神经网络广义逆系统控制

提出适合于高阶非线性系统线性化解耦的广义逆系统，它与被控系统复合后，不但能实现原系统的线性化和解耦，而且通过合理地设计逆系统，可使伪线性复合系统的极点在复平面上任意配置，进一步提出由静态神经网络和若干积分惯性等线性环节组成的神经网络广义逆系统，为模型未知且内部状态不易测量的高阶非线性系统的线性化解耦控制提供一条有效途径，进一步拓展了神经网络逆系统控制方法的适用范围。     

神经网络广义逆系统控制

提出适合于高阶非线性系统线性化解耦的广义逆系统.它与被控系统复合后,不但能实现原系统的线性化和解耦,而且通过合理地设计逆系统,可使伪线性复合系统的极点在复平面上任意配置.进一步提出由静态神经网络和若干积分惯性等线性环节组成的神经网络广义逆系统,为模型未知且内部状态不易测量的高阶非线性系统的线性化解耦控制提供一条有效途径,进一步拓展了神经网络逆系统控制方法的适用范围.     

两电机变频系统神经网络广义逆内模控制

为了提高非线性强耦合的两电机变频调速系统的解耦控制性能和鲁棒性能,提出了基于神经网络广义逆系统的二自由度内模控制方法。先对原系统数学模型进行广义逆存在性分析,进而推导出原系统的广义逆数学模型,再用动态神经网络逼近广义逆模型,从而串接在原系统之前组成广义伪线性复合系统,实现系统的解耦线性化与开环稳定,有利于系统的综合。然后对广义伪线性系统引入二自由度内模控制,保证系统的鲁棒稳定性。最后基于S7-300的平台,做了相关的试验研究。结果表明,该方法不但能够很好地实现系统的解耦,而且当系统存在建模误差和负载扰动的情况时,仍能使系统保持高性能的控制。     

一种新型神经网络广义逆系统

提出一种新型带有反馈环节的广义逆系统方法,并给出了其存在性的证明．该方法首先利用神经网络构造被控对象的α 阶逆系统,通过改变反馈环节参数可随时任意配置复合系统极点,无需重新构造广义逆系统．分别对ＳＩＳＯ 和ＭＩＭＯ 非线性系统进行仿真研究．仿真结果表明,在配置极点变换时,利用该方法构造的广义逆系统仍可对原系统实现有效的线性化和解耦．

     

非线性系统的神经网络自适应逆控制

提出了非线性系统的神经网络自适应逆控制方法。设计中使用了2个神经网络，经离线训练的NN1实现非线性系统的逆，在线网络NN2用于补偿逆误差和系统的动态特性变化，对一非线性系统的仿真结果表明，神经网络自适应逆控制能够提高系统的动态性能，并且具有较好的鲁棒性。     

动物细胞悬浮培养过程神经网络逆解耦控制

动物细胞的悬浮培养以细胞增殖快、生产效率高等优势,成为动物细胞大规模培养的首选方式。而动物细胞悬浮培养过程是一个非线性、强耦合的多输入多输出系统,对一些生物参数(如细胞密度、基质浓度和产物浓度)的控制是提高整个生产水平的关键,应用神经网络逆系统方法对动物细胞悬浮培养过程进行线性化解耦控制,根据培养过程的特点,给出了相应的数学模型,并证明了系统的可逆性,利用神经网络的非线性逼近能力辨识出原系统的逆系统,然后串接在原系统前面构成伪线性复合系统,使动物细胞悬浮培养过程线性化解耦成三个子系统：一阶线性细胞密度子系统、一阶线性基质浓度子系统和一阶线性产物浓度子系统,最后设计模糊PID控制器对各解耦后的线性子系统进行控制,避免了传统PID控制器最优参数选取困难的问题。仿真结果表明,神经网络逆系统方法实现了对动物细胞悬浮培养过程的线性化解耦,系统对给定输入实现了高性能跟踪控制。     

基于神经网络逆系统的超临界流体色谱系统控制

针对超临界流体色谱系统具有多变量、非线性、强耦合的特点,提出一种基于神经网络逆系统的超临界流体色谱系统控制方案。通过RBF神经网络在线逆辨识建立了超临界流体色谱系统的神经网络逆系统模型,并将辨识得到的逆模型作为控制器模型与超临界流体色谱系统进行串联,构成一个伪线性复合系统。并将本方案应用于SFC-SEP600超临界流体色谱系统上进行了梯度洗脱流量控制实验,实验结果表明该模型梯度流量控制精度完全符合技术指标要求,实验结果验证了方案的有效性和可行性。     

MDF施胶系统的神经网络逆复合控制器设计

针对中密度纤维板(MDF)施胶系统,在分析其物理特性的基础上,利用神经网络广义逆控制的方法设计出期望的伪线性系统,并结合经典控制理论,采用PID控制器作为该伪线性系统的闭环控制器.由神经网络广义逆系统与作为附加控制器的PID控制器结合在一起构成的控制器称作神经网络逆复合控制器.为验证该控制器的可行性,通过Matlab进...     

基于逆系统方法的广义非线性系统控制及电力系统应用

对一般广义非线性系统的反馈线性化问题,通过将代数变量视为虚拟输入,给出了构造性递归求逆算法,实现了系统的精确反馈线性化.作为该算法的应用,讨论了带有非线性负荷的三节点电力系统的励磁控制问题,设计了非线性励磁控制器.仿真结果表明了方法的有效性.     

非线性广义系统的右可逆性

研究了广义非线性系统的右可逆性,给出构造性的求逆算法以克服以往结果中需求解非线性方程组的困难,从而使得求逆算法对任意足够光滑的非线性广义系统皆为可行.     

一种感应电机多模型解耦控制方法研究

提出一种基于新型广义逆系统的感应电机多模型解耦控制方法.通过对电机输入空间进行划分,利用神经网络辨识得到其广义逆系统模型,并针对每个子逆模型设计相应的控制器,使得闭环控制系统的传递函数实现任意的零极点配置.通过对电机输入空间的训练数据进行聚类,计算参考信号对每个类的相异度以实现模型之间的切换.仿真实验表明,该方法可对电机的转速和磁链实现有效的解耦控制,整个控制系统具有良好的鲁棒性.     

Nonlinear system compound inverse control method

A compound neural network is utilized to identify the dynamic nonlinear system. This network is composed of two parts： one is a linear neural network, and the other is a recurrent neural network. Based on the inverse theory a compound inverse control method is proposed. The controller has also two parts： a linear controller and a nonlinear neural network controller. The stability condition of the closed-loop neural network-based compound inverse control system is demonstrated .based on the Lyapunov theory. Simulation studies have shown that this scheme is simple and has good control accuracy and robustness.     

Nonlinear system compound inverse control method

A compound neural network is utilized to identify the dynamic nonlinear system.This network is composed of two parts: one is a linear neural network,and the other is a recurrent neural network.Based on the inverse theory a compound inverse control method is proposed.The controller has also two parts:a linear controller and a nonlinear neural network controller.The stability condition of the closed-loop neural network-based compound inverse control system is demonstrated based on the Lyapunov theory.Simulation studies have shown that this scheme is simple and has good control accuracy and robustness.     

Generating generalized inverse Gaussian random variates by fast inversion

The inversion method for generating non-uniformly distributed random variates is a crucial part in many applications of Monte Carlo techniques, e.g., when low discrepancy sequences or copula based models are used. Unfortunately, closed form expressions of quantile functions of important distributions are often not available. The (generalized) inverse Gaussian distribution is a prominent example. It is shown that algorithms that are based on polynomial approximation are well suited for this distribution. Their precision is close to machine precision and they are much faster than root finding methods like the bisection method that has been recently proposed.     

Application of constrained generalized inverse to pattern classification

A constrained MSE procedure in the generalized inverse setting is presented. The procedure is motivated by Fisher's linear discriminant. The procedure is adaptive and tries to classify the means of the classes correctly and then vary the margin of this classification to achieve the least possible errors on the design set. The procedure was carried out on an example with very favorable results.     

Nonlinear system PID-type multi-step predictive control

A compound neural network was constructed during the process of identification and multi-step prediction. Under the PID-type long-range predictive cost function, the control signal was calculated based on gradient algorithm. The nonlinear controller‘ s structure was similar to the conventional PID controller. The parameters of this controller were tuned by using a local recurrent neural network on-line. The controller has a better effect than the conventional PID controller. Sinmlation study shows the effectiveness and good performance.     

Control of a nonlinear liquid level system using a new artificial neural network based reinforcement learning approach

Most industrial processes exhibit inherent nonlinear characteristics. Hence, classical control strategies which use linearized models are not effective in achieving optimal control. In this paper an Artificial Neural Network (ANN) based reinforcement learning (RL) strategy is proposed for controlling a nonlinear interacting liquid level system. This ANN-RL control strategy takes advantage of the generalization, noise immunity and function approximation capabilities of the ANN and optimal decision making capabilities of the RL approach. Two different ANN-RL approaches for solving a generic nonlinear control problem are proposed and their performances are evaluated by applying them to two benchmark nonlinear liquid level control problems. Comparison of the ANN-RL approach is also made to a discretized state space based pure RL control strategy. Performance comparison on the benchmark nonlinear liquid level control problems indicate that the ANN-RL approach results in better control as evidenced by less oscillations, disturbance rejection and overshoot.     

A new generalized particle approach to parallel bandwidth allocation

This paper presents a new generalized particle (GP) approach to dynamical optimization of network bandwidth allocation, which can also be used to optimize other resource assignments in networks. By using the GP model, the complicated network bandwidth allocation problem is transformed into the kinematics and dynamics of numerous particles in two reciprocal dual force-fields. The proposed model and algorithm are featured by the powerful processing ability under a complex environment that involves the various interactions among network entities, the market mechanism between the demands and service, and other phenomena common in networks, such as congestion, metabolism, and breakdown of network entities. The GP approach also has the advantages in terms of the higher parallelism, lower computation complexities, and the easiness for hardware implementation. The properties of the approach, including the correctness, convergency and stability, are discussed in details. Simulation results attest to the effectiveness and suitability of the proposed approach.