A combination of fuzzy logic and neural network controller for multiple-input multiple-output systems

Generally, the difficulty of multiple-input multiple-output (MIMO) systems control is how to overcome the coupling effects between the degrees of freedom. Owing to the computational burden and dynamic uncertainty of MIMO systems, the model-based decoupling approach is not practical for real-time control. A hybrid fuzzy logic and neural network controller (HFNC) is proposed here to overcome this problem and to improve the control performance. Firstly, a traditional fuzzy controller (TFC) is designed from a single-input single-output (SISO) systems viewpoint for controlling the degrees of freedom of a MIMO system. Secondly, an appropriate coupling neural network controller is introduced into the TFC for compensating the system coupling effects. This control strategy not only can simplify the implementation problem of fuzzy control but also can improve the control performance. The state-space approach for fuzzy control systems stability analysis is employed to evaluate the stability and robustness of this intelligent hybrid controller. In addition, a dynamic absorber with a twolevel mass-spring-damper structure was designed and constructed to verify the stability and robustness of a HFNC by numerical simulation and to investigate the control performance by comparing the experimental results of the HFNC with that of a TFC for this MIMO system.     

基于模糊神经网络的冗余度变几何桁架机器人自适应控制

本文提出了一种基于模糊神经网络（FNN）的机器人位置自适应控制方法．利用模糊 神经网络模型来辨识冗余度变几何桁架机器人的逆动力学模型,用常规反馈控制器完成外部 干扰的补偿和闭环控制．并以四重四面体变几何桁架机器人为例进行仿真计算,表明该控制 方法具有良好的轨迹跟踪精度和抗干扰能力．     

基于RBF神经网络逆系统的机械手解耦控制策略

针对机械手系统具有非线性时变、多变量、强耦合的特点,提出一种基于RBF神经网络逆系统的机械手解耦控制策略。首先证明了系统的可逆性,进一步通过神经网络在线逆辨识建立机械手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机械手系统串联,构成伪线性复合系统,实现了将具有强耦合特性的多变量输入/输出机械手系统解耦成单个独立的伪线性对象。最后以两关节机械手为仿真对象进行了仿真,仿真结果验证了本方案的有效性和可行性。     

Nonlinear control with end-point acceleration feedback for a two-link flexible manipulator: Experimental results

Experimental results for end-point positioning of multi-link flexible manipulators through end-point acceleration feedback are presented in this article. The advocated controllers are implemented on a two-link flexible arm developed at the Control/Robotics Research Laboratory at Polytechnic University. The advocated approach in this article is based on a two-stage control design. The first stage is a nonlinear (1) feedback linearizing controller corresponding to the rigid body motion of the manipulator. Because this scheme does not utilize any feedback from the end-point motion, significant vibrations are induced at the end effector. To this effect, and to enhance the robustness of the closed-loop dynamics to parameter variations, the inner loop is augmented with an outer loop based on a linear output LQR design that utilizes an end-point acceleration feedback. The forearm of the manipulator is significantly more flexible as compared with the upper arm. Experimental and simulation results validate the fact that the end-effector performance is significantly better with the proposed (1) feedback linearizing control as compared with the linear independent joint PD control. In addition, the nonlinear control offers other advantages in terms of smaller and smoother actuator torques and reducing the effects of nonlinearities. Close conformation between simulation and experimental results validates the accuracy of the model.     

Observer-based adaptive control of robot manipulators: Fuzzy systems approach

This paper presents a fuzzy adaptive control suitable for motion control of multi-link robot manipulators with structured and unstructured uncertainties. When joint velocities are available, full state fuzzy adaptive feedback control is designed to ensure the stability of the closed loop dynamic. If the joint velocities are not measurable, an observer is introduced and an adaptive output feedback control is designed based on the estimated velocities. In the proposed control scheme, we need not derive the linear formulation of robot dynamic equation and tune the parameters. To reduce the number of fuzzy rules of the fuzzy controller, we consider the properties of robot dynamics and the decomposition of the uncertainties terms. The proposed controller is robust against uncertainties and external disturbance. Further, it is shown that required stability conditions, in both cases, can be formulated as LMI problems and solved using dedicated software. The validity of the control scheme is demonstrated by computer simulations on a two-link robot manipulator.     

Adaptive neural network output feedback control for flexible multi-link robotic manipulators

In this paper, a novel approach for adaptive control of flexible multi-link robots in the joint space is presented. The approach is valid for a class of highly uncertain systems with arbitrary but bounded dimension. The problem of trajectory tracking is solved through developing a stable inversion for robot dynamics using only joint angles measurement; then a linear dynamic compensator is utilised to stabilise the tracking error for the nominal system. Furthermore, a high gain observer is designed to provide an estimate for error dynamics. A linear in parameter neural network based adaptive signal is used to approximate and eliminate the effect of uncertainties due to link flexibilities and vibration modes on tracking performance, where the adaptation rule for the neural network weights is derived based on Lyapunov function. The stability and the ultimate boundedness of the error signals and closed-loop system is demonstrated through the Lyapunov stability theory. Computer simulations of the proposed robust controller are carried to validate on a two-link flexible planar manipulator.     

一种多变量模糊神经网络解耦控制器的设计

为提高多变量、非线性和强耦合系统的动态特性和解耦能力,根据解耦原理和神经网络思想,提出一种两级串联结构的自适应模糊神经网络解耦控制器.前级是基于智能权函数规则的自调整模糊控制器,后级是基于动态耦合特性的自适应神经网络解耦控制器.同时从理论上证明了学习算法的收敛性.仿真实例表明,所提出的解耦控制器具有良好的鲁棒性和自适应解耦能力,是解决多变量、非线性和强耦合问题的一种简便有效的控制算法.     

自适应神经模糊推理结合PID控制的并联机器人控制方法

针对6自由度液压驱动并联机器人的精确控制问题,提出一种结合自适应神经模糊推理系统(ANFIS)和比例积分微分(PID)控制的机器人控制方法。首先,利用浮动坐标系描述法(FFRF)来模拟机器人柔性组件,并构建并联机器人的拉格朗日动力学模型。然后,根据模糊推理中的模糊规则来自适应调整PID控制器参数。最后,利用神经自适应学习算法使模糊逻辑能计算隶属度函数参数,从而使模糊推理系统能追踪给定的输入和输出数据。将该控制器与传统PID控制器、模糊PID控制器进行比较,结果表明,ANFIS自整定PID控制器大大减小了末端器位移误差,能很好的控制并联机器人末端机械手的运动。     

Robust tracking control of a unicycle-type wheeled mobile manipulator using a hybrid sliding mode fuzzy neural network

This article presents a robust tracking controller for an uncertain mobile manipulator system. A rigid robotic arm is mounted on a wheeled mobile platform whose motion is subject to nonholonomic constraints. The sliding mode control (SMC) method is associated with the fuzzy neural network (FNN) to constitute a robust control scheme to cope with three types of system uncertainties; namely, external disturbances, modelling errors, and strong couplings in between the mobile platform and the onboard arm subsystems. All parameter adjustment rules for the proposed controller are derived from the Lyapunov theory such that the tracking error dynamics and the FNN weighting updates are ensured to be stable with uniform ultimate boundedness (UUB).     

Query based model learning and stable tracking of a robot arm using radial basis function network

In the context of a robot manipulator, a generalized neural emulator over the complete workspace is very difficult to obtain because of dimensionally insufficient training data. A query based learning algorithm is proposed in this paper that can generate new examples where control inputs are independent of states of the system. This algorithm is centered around the concept of network inversion using an extended Kalman filtering based algorithm. This is a novel idea since robot manipulator is an open loop unstable system and generation of control input independent of state is a research issue for neural model identification. Two trajectory independent stable control schemes have been designed using the neural emulator. One of the control schemes uses forward-inverse-modeling approach to update the controller parameters adaptively following Lyapunov function synthesis technique. The proposed scheme is trajectory independent unlike the back-propagation scheme. The second type of controller predicts the minimum variance estimate of control action using recall process (network inversion) and the control law is derived following a Lyapunov function synthesis approach so that the closed loop system consisting of controller and neural emulator remains stable. The simulation experiments show that the model validation approach is efficient and the proposed control schemes guarantee stable accurate tracking.     

Fuzzy self-adaptive radial basis function neural network-based control of a seven-link redundant industrial manipulator

This paper proposes a method for the identification of dynamics and control of a multi-link industrial robot manipulator using Runge-Kutta-Gill neural networks (RKGNNs). RKGNNs are used to identify an ordinary differential equation of the dynamics of the robot manipulator. A structured function neural network (NN) with sub-networks to represent the components of the dynamics is used in the RKGNNs. The sub-networks consist of shape adaptive radial basis function (RBF) NNs. An evolutionary algorithm is used to optimize the shape parameters and the weights of the RBFNNs. Due to the fact that the RKGNNs can accurately grasp the changing rates of the states, this method can effectively be used for long-term prediction of the states of the robot manipulator dynamics. Unlike in conventional methods, the proposed method can even be used without input torque information because a torque network is part of the functional network. This method can be proposed as an effective option for the dynamics identification of manipulators with high degrees-offreedom, as opposed to the derivation of dynamic equations and making additional hardware changes as in the case of statistical parameter identification such as linear least-squares method. Experiments were carried out using a seven-link industrial manipulator. The manipulator was controlled for a given trajectory, using adaptive fuzzy selection of nonlinear dynamic models identified previously. Promising experimental results are obtained to prove the ability of the proposed method in capturing nonlinear dynamics of a multi-link manipulator in an effective manner.     

Robust Control of a Two-Link Flexible Manipulator with Quasi-Static Deflection Compensation Using Neural Networks

A robust control method of a two-link flexible manipulator with neural networks based quasi-static distortion compensation is proposed and experimentally investigated. The dynamics equation of the flexible manipulator is divided into a slow subsystem and a fast subsystem based on the assumed mode method and singular perturbation theory. A decomposition based robust controller is proposed with respect to the slow subsystem, and H ^∞ control is applied to the fast subsystem. The overall closed-loop control is determined by the composite algorithm that combines the two control laws. Furthermore, a neural network compensation scheme is also integrated into the control system to compensate for quasi-static deflection. The proposed control method has been implemented on a two-link flexible manipulator for precise end-tip tracking control. Experimental results are presented in this paper along with concluding remarks.     

Interval type 2 hierarchical FNN with the H-infinity condition for MIMO non-affine systems

To develop a controller that deals with noise-corrupted training data and rule uncertainties for interconnected multi-input–multi-output (MIMO) non-affine nonlinear systems with unmeasured states, an interval type-2 fuzzy system is integrated with an observer-based hierarchical fuzzy neural controller (IT2HFNC) in this paper. Also, an H_∞ control technique and a strictly positive real Lyapunov (SPR-Lyapunov) design approach are employed for attenuating the influence of both external disturbances and fuzzy logic approximation error on the tracking of errors. Moreover, the proposed hierarchical fuzzy structure can greatly reduce the number of adjusted parameters of the IT2HFNC, and then, the problem of online computational burden can be solved. According to the design of the interval type-2 fuzzy neural network and H_∞ control technique, the IT2HFNN controller can improve its robustness to noise, uncertainties, approximation errors, and external disturbances. Simulation results are reported to show the performance of the proposed control system mode and algorithms.     

多变量模糊神经网络控制器的研究

提出一种MIMO系统的模糊神经网络控制器结构,阐述了基本设计思想和具体算法过程。应用实例仿真结果表明,它可用于控制强耦合带时延多变量系统,并使系统具有良好的动态和静态性能。     

Neural networks and fuzzy logic-based spark advance control of SI engines

In SI engines, spark advance (SA) needs to be controlled to get Maximum Brake Torque (MBT) timing. Spark advance can be controlled either by open loop or by closed loop controller. The open loop controller requires extensive testing and calibration of engine, to develop look up tables. In closed loop controller, empirical rules relating variables deduced from cylinder pressure are used. One of such empirical rules is to fix location of peak pressure (LPP) at a desired value of the crank angle. In the present work, a combined neural network and fuzzy logic-based control scheme is designed for SA control to get MBT timing. The fuzzy logic controller is designed to maintain LPP of SI engine close to 16° ATDC. The controller works in conjunction with Recurrent Neural Network model for cylinder pressure identification. LPP is estimated from cylinder pressure curve reconstructed using neural network model and is used as feedback signal to fuzzy logic controller. The simulations have been carried out to test the performance of the combined neural network and fuzzy logic-based control strategy. The simulation results show that the proposed strategy can quite satisfactorily control LPP to its desired value.     

Neural Network Identification Based Multivariable Feedback Linearization Robust Control for a Two-Link Manipulator

Regarding to the variations of the load and unmodeled dynamic, robot manipulators are known as a nonlinear dynamic system. Overcoming such problems like uncertainties and nonlinear characteristics in the model of two-link manipulator is the principal goal of this paper. To approach to this aim, a neural network is combined with a linear robust control in which the result has the advantages of, the first, approximated nonlinear elements and the second, the guaranteed robustness. To design the proposed controller, at first, multivariable feedback linearization is employed to convert the nonlinear model to linear one. Second, the unknown parameters of the system are identified by neural network based on a new proposed learning rule. Third, Mixed linear feedback-H_?∞? robust control method is proposed to stabilize the closed loop system. The closed loop system based on the proposed controller is analyzed and some numerical simulations are performed. Results show suitable responses of the closed loop system.     

Neural-network-based two-loop control of robotic manipulators including actuator dynamics in task space

A neural-network-based motion controller in task space is presented in this paper. The proposed controller is addressed as a two-loop cascade control scheme. The outer loop is given by kinematic control in the task space. It provides a joint velocity reference signal to the inner one. The inner loop implements a velocity servo loop at the robot joint level. A radial basis function network （RBFN） is integrated with proportional-integral （PI） control to construct a velocity tracking control scheme for the inner loop. Finally, a prototype technology based control system is designed for a robotic manipulator. The proposed control scheme is applied to the robotic manipulator. Experimental results confirm the validity of the proposed control scheme by comparing it with other control strategies.     

受时变约束柔性臂鲁棒RBF神经网络力/位置控制

研究了受时变约束的柔性臂系统，建立了分布参数模型，通过奇异摄动方法将该模型划分为表征系统刚性运动的集中参数子系统和表征系统振动的分布参数子系统．设计了集中参数子系统的鲁棒RBF神经网络力／位置控制算法和分布参数子系统的鲁棒自适应振动抑制控制算法．理论分析及仿真结果验证了该方法的有效性．     

捕获目标卫星后组合体航天器模糊神经网络滑模控制

探讨了漂浮基空间机械臂系统在轨捕获参数未知目标卫星后组合体航天器的镇定控制问题.首先在耦合空间机械臂系统捕获目标卫星操作过程动量、冲量的传递的基础上,建立了适用于漂浮基空间机械臂系统在轨捕获漂浮卫星控制系统设计的组合体航天器数学模型.利用该模型,设计了一种基于模糊高斯基神经网络的非奇异Terminal滑模控制算法.提出的控制算法不仅不要求系统动力学方程关于惯性参数呈线性函数关系,而且也不需要预知系统惯性参数;由于利用神经网络的自学习能力修正模糊控制的控制规则和隶属函数,这样在系统参数识别中,模糊神经网络可减少模糊规则数,更适应于空间机械臂系统在轨捕获的实际应用.最后通过仿真试验对比结果验证了所提出的控制算法的有效性.     

Neural network controllers for robot manipulators application of damping neurons

^_This paper presents an effective adaptive neural network feedback controller for force control of robot manipulators in an unknown environment by applying damping neurons which possess elastic-viscous properties. The unexpected overshooting and oscillation caused by the unknown and/or unmodeled dynamics of a robot manipulator and an environment can be decreased efficiently by the effect of the proposed damping neurons. Furthermore, a fuzzy controlled evaluation function is applied for the learning of the proposed neural network controller, so that the controller is able to adapt to the unknown environment more effectively. The effectiveness of the proposed neural network controller is evaluated by experiment with a 3 d.o.f. direct-drive planar robot manipulator.