基于T-S型的平面倒立摆双闭环模糊控制研究

针对多变量、非线性、强耦合的平面一级倒立摆系统,设计了双闭环模糊控制器.在系统数学模型的基础上,将平面一级倒立摆系统近似分解成2个非耦合的独立运动,然后利用极点配置的反馈控制方法,设计了2个模糊控制器,分别控制两个独立运动.解决了模糊控制规则数随输入变量数指数增加问题,降低了倒立摆控制系统设计的难度和复杂度.     

平面一级倒立摆的稳定控制

平面倒立摆系统是进行控制理论研究的理想实验平台.在对平面一级倒立摆进行运动学和动力学分析的基础上,采用拉格朗日方程建立了它的动力学模型.分别设计了LQR控制器和模糊控制器,应用所设计的控制器对倒立摆系统进行了实时控制实验.验证了两种控制方法的优缺点.     

平面二级倒立摆基于参数优化的模糊控制

对基于XY平台的平面二级倒立摆进行运动学和动力学分析,得到系统状态方程。通过构造综合误差E和综合误差变化率EC,减少输入变量维数设计模糊控制器,使模糊控制器的控制规则更为简单、有效,同时对量化因子参数进行优化。     

模糊规则控制一种绝对不稳定系统

本文设计了一个高精度、高分辨率的模糊控制器，并用以控制二阶倒立摆获得成功。提出了一种处理多变量系统的新观点，给出了模糊控制二阶倒立摆的控制规则；和一种强有力的清晰化方法，从而使模糊控制器的输出更加细腻，应用上述理论设计的高精度、高分辨率的模糊控制器，对二阶倒立摆进行实时控制获得成功。     

不确定平面二级倒立摆的鲁棒自适应控制

为提高倒立摆控制系统的抗扰动能力,降低其对未建模动态等的敏感度,研究了不确定平面二级倒立摆的鲁棒自适应控制器的设计方法。把倒立摆动力学模型分解为确定和不确定两部分,用一个非线性参数化模糊逻辑系统逼近平面二级倒立摆的不确定动态,采用李雅普诺夫稳定性理论推导出使平面二级倒立摆的状态误差渐近收敛的鲁棒控制器及自适应律。理论分析和仿真结果表明所提出的控制算法是有效的。     

基于变量分组模糊控制算法的倒立摆系统

采用模糊控制理论研究了直线一级倒立摆控制问题。直线一级倒立摆系统是多变量不稳系统,为了解决模糊规则爆炸问题,本文采用了变量分组的方法完成倒立摆模糊控制器的设计方案。要使直线一级倒立摆系统稳定,必须对小车位置和摆杆角度同时进行闭环控制,而单一的控制只能控制一个控制量,本文提出了两回路的模糊控制方案。仿真和实验结果证明了该方案的可行性和良好的控制性能。     

倒立摆的一种模糊控制方法

提出一种模糊控制方案,实现对倒立摆系统的平衡控制.针对倒立摆系统多变量的特性,采用双模糊控制器方案,分别对摆杆倾角和小车位移设计模糊控制器,大大降低了设计难度;为了实现对摆角和位移双重控制的功能,采用两个控制器轮流控制的策略,当摆角偏差或角速度值较大时,摆角控制器起作用,保持摆杆的垂直,反之,位移控制器起作用,调整小车位置不越界.在实际的物理设备上进行了实时控制实验,实验结果验证了方案的正确性和有效性.     

基于LQR三级倒立摆变论域自适应模糊控制

针对传统模糊控制的不足,以三级倒立摆为例,应用变论域自适应模糊控制理论,给出了三级倒立摆的数学模型,并验证了其可控性。考虑到三级倒立摆为多变量系统,为了解决模糊控制器规则组合爆炸问题,利用LQR理论先设计出状态反馈器,再进行降维处理。最后利用变论域自适应模糊控制理论给出伸缩因子,从而得到变论域自适应模糊控制器。仿真结果表明,该方法控制精度高,具有良好的稳定性和鲁棒性,可实现倒立摆系统的随动控制。     

平面倒立摆建模与最优控制算法的研究

倒立摆系统是一个多变量、非线性、高阶次、强耦合、欠驱动的自然不稳定系统,是自动控制理论教学和研究中典型的物理模型。本文以平面倒立摆系统为研究对象,使用拉格朗日方程建立了多级平面倒立摆的数学模型。采用线性二次最优控制算法,分别设计了LQR及LQR-模糊控制器,实现平面多级倒立摆的平衡控制,结论证明了本文设计的LQR控制器有很好的稳定性、鲁棒性和适应性。     

倒立摆的双闭环模糊控制

对倒立摆采用双闭环的模糊控制方案，内环控制倒立摆的角度，外环控制倒立摆的位置，两个模糊控制器的设计都很简单，执行时间很短。在实际倒立摆装置上的实验结果验证了该方案的可行性和良好的控制性能。     

Modeling Approach for Flexible Production Chain Operation

To meet the dynamically changing market requirements, production enterprises are collaborating in the form of production chains to share life cycle engineering data/knowledge as well as to improve production efficiency and product quality. This paper proposes a model-based control mechanism and intelligent control layer, which is used to control the operation of chained production enterprises. The control mechanism is achieved by defining control levels as mapped to production chain topology using the concept of operation isolation area. Production constraints and control rules are defined as attached to structure elements. The proposed control layer is used to decide the suitable operation of the selected production chain, in view of process constraints and operating conditions. A case study of a generic production chain with recycling is used to show the proposed control mechanism.     

基于C8051F920的超空泡通气系统压力控制研究

为了形成精确可控的超空泡形态,在超空泡技术研究中,需要精确控制多路气体流量.提出了以压力直接控制,实现流量间接控制的通气系统控制方案,建立了通气系统的数学模型.提出了通气系统的控制算法,对压力控制环和阀芯开度控制环分别采用比例积分算法和比例算法实现控制压力的目的.以C8051F920单片机为控制器核心,采用压力传感器作为传感元件,调节阀为执行元件,设计出一套压力控制的硬件和软件系统.仿真结果表明,所设计的控制系统鲁棒性好,响应速度快.系统为水下航行体超空泡形态研究提供了可靠的试验平台.     

Adaptive Kalman filter and dynamic recurrent neural networks-based control design of macro-micro manipulator

In this paper,a composite control scheme for macro-micro dual-drive positioning stage with high acceleration and high precision is proposed.The objective of control is to improve the precision by reducing the influence of system vibration and external noise.The positioning stage is composed of voice coil motor(VCM) as macro driver and piezoelectric actuator(PEA) as micro driver.The precision of the macro drive positioning stage is improved by the combined PID control with adaptive Kalman filter(AKF).AKF is used to compensate VCM vibration(as the virtual noise) and the external noise.The control scheme of the micro drive positioning stage is presented as the integrated one with PID and intelligent adaptive inverse control approach to compensate the positioning error caused by macro drive positioning stage.A dynamic recurrent neural networks(DRNN) based inverse control approach is proposed to offset the hysteresis nonlinearity of PEA.Simulations show the positioning precision of macro-micro dual-drive stage is clearly improved via the proposed control scheme.     

Robust learning control for robotic manipulators with an extension to a class of non-linear systems

A robust learning control (RLC) scheme is developed for robotic manipulators by a synthesis of learning control and robust control methods. The non-linear learning control strategy is applied directly to the structured system uncertainties that can be separated and expressed as products of unknown but repeatable (over iterations) state-independent time functions and known state-dependent functions. The non-linear uncertain terms in robotic dynamics such as centrifugal, Coriolis and gravitational forces belong to this category. For unstructured uncertainties which may have non-repeatable factors but are limited by a set of known bounding functions as the only a priori knowledge, e.g the frictions of a robotic manipulator, robust control strategies such as variable structure control strategy can be applied to ensure global asymptotic stability. By virtue of the learning and robust properties, the new control system can easily fulfil control objectives that are difficult for either learning control or variable structure control alone to achieve satisfactorily. The proposed RLC scheme is further shown to be applicable to certain classes of non-linear uncertain systems which include robotic dynamics as asubset. Various important properties concerning learning control, such as the need for a resetting condition and derivative signals, whether using iterative control mode or repetitive control mode, are also made clear in relation to different control objectives and plant dynamics.     

Integrated stability and traction control for electric vehicles using model predictive control

In this paper, an integrated vehicle and wheel stability control is developed and experimentally evaluated. The integrated structure provides a more accurate solution as the output of the stability controller is not altered by a separate unit, therefore its optimality is not compromised. Model predictive control is used to find the optimal control actions. The proposed control scheme can be applied to a wide variety of vehicle driveline and actuation configurations such as: four, front and rear wheel drive systems. Computer simulations as well as experiments are provided to show the effectiveness of the proposed control algorithm.     

电动滑板车智能红外遥控器的设计与实现

提出了一种新型电动滑板车的智能红外遥控器的设计与实现方法,该遥控器对滑板车的运行状态进行控制,实现滑板车的无极变速以及刹车等功能。设计中添加了MCU智能模块,加强了传输的抗干扰性,最大限度地降低了同频干扰的概率,实现了遥控器状态的最佳控制。给出了智能红外遥控器的详细设计与实现方法。实践表明:该遥控器性能稳定,抗干扰能力强,易于拓展新功能。     

Optimal Control Of Parallel Hybrid Electric Vehicles Based On Theory Of Switched System

The focus of this paper is the control strategy used to control general parallel hybrid electric vehicles (HEV). The torque split control problem of HEV is formulated as the optimal control of a switched system. A model‐based strategy for fuel‐optimal control is presented. The optimal control problem of such a switched system is formulated as a two‐stage optimization problem. Dynamic programming is utilized to determine the optimal control action that minimizes the cost function. Simulated results indicate that this method is effective.     

Multiobjective design and control optimization for minimum thermal postbuckling dynamic response and maximum buckling temperature of composite laminates

Design and control optimization is presented to minimize the thermal postbuckling dynamic response and to maximize the buckling temperature level of composite laminated plates subjected to thermal distribution varying linearly through the thickness and arbitrarily with respect to the in-plane coordinates. The total elastic energy of the laminates is taken as a measure of the dynamic response. The optimization control problem is solved under constraints on the laminate thickness and the control energy produced by a transverse dynamic load distributed over the upper surface of the laminate. The constrained control objective is expressed as the sum of the total elastic energy and penalty term involving the control force, which may be considered as a measure of the control energy. The thickness of layers and the fibers orientation angles are taken as optimization design variables. The design and control objectives are formulated based on shear deformation theory accounting for the von-Karman nonlinearity. The displacements are chosen as the sum of time-independent displacements due to the static thermal load and time-dependent displacements due to the initial disturbances and the applied control force. Liapunov–Bellman theory is used to obtain the optimal control force, buckled deflections and controlled elastic energy. Numerical examples are presented for angle-ply antisymmetric laminates with simply supported edges. Graphical studies are carried out to show the advantages of the present design and control procedures.     

新型动态矩阵控制与先进PID控制的对比研究

由于预测控制对模型的选择比较宽泛,可以选择非参数模型,如阶跃响应模型为预测模型.利用预测控制的该优点,论文基于多变量动态矩阵控制思想,建立了一种新型单元机组负荷控制系统.利用该控制系统对负荷控制进行控制仿真,将控制结果与优化的先进PID控制进行对比.对比结果表明,该控制系统负荷响应速度较快,压力波动较小,控制效果良好.