Stewart平台神经网络非奇异终端滑模控制

针对Stewart平台的六自由度（six degrees of freedom, 6-DOF）轨迹跟踪问题,提出一种基于神经网络的非奇异终端滑模控制方法并应用于Stewart平台的位置姿态控制中。通过分析Stewart平台的位置反解和速度反解,建立运动学方程,利用牛顿-欧拉方程建立动力学方程,并结合加速度反解得到了平台的状态空间表达式;基于非奇异滑模面函数,设计非奇异终端滑模控制律。考虑到径向基函数（radial Basis function, RBF）神经网络的逼近特性,采用RBF神经网络对模型未知部分进行自适应逼近,并利用Lyapunov第二法设计了自适应律;通过仿真证明控制器设计的有效性。仿真结果表明,相比于比例积分微分（proportional integral derivative,PID）控制器,提出的RBF神经网络非奇异终端滑模控制器具有更好的轨迹跟踪精度和动态特性。     

基于能量分析的桥式起重机防摆控制方法

针对欠驱动桥式起重机在自动化驾驶研究中负载升/落吊运动与台车水平位移联动时,负载摆动抑制效果和控制性能不能满足实际工程需要,易造成安全事故的问题,提出一种基于能量分析的桥式起重机联动系统非线性耦合防摆控制器.采用非线性耦合控制方法,构造新型储能函数,设计出非线性耦合防摆控制器.利用LaSalle不变性原理和Lyapunov方法对该闭环反馈系统稳定性进行严格的数学分析.理论推导、仿真与实验结果表明:相比于非线性跟踪控制器和局部反馈线性化控制器,所提非线性耦合防摆控制器具有更佳的控制性能,不仅提高了负载的吊运效率,而且能够有效抑制和快速消除负载摆角;在添加外部扰动的情况下,仍能取得良好的控制效果,具有较强的鲁棒性,为桥式起重机联动系统提供了一种新的防摆控制方法.     

基于滑模方法的桥式吊车系统的抗摆控制

针对桥式吊车这类欠驱动系统，提出一种基于滑模控制的抗摆方法．该方法将系统状态分成两组。构造出一种双层滑动平面．结合桥式吊车系统数学模型的特点。求取了总的滑模控制量并进一步设计了控制器的参数．采用Lyapunov方法，从理论上证明了各级滑动平面的稳定性．仿真结果验证了该方法对于桥武吊车系统抗摆控制的有效性．     

二阶动态滑模控制在移动机械臂输出跟踪中的应用

针对移动机械臂的输出跟踪问题,结合高阶滑模控制和动态滑模控制的设计思想为其设计了一种二阶动态滑模控制器.首先给出了包括驱动电机动态特性的移动机械臂的简化动态模型,然后通过微分同胚和输入变换将其分解为四个低阶子系统,并给出了其输出跟踪的二阶动态滑模控制器的设计方法.仿真结果表明,所设计的二阶动态滑模控制器不仅能很好地跟踪给定轨迹,而且能有效地削弱滑模控制系统的抖振.     

带输入饱和的双摆桥式起重机神经网络滑模控制

针对现代工业中输入饱和受限的双摆桥式起重机防摇摆控制问题,设计了一种基于神经网络的非奇异终端滑模控制器。首先,分析起重机的非线性动力学系统,并引入抗饱和模块将系统所需的控制力限制在驱动电机能提供的最大驱动力内;然后,采用部分状态信息反馈控制设计控制器,该控制器只需起重机小车位置、速度的反馈信息,无须实时测量吊重和摆角;之后,利用所提控制器跟踪经过规划的S形平滑函数,并用神经网络逼近起重机系统中复杂未知的非线性函数部分;最后,通过李雅普诺夫稳定性理论对系统状态的稳定性进行分析。仿真结果表明,所提控制器能在保证起重机小车准确定位的同时,有效抑制吊钩和重物的残余摆动,并且对外界干扰具有较强的鲁棒性。     

一类非线性仿射系统的滑模降阶控制器设计

针对一类非线性仿射系统的控制器设计问题,基于滑模变结构控制理论,提出一种新的控制器设计方法:滑模降阶方法.首先反复运用变结构控制理论对一类n阶的仿射非线性系统构造n-1个微分同胚变换函数和n-1个滑动流形,将初始系统降至一阶系统,并给出了变结构控制律;然后利用当前级与上一级控制输入的映射关系进行n-1次反推运算,即可得到初始系统的控制输入;最后通过仿真算例表明了所提出方法的有效性和可行性.     

复式套索人工肌肉驱动的下肢外骨骼的运动控制

设计了复式套索人工肌肉驱动的下肢外骨骼系统,并研究了外骨骼关节精密运动控制问题.首先,介绍了复式套索人工肌肉驱动的下肢外骨骼系统.其次,建立了复式套索人工肌肉的传递模型,并基于拉格朗日法建立了下肢外骨骼动力学模型.然后,基于该动力学模型提出了一种滑模控制方法.进行了滑模控制与比例-微分(PD)控制外骨骼膝关节运动的对比实验,证明了该滑模控制算法具有更高的运动控制精度.最后,完成了下肢外骨骼的被动训练实验,其关节角度跟踪误差低于3.78?.     

基于Lie理论对两轮不稳定小车的变结构控制

为了有效控制两轮不稳定小车,减少在控制过程中产生的抖振,首先利用一种非线性系统微分几何方法-Lie理论对两轮不稳定小车系统进行坐标和输入量的变换,实现了系统的局部反馈线性化,进而通过近似得到系统的线性模型.然后针对常规滑模变结构控制抖振较强的问题,设计了一种新的滑模控制方法,用动态滑模变结构控制.方法通过设计新的切换函数,使切换函数与系统控制输入的一阶或高阶导数有关,可将不连续项转移到控制的一阶或高阶导数中去,得到在时间上本质连续的动态滑模控制律,有效地降低了抖振.仿真和实验结果表明,采用的方法以及设计的控制器对不稳定小车的控制是有效的.     

基于函数滑模控制器的机械手轨迹跟踪控制

提出一种基于函数滑模控制器(FSMC)的控制策略,用于不确定机械手的轨迹跟踪控制。首先,由动力学模型和滑模函数得到系统的不确定项;然后,利用RBF神经网络逼近系统不确定项,由于神经网络逼近存在误差,而且在初始阶段误差较大,设计函数滑模控制器和鲁棒补偿项对神经网络逼近误差进行补偿,以克服普通滑模控制器容易引起的抖振问题,同时提高系统的跟踪控制性能。基于李亚普诺夫理论证明了闭环系统的全局稳定性,仿真实验也验证了方法的有效性。     

基于函数滑模控制器的机械手轨迹跟踪控制

提出一种基于函数滑模控制器(FSMC)的控制策略,用于不确定机械手的轨迹跟踪控制。首先,由动力学模型和滑模函数得到系统的不确定项;然后,利用RBF神经网络逼近系统不确定项,由于神经网络逼近存在误差,而且在初始阶段误差较大,设计函数滑模控制器和鲁棒补偿项对神经网络逼近误差进行补偿,以克服普通滑模控制器容易引起的抖振问题,同时提高系统的跟踪控制性能。基于李亚普诺夫理论证明了闭环系统的全局稳定性,仿真实验也验证了方法的有效性。     

Stable adaptive compensation with fuzzy CMAC for an overhead crane

In order to control mechanical systems, this paper proposes a novel fast control strategy. The controller includes a normal proportional and derivative (PD) regulator and a fuzzy cerebellar model articulation controller (CMAC). For an overhead crane, this control can realize both position tracking and anti-swing. Using a Lyapunov method and an input-to-state stability technique, the PD control with CMAC compensation is proven to be robustly stable with bounded uncertainties. Real-time experiments are presented comparing this new stable control strategy with regular crane controllers.     

Application of intelligent sliding mode control with moving sliding surface for overhead cranes

In this paper, neural-based fuzzy logic sliding mode control with moving sliding surface has been designed for supervision of an overhead crane. A mathematical model has been established of the crane, and equations of motion have been obtained. First, the suitable sliding surface coefficient has been determined for the fixed sliding surface in the design of sliding mode control. The sliding surface has been moved by using neural-based fuzzy logic algorithm to eliminate disadvantage of the regular sliding mode control. By application of this control algorithm, the control performance incredibly increased. In the application, during the carriage of the load to a target which was 1 m away by a crane with 3 kg of load and 100 cm of rope length, the parameters of effected controllers were updated and their training was realized. In order to display the insensitiveness of the controller to parametric uncertainty, the value of the load was taken as 8 kg and the length of the rope was taken as 3 m and controls for a different target were realized. MATLAB program was used for numerical solutions, and results were examined graphically. Obtained results displayed the success of the algorithm of neural-based fuzzy logic sliding mode control.     

An Efficient Adaptive Hierarchical Sliding Mode Control Strategy Using Neural Networks for 3D Overhead Cranes

Machine Intelligence Research - In this paper, a new adaptive hierarchical sliding mode control scheme for a 3D overhead crane system is proposed. A controller is first designed by the use of a...     

A New Adaptive Neuro-sliding Mode Control for Gantry Crane

This paper presents a new adaptive neuro-sliding mode control for gantry crane as varying rope length. This control method derived from combining the sliding surfaces of three subsystem of the gantry crane (trolley position, rope length, anti-swing) to draw out two system sliding surfaces: the trolley position with the anti-swing and the rope length and the anti-swing. On the based of the sliding mode control principle, drawn out the equivalent controller and the switching controller for gantry crane. But due to the uncertain parameters-nonlinear model of gantry crane with the bound disturbances, combining the neural approximate method, defined the neural controller and the compensation controller for the difference between the equivalent controller and the neural controller for two system control inputs: trolley position and rope length. The adaptive control laws for these controllers were deduced from Lyapunov’s stable criteria to asymptotically stabilize the sliding surfaces. Simulation studies are performed to illustrate the effectiveness of the proposed control.     

Robust Tracking Control For A Wheeled Mobile Manipulator With Dual Arms Using Hybrid Sliding‐Mode Neural Network

In this paper, a robust tracking controller is proposed for the trajectory tracking problem of a dual‐arm wheeled mobile manipulator subject to some modeling uncertainties and external disturbances. Based on backstepping techniques, the design procedure is divided into two levels. In the kinematic level, the auxiliary velocity commands for each subsystem are first presented. A sliding‐mode equivalent controller, composed of neural network control, robust scheme and proportional control, is constructed in the dynamic level to deal with the dynamic effect. To deal with inadequate modeling and parameter uncertainties, the neural network controller is used to mimic the sliding‐mode equivalent control law; the robust controller is designed to compensate for the approximation error and to incorporate the system dynamics into the sliding manifold. The proportional controller is added to improve the system's transient performance, which may be degraded by the neural network's random initialization. All the parameter adjustment rules for the proposed controller are derived from the Lyapunov stability theory and e‐modification such that uniform ultimate boundedness (UUB) can be assured. A comparative simulation study with different controllers is included to illustrate the effectiveness of the proposed method.     

基于目标的动态加权模糊控制

针对单输入、多目标控制系统，提出一种基于目标的动态加权模糊控制模型。该模型根据控制目标设计子模糊控制器，通过模糊推理得到的加权系数动态连接各个子目标模糊控制器，使多个子目标模糊控制器在控制过程中既相互竞争又相互协调。该模型用于吊车控制的仿真结果证明了其有效性。     

A nonlinear robust PI controller for an uncertain system

This paper presents a smooth control strategy for the regulation problem of an uncertain system, which assures uniform ultimate boundedness of the closed-loop system inside of the zero-state neighbourhood. This neighbourhood can be made arbitrarily small. To this end, a class of nonlinear proportional integral controllers or PI controllers was designed. The behaviour of this controller emulates very close a sliding mode controller. To accomplish this behaviour saturation functions were combined with traditional PI controller. The controller did not need a high-gain controller or a sliding mode controller to accomplish robustness against unmodelled persistent perturbations. The obtained closed-solution has a finite time of convergence in a small vicinity. The corresponding stability convergence analysis was done applying the traditional Lyapunov method. Numerical simulations were carried out to assess the effectiveness of the obtained controller.     

基于观测器的双吊具桥吊时变滑模同步控制

为针对双起升桥式吊车双吊具同步运行过程中普遍存在的无法精确建模、系统参数变化、外部扰动未知等问题,采用交叉耦合策略,提出了一种基于非线性扰动观测器的时变滑模同步控制方法。首先,采用时变滑模控制保证了控制器的全局鲁棒性;其次,利用非线性扰动观测器观测聚合扰动,对控制器进行扰动补偿;此外,提出一种可动态适应控制系统变化的变增益趋近律,有效抑制了控制输入抖振、缩短了趋近时间。最后,利用Lyapunov理论证明控制器的渐进稳定性,并通过仿真结果表明了所提出方法的有效性,控制器在未知扰动存在的情况下仍具有良好性能。     

Motion control of an omnidirectional mobile platform for trajectory tracking using an integral sliding mode controller

In this paper, a new tracking controller that integrates a kinematic controller (KC) with an integral sliding mode dynamic controller (ISMC) is designed for an omnidirectional mobile platform (OMP) to track a desired trajectory at a desired velocity. First, a posture tracking error vector is defined, and a kinematic controller (KC) is chosen to make the posture tracking error vector convergent to zero asymptotically. Second, an integral sliding surface vector is defined based on the angular velocity tracking error vector and its integral term. An integral sliding mode dynamic controller (ISMC) is designed to make the integral sliding surface vector and the angular velocity tracking error vector convergent to zero asymptotically. The above controllers are obtained based on the Lyapunov stability theory. To implement the designed tracking controller, a control system is developed based on PIC18F452. A scheme for measuring the posture tracking error vector using a camera sensor combined with an angular sensor is introduced. The simulation and experimental results are presented to illustrate the effectiveness and applicability of the proposed tracking controller.