共查询到17条相似文献,搜索用时 140 毫秒
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基于粒子群优化的非线性系统最小二乘支持向量机预测控制方法 总被引:11,自引:3,他引:8
对于非线性系统预测控制问题, 本文提出了一种基于模型学习和粒子群优化(PSO)的单步预测控制算法.该方法使用最小二乘支持向量机(LS-SVM)建立非线性系统模型并预测系统的输出值, 通过输出反馈和偏差校正减少预测误差, 由PSO滚动优化获得非线性系统的控制量. 该方法能在非线性系统数学模型未知的情况下设计出有效的预测控制器. 通过对单变量多变量非线性系统进行仿真, 证明了该预测控制方法是有效的, 且具有良好的自适应能力和鲁棒性. 相似文献
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研究电厂锅炉温度优化控制问题,由于温度的稳定性受到多种因素的影响,所以温度控制系统是一个具有时滞性、非线性和时变性的复杂系统,传统PID控制和模糊控制难以建立精确的数学模型,控制系统的超调时间长,超调大.为了解决控制系统的超调时间长,超调大等问题,为了优化温控制系统,在传统PID控制和Smith预估计器的基础上,结合模糊控制系统良好非线性优点,提出模糊Smith的温控制系统.方法通过Smith预估器对模型的时滞性进行补偿,使时滞系统的超调减小,系统的稳定性增强.通过建立温控制系统数学模型并进行仿真实验,结果表明模糊Smith的控制方法降低了超调量,缩短了超调时间,有效提高了温控制系统的鲁棒性和抗干扰性. 相似文献
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针对控制工程中出现的控制量波动都较大的广义预测控制问题,提出了一种改进的预测控制算法.通过对传统广义预测控制算法的控制目标函数中增加输出增量优化目标项,并充分利用预测控制的输入增量变化信息,在线对控制增量加权系数进行修正,用以抑制输入增量的较大波动,从而使输出量的波动和超调得到改善,增强了系统的稳定性.仿真结果验证了该方法的可行性和有效性,效果良好. 相似文献
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一种基于Wiener模型的非线性预测控制算法 总被引:3,自引:0,他引:3
针对一类Wiener模型描述的非线性系统,提出了一种改进的非线性预测控制算法.该算法利用Laguerre函数描述Wiener模型动态线性部分的控制信号,将预测控制中在预测时域内优化求解未来控制输入序列转化为优化求解一组无记忆的Laguerre系数,以减少优化所需的计算量.利用静态模糊模型来逼近Wiener模型的非线性部分,将非线性预测控制优化问题转化为线性预测控制优化问题,克服了求控制输入时解非线性方程的困难,进而推导出了预测控制输入的解析式.CSTR过程的仿真结果表明了本文算法的有效性和可行性. 相似文献
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《控制理论与应用》2015,(8)
针对环管式聚丙烯生产过程装置多变量、耦合和非线性等特性容易导致过程控制不稳定及质量指标波动问题,本文提出了一种基于修正闭环子空间辨识–分段线性(MSSARX--PWL)维纳(Wiener)模型结构的非线性模型预测控制算法.利用修正的闭环子空间辨识方法(MSSARX)辨识对象在闭环工况下的线性状态空间模型,并将该线性模型与多变量分段线性化(PWL)方法辨识得到的非线性稳态模型结合,建立双环管丙烯聚合反应动态过程的非线性预测模型,而后进一步将非线性模型转化为线性模型,在线性预测控制算法框架下用二次线性规划方法(LQP)优化控制器,无须用非线性规划方法(NLP)求解.从双环管丙烯聚合反应过程仿真例子表明,该算法不仅能保证模型和控制精度,而且能提高计算效率. 相似文献
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双线性模型预测控制的研究表明,采用一般双线性模型的预测控制将涉及非线性优化问题,在线处理相当困难,而采用线性近似模型的预测控制又会带来较大的偏差.针对一类输入一输出双线性系统,提出了一种双线性系统的广义预测控制算法.该算法将基于输入-输出模型双线性系统中的双线性项和线性项合并,建立了一种类似于线性系统的ARIMA模型,并充分利用多步最优预测信息,由递推近似实现多步预测.控制律具有解析形式,避免了一般非线性寻优的复杂计算,并能适用于非最小相位双线性系统.仿真实验表明该算法具有良好的控制效果. 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
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研究PID控制系统优化问题,工业控制被控对象均具有非线性、时变和大时滞性,引起系统的品质性能差,传统的线性控制难以达到所要求精度。为了提高系统控制精度,利用PID控制器各增益参数与偏差信号间的非线性关系,提出一种非线性PID控制算法。首先将PID参数转化为优化问题,然后采用粒子群算法的全局、并行搜索能力对非线性控制参数进行求解,得到一组最优的PID控制参数。仿真结果表明,相对于传统线性PID控制,非线性PID控制器超调小,调节时间短,并提高了控制精度,有效解决了传统PID难以准确控制非线性对象的难题。 相似文献
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A hybrid pseudo-linear RBF-ARX model that combines Gaussian radial basis function (RBF) networks and linear ARX model structure is utilized for representing the dynamic behavior of a class of smooth nonlinear and non-stationary systems. This model is locally linear at each working point and globally nonlinear within whole working range. Based on the structural characteristics of the RBF-ARX model, three receding horizon predictive control (RBF-ARX-MPC) strategies are designed: (1) the RBF-ARX-MPC algorithm based on single-point linearization (MPC-SPL); (2) the RBF-ARX-MPC algorithm based on multi-point linearization (MPC-MPL); and (3) the RBF-ARX-MPC algorithm based on globally nonlinear optimization (MPC-GNO). In the MPC-SPL, the future multi-step-ahead predictive output of the system is obtained based on the local linearization of the RBF-ARX model at only current working-point, while in the MPC-MPL the future long-term output prediction is obtained according to the future local characteristics from previous online optimization results of the RBF-ARX model based MPC. In the MPC-GNO, the globally nonlinear characteristics of the RBF-ARX model are fully used for online getting control variables of the MPC. Real-time control experiments for the three type MPCs are carried out on a water tank system, which are also compared with a classical PID control and a traditional linear ARX model-based MPC. The results verify that the modeling method and the model-based predictive control strategies are realizable and effective for the nonlinear and unstable system. Moreover, it is also shown that the MPC-GNO can obtain better control performance but need more computation time compared to the other MPCs, which makes it possible to be applied into some slowly varying processes. 相似文献
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In this paper the activated sludge process, which is a process for biological nitrogen removal in municipal wastewater treatment plants, is modeled as a discrete-time bilinear system by application of a recursive prediction error method system identification technique. A novel bilinear model predictive control algorithm is also derived and applied on a simulation model of the activated sludge process. For discrete-time bilinear systems, a quadratic cost on the predicted outputs and inputs, together with input/state constraints, results in a nonlinear non-convex optimization problem. An investigation is performed where the suggested control algorithm is compared with a linear counterpart. The results reveals that even though the identified bilinear black-box model describes the dynamics of the activated sludge process better than linear black-box models, bilinear model predictive control only gives moderate improvements of the control performance compared to linear model predictive control laws. 相似文献