基于参考模型的珩磨机往复位置自适应控制

通过简要介绍珩磨加工的基础上,建立其往复运动数学模型及其参考模型系统,然后利用MATLAB工具对控制系统进行仿真分析.结果表明采用参考模型自适应控制方法能够使珩磨头往复换向位置与参考模型一致,同时超调量也有所减小,能够很好地满足控制系统的性能指标,并具有一定的抗干扰能力.     

Sliding mode control with fuzzy adaptive perturbation compensator for 6-DOF parallel manipulator

This paper proposes a sliding mode controller with fuzzy adaptive perturbation compensator (FAPC) to get a good control performance and reduce the chatter. The proposed algorithm can reduce the chattering because the proposed fuzzy adaptive perturbation compensator compensates the perturbation terms. The compensator computes the control input for compensating unmodeled dynamic terms and disturbance by using the observer-based fuzzy adaptive network (FAN). The weighting parameters of the compensator are updated by on-line adaptive scheme in order to minimize the estimation error and the estimation velocity error of each actuator. Therefore, the combination of sliding mode control and fuzzy adaptive network gives the robust and intelligent routine to get a good control performance. To evaluate the control performance of the proposed approach, tracking control is experimentally carried out for the hydraulic motion platform which consists of a 6-DOF parallel manipulator.     

基于模糊控制的六面顶压机压力控制系统建模与仿真

将先进的电液比例控制技术与计算机控制技术相结合,对六面顶金刚石压机压力控制系统进行了设计,建立了该压力控制的数学模型.运用常规PID控制器的设计方法,设计出了模糊自适应PID控制器,并在MATLAB环境下进行仿真实验.结果表明,采用该控制算法,系统的调节时间缩短,响应速度加快,适应能力和鲁棒性得到提高,系统具有更好的动态特性和稳定性,有效地减少了压力的波动.     

A PID-type fuzzy controller model for machine control applications

This paper introduces a PID-like fuzzy controller model, which consists of only five fuzzy rules. Each fuzzy rule is designed to carry out a single control task. A self-learning procedure is used to determine the input scale factor for auto-tuning the system. A new performance criterion is suggested as an object function for the auto-tuning procedure. The features of this fuzzy controller, used in position control applications, are demonstrated by the simulation results of two different types of plants — a d.c. motor and an inverted pendulum.Nomenclature adjusting parameter of AIAE - C _I integral control constant in ruleR _I - C _L leading control constant in ruleR _zn andR _zp - C _P proportional control constant in ruleR _P andR _N - D _ZE domain of membership function _ZE - K _a adaptive adjusting constant ofC _L - K _H adjusting parameter for high-border of dynamic domain space - K _L adjusting parameter for low-border of dynamic domain space - P _R ramp input command - P _S step input command - S _de scaling factor for input variable de(change of error) - S _e scaling factor for input variablee(error) - S _o controller output scaling factor - U _max maximum output limit of fuzzy controller     

基于ADAMS模型的履带车辆半主动悬挂自适应控制

建立了某履带车辆ADAMS整车模型。搭建了控制算法MATLAB与实体模型ADAMS之间的联合仿真接口。提出了采用模糊控制器调整PID调节器参数实现基于ADAMS建模的整车半主动悬挂控制策略。用动行程及其变化率作为模糊控制器的输入,通过模糊控制器的输出动态调整PID调节器的参数,形成了车辆半主动悬挂自适应控制系统。仿真研究表明,该自适应控制系统能够有效协调加速度和动行程在不同频段的矛盾,明显改善履带车辆行驶的平顺性。     

数控凸轮磨床磨削力的模糊适应控制研究

以数控凸轮磨床的磨削过程为研究对象,建立了凸轮磨削过程的磨削力数学模型,研究了磨削力的间接检测和控制方法,并在此基础上提出一种基于模糊策略的适应控制方法对凸轮磨削过程的磨削力给予控制,采用MATALAB进行了控制器的设计和磨削加工的仿真验证,结果表明该方法能有效地解决凸轮磨削过程中的磨削力的波动问题,控制器具有良好的动态特性,实现了磨削过程中的最优金属切除率的目的,提高了凸轮磨削的表面质量。     

Adaptive backstepping fault-tolerant control for flexible spacecraft with unknown bounded disturbances and actuator failures

In this paper, a robust adaptive fault-tolerant control approach to attitude tracking of flexible spacecraft is proposed for use in situations when there are reaction wheel/actuator failures, persistent bounded disturbances and unknown inertia parameter uncertainties. The controller is designed based on an adaptive backstepping sliding mode control scheme, and a sufficient condition under which this control law can render the system semi-globally input-to-state stable is also provided such that the closed-loop system is robust with respect to any disturbance within a quantifiable restriction on the amplitude, as well as the set of initial conditions, if the control gains are designed appropriately. Moreover, in the design, the control law does not need a fault detection and isolation mechanism even if the failure time instants, patterns and values on actuator failures are also unknown for the designers, as motivated from a practical spacecraft control application. In addition to detailed derivations of the new controller design and a rigorous sketch of all the associated stability and attitude error convergence proofs, illustrative simulation results of an application to flexible spacecraft show that high precise attitude control and vibration suppression are successfully achieved using various scenarios of controlling effective failures.     

基于模糊控制的铁芯横剪线控制系统改造

原有的变压器铁芯横剪线控制系统由许多单片机、大量的数字和模拟电子元器件构成,系统的元器件老化严重,故障率非常高.运用欧姆龙CQM1H系列PLC取代原控制系统进行改造升级,并且运用PI控制对铁芯横剪线进行速度控制,引入模糊PI控制对其电流进行控制,使该设备能够恢复原有功能,正常自动运行,满足变压器厂的生产需要.现场运行表明:方案设计合理,系统运行稳定,提高了硅钢片的生产效率,降低了变压器的生产成本,具有一定的实际应用价值.     

基于输入整形技术的柔性关节机械手控制研究

柔性机械手具有双时标特性,现有控制策略中需要对电机转角及机械臂转角的状态反馈,增加了控制策略的复杂性.建立了柔性关节机械手动力学模型,针对目前控制策略存在的算法复杂问题,提出了基于输入整形技术的柔性关节机械手运动控制策略.以电机转角作为反馈,根据机械手末端输出与目标输入之间的传递函数,设计了脉冲序列,通过与目标轨迹的卷积进行整形,可以实现轨迹跟踪.通过仿真实例表明,通过对目标轨迹的整形,能够有效减小超调和残余振动,具有一定的可行性.     

基于模糊控制的电磁式助力转向控制系统

电磁式助力转向是汽车动力转向系统领域的一项新结构。电磁助力器工作电流的控制是电磁式助力控制系统软件设计的一个最基本的控制策略和方法。文中详细地介绍了电磁助力器工作电流模糊控制器的设计方法。文中也给出了方向盘把持力在阶跃输入下的时域响应的仿真结果．该结果表明电磁助力系统具有较好的响应特性。     

A fuzzy model based adaptive PID controller design for nonlinear and uncertain processes

We develop a novel adaptive tuning method for classical proportional–integral–derivative (PID) controller to control nonlinear processes to adjust PID gains, a problem which is very difficult to overcome in the classical PID controllers. By incorporating classical PID control, which is well-known in industry, to the control of nonlinear processes, we introduce a method which can readily be used by the industry. In this method, controller design does not require a first principal model of the process which is usually very difficult to obtain. Instead, it depends on a fuzzy process model which is constructed from the measured input–output data of the process. A soft limiter is used to impose industrial limits on the control input. The performance of the system is successfully tested on the bioreactor, a highly nonlinear process involving instabilities. Several tests showed the method's success in tracking, robustness to noise, and adaptation properties. We as well compared our system's performance to those of a plant with altered parameters with measurement noise, and obtained less ringing and better tracking. To conclude, we present a novel adaptive control method that is built upon the well-known PID architecture that successfully controls highly nonlinear industrial processes, even under conditions such as strong parameter variations, noise, and instabilities.     

Finite element based model predictive control for active vibration suppression of a one-link flexible manipulator

This paper presents a unique approach for active vibration control of a one-link flexible manipulator. The method combines a finite element model of the manipulator and an advanced model predictive controller to suppress vibration at its tip. This hybrid methodology improves significantly over the standard application of a predictive controller for vibration control. The finite element model used in place of standard modelling in the control algorithm provides a more accurate prediction of dynamic behavior, resulting in enhanced control. Closed loop control experiments were performed using the flexible manipulator, instrumented with strain gauges and piezoelectric actuators. In all instances, experimental and simulation results demonstrate that the finite element based predictive controller provides improved active vibration suppression in comparison with using a standard predictive control strategy.     

基于模糊神经网络的梭式窑自适应控制系统应用研究

针对梭式窑温度控制过程存在非线性、时变性和不确定性等情况,设计了基于模糊神经网络的自适应控制(MRAC)系统并运用于梭式窑中,即采用RBF径向基网络作为梭式窑温度对象的辨识网络,它产生的Jacobin信息与控制误差一起提供给模糊神经网络控制器作为其学习信号,经过MATLAB仿真调整初始参数。最后运用VC++软件结合数据采集卡对燃气梭式窑进行控制。该控制系统能辨识出梭式窑时变性、非线性、长滞后性的对象特性,实时修正控制器参数,以适应对象特性的变化,在实际应用中取得了良好的控制效果。     

Adaptive fuzzy prescribed performance control for MIMO nonlinear systems with unknown control direction and unknown dead-zone inputs

In this study, an adaptive fuzzy prescribed performance control approach is developed for a class of uncertain multi-input and multi-output (MIMO) nonlinear systems with unknown control direction and unknown dead-zone inputs. The properties of symmetric matrix are exploited to design adaptive fuzzy prescribed performance controller, and a Nussbaum-type function is incorporated in the controller to estimate the unknown control direction. This method has two prominent advantages: it does not require the priori knowledge of control direction and only three parameters need to be updated on-line for this MIMO systems. It is proved that all the signals in the resulting closed-loop system are bounded and that the tracking errors converge to a small residual set with the prescribed performance bounds. The effectiveness of the proposed approach is validated by simulation results.     

复合模糊控制器在航空发动机上的应用研究

提出一种基于参考模型的复合模糊控制方法,它把主模糊控制器与基于参考模型的逆模糊控制器进行复合,构成复合模糊控制器,主、逆模糊控制器输出构成合成控制量。并把该方法用于航空发动机控制,通过让被控制量逼近参考模型性能,实现对低压转子转速的无静差控制与基本模糊控制相比,控制性能改善较大,供油量变化物理可实现。仿真结果证明了该方法的有效性。     

一种新的模糊神经网络及在化工过程控制系统的应用研究

本文提出一种新的采用补偿模糊算子的神经网络，它使控制系统具有更强的适应性和鲁棒性。该系统不仅能够自适应地调整隶属度函数。而且能够动态优化模糊规则，将其应用于化学反应器（CSTR）的控制中。仿真实验表明该网络的良好性能。     

Optimal actuator sizing and end point deformation for mobile robotic manipulators with elastic joint based on load criteria

Application of a numerical method to determine the maximum dynamic load carrying capacity (DLCC) for a robotic manipulator carrying an automobile petrol tank with joint elasticity subject to accuracy and actuator constraints is described in this paper. The maximum DLCC which can be achieved by a manipulator during a given trajectory is limited by a number of factors. The most important of which are the dynamic specification of the manipulator, the actuator limitations, and the elasticity of the joints such as reducers and servo drive system. Initially, the kinematic equations for carrying the automobile petrol tank by the robotic arm with 6 degrees of freedom (DOF) were calculated. The mechanical modeling was achieved via software programming. Dynamic modeling of the flexible joints manipulator was done simply for the three major axes. A method for determination of the dynamic load capacity with specific reference to both accuracy and actuator constraints is explained. The results obtained indicate the importance of both constraints and which constraint is more critical for accuracy and tracking.     

基于ANFIS及MPC的车辆转向换道控制系统设计

为提高智能车辆在高速工况下进行转向换道避撞时的行驶稳定性,设计了一种基于ANFIS及MPC的车辆转向换道控制系统。车辆转向换道控制系统是以模型预测控制(Model Predictive Control, MPC)算法为基础,结合五阶多项式换道路径和最小车距安全模型搭建的;以理想横摆角速度与实际横摆角速度的偏差及其变化率为双输入,利用自适应神经模糊系统(Adaptive Network-based Fuzzy Inference System, ANFIS)规则输出所需的附加横摆力矩,对车轮进行差动制动,以修正车身姿态,实现行车稳定。仿真结果对比表明,此车辆转向换道控制系统可显著提高车辆在高速工况下进行转向换道避撞时的行驶稳定性。     

Hybrid control of an active suspension system with full-car model using H∞ and nonlinear adaptive control methods

This paper presents hybrid control of an active suspension system with a full-car model by using H_∞ and nonlinear adaptive control methods. The full-car model has seven degrees of freedom including heaving, pitching and rolling motions. In the active suspension system, the controller shows good performance: small gains from the road disturbances to the heaving, pitching and rolling accelerations of the car body. Also the controlled system must be robust to system parameter variations. As the control method, H_∞ controller is designed so as to guarantee the robustness of a closed-loop system in the presence of uncertainties and disturbances. The system parameter variations are taken into account by multiplicative uncertainty model and the system robustness is guaranteed by small gain theorem. The active system with H_∞ controller can reduce the accelerations of the car body in the heaving, pitching and rolling directons. The nonlinearity of a hydraulic actuator is handled by nonlinear adaptive control based on the back-stepping method. The effectiveness of the controllers is verified through simulation results in both frequency and time domains.     

Model predictive control based on reduced order models applied to belt conveyor system

In the paper, a model predictive controller based on reduced order model is proposed to control belt conveyor system, which is an electro-mechanics complex system with long visco-elastic body. Firstly, in order to design low-degree controller, the balanced truncation method is used for belt conveyor model reduction. Secondly, MPC algorithm based on reduced order model for belt conveyor system is presented. Because of the error bound between the full-order model and reduced order model, two Kalman state estimators are applied in the control scheme to achieve better system performance. Finally, the simulation experiments are shown that balanced truncation method can significantly reduce the model order with high-accuracy and model predictive control based on reduced-model performs well in controlling the belt conveyor system.