电子节气门模糊自适应调节滑模控制及仿真

电子节气门控制系统具有强非线性时变特性,需采用非线性控制方法进行控制,其中滑模控制应用比较普遍,但系统在切换状态时容易出现抖振现象,不利于系统的精确控制。为改善性能,采用模糊自适应调节滑模控制方法,用模糊逻辑系统来代替传统滑模控制中的指数趋近率系数,从而达到消除滑模控制抖振现象。根据系统数学模型设计了模糊自适应调节滑模控制器,通过在Matlab中进行仿真。仿真结果表明:模糊自适应调节滑模控制能有效解决系统存在的非线性问题,消除滑模控制中出现的抖振,满足系统精确控制的要求。

含新型虚拟电机的直流微网动态稳定性分析与自适应电压惯性控制

为了提升虚拟直流电机的电压惯量与阻尼支持能力,并进一步简化附加控制器结构,显著改善直流微网的动态稳定性,提出了一种新型的虚拟直流电机控制(virtual DC machine control,VDMC)。首先,将双向DC/DC换流器与直流电机进行类比,通过模拟直流电机的功率调节特性,得到适用于双向DC/DC换流器的VDMC模型。其次,通过对所提出VDMC进行改进,得到了更为简化的控制结构,并且具备更加优越的电压动态性能和惯性支撑能力。在此基础上,对改进后的虚拟电机设计自适应电压惯量调节控制技术,使其能够动态响应电压变化,进一步提高系统的动态稳定性。最后,根据阻抗比判据,理论分析所提VDMC对系统的稳定性支持作用,并通过时域仿真算例,验证所提控制策略的有效性。

开流耦合映象格子的稳定态

采用自适应调节控制 ,选择适当的控制参数 ,开流耦合映象格子 (OCML)混沌系统很快就能被控制到均匀的稳定态上去 .用本征值稳定性分析方法可以确定控制参数的范围 .但研究表明由此确定的控制参数范围内的参数并不都能使得系统被控制到稳定态 。

一氧化氮吸入系统的研究现状及发展趋势

从NO气体的配置、NO/NO2气体的监控和NO吸入系统与呼吸机同步工作这三方面的研究现状出发,探讨了当前NO吸入系统研究中存在的问题,展望NO吸入系统可能的发展趋势。

Neural adaptive PSD decoupling controller and its application in three-phase electrode adjusting system of submerged arc furnace

Taking three-phase electrode adjusting system of submerged arc furnace as study object which has nonlinear, time-variant, multivariable and strong coupling features, a neural adaptive PSD(proportion, sum and differential) dispersive decoupling controller was developed by combining neural adaptive PSD algorithm with dispersive decoupling network. In this work, the production technology process and control difficulties of submerged arc furnace were simply introduced, the necessity of establishing a neural adaptive PSD dispersive decoupling controller was discussed, the design method and the implementation steps of the controller are expounded in detail, and the block diagram of the controlled system is presented. By comparison with experimental results of the conventional PID controller and the adaptive PSD controller, the decoupling ability, adaptive ability, self-learning ability and robustness of the neural adaptive PSD dispersive decoupling controller have been testified effectively. The controller is applicable to the three-phase electrode adjusting system of submerged arc furnace, and it will play an important role for achieving the power balance of three-phrase electrodes, saving energy and reducing consumption in the process of smelting.

A novel robust adaptive controller for EAF electrode regulator system based on approximate model method

The electrode regulator system is a complex system with many variables, strong coupling and strong nonlinearity, while conventional control methods such as proportional integral derivative （PID） can not meet the requirements. A robust adaptive neural network controller （RANNC） for electrode regulator system was proposed. Artificial neural networks were established to learn the system dynamics. The nonlinear control law was derived directly based on an input-output approximating method via the Taylor expansion, which avoids complex control development and intensive computation. The stability of the closed-loop system was established by the Lyapunov method. The current fluctuation relative percentage is less than ±8% and heating rate is up to 6.32 ℃/min when the proposed controller is used. The experiment results show that the proposed control scheme is better than inverse neural network controller （INNC） and PID controller （PIDC）.

OUTPUT REGULATION PROBLEM FOR A CLASS OF SISO INFINITE DIMENSIONAL SYSTEMS VIA A FINITE DIMENSIONAL DYNAMIC CONTROL

This paper deals with the output regulation problem for a class of SISO infinite dimensional systems with an uncertain exosystem.For these systems,a concept of relative degree is firstly introduced and used to construct a transformation which leads to the canonical form of output feedback systems.Then,based on this canonical form,by means of an internal model and a recursive adaptive control,the authors obtain an adaptive regulator which solves the problem.It should be pointed out that the proposed regulator is finite dimensional while it is usually infinite dimensional in existing literatures.