlinear operator and also a linear subsystem which can be open-loop unstable and is represented in state-space model form. The multi-step predictive control cost index to be minimised involves both weighted error and control signal costing terms. The solution for the control law is derived in the time domain using a general operator representation of the process. The controller includes an internal model of the nonlinear process, but because of the assumed structure of the system, the state observer is only required to be linear. In the asymptotic case, where the plant is linear, the controller reduces to a state-space version of the well-known GPC controller. 相似文献
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本文结合现场的实际过程数据,首先应用能量平衡建立了强制循环蒸发过程的动态模型.针对该过程的多变量、非线性以及强耦合特性,在常规增量式PID控制器的基础上提出基于神经网络与多模型切换的非线性自适应解耦PID控制策略.该控制器是由线性自适应解耦PID控制器和基于神经网络的非线性自适应解耦PID控制器以及切换机构组成.其中线性自适应解耦PID控制器可以保证系统的稳定,而基于神经网络的非线性自适应解耦PID控制器则可以有效地提高系统的性能.上述过程的PID参数是通过广义预测的方法得到,最后通过仿真表明,上述控制方法不仅消除了回路间的耦合,在稳定生产的同时提高了蒸发的效率. 相似文献
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Guang-Yan Zhu Michael A. Henson Babatunde A. Ogunnaike 《Journal of Process Control》2000,10(5):449-458
A plant-wide control strategy based on integrating linear model predictive control (LMPC) and nonlinear model predictive control (NMPC) is proposed. The hybrid method is applicable to plants that can be decomposed into approximately linear subsystems and highly nonlinear subsystems that interact via mass and energy flows. LMPC is applied to the linear subsystems and NMPC is applied to the nonlinear subsystems. A simple controller coordination strategy that counteracts interaction effects is proposed for the case of one linear subsystem and one nonlinear subsystem. A reactor/separator process with recycle is used to compare the hybrid method to conventional LMPC and NMPC techniques. 相似文献
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The linear model predictive control which is frequently used for building climate control benefits from the fact that the resulting optimization task is convex (thus easily and quickly solvable). On the other hand, the nonlinear model predictive control enables the use of a more detailed nonlinear model and it takes advantage of the fact that it addresses the optimization task more directly, however, it requires a more computationally complex algorithm for solving the non-convex optimization problem. In this paper, the gap between the linear and the nonlinear one is bridged by introducing a predictive controller with linear time-dependent model. Making use of linear time-dependent model of the building, the newly proposed controller obtains predictions which are closer to reality than those of linear time invariant model, however, the computational complexity is still kept low since the optimization task remains convex. The concept of linear time-dependent predictive controller is verified on a set of numerical experiments performed using a high fidelity model created in a building simulation environment and compared to the previously mentioned alternatives. Furthermore, the model for the nonlinear variant is identified using an adaptation of the existing model predictive control relevant identification method and the optimization algorithm for the nonlinear predictive controller is adapted such that it can handle also restrictions on discrete-valued nature of the manipulated variables. The presented comparisons show that the current adaptations lead to more efficient building climate control. 相似文献