Adaptive control of nonlinear PID-based analog neural networks for a nonholonomic mobile robot

An adaptive controller of nonlinear PID-based analog neural networks is developed for the velocity- and orientation-tracking control of a nonholonomic mobile robot. A superb mixture of a conventional PID controller and a neural network, which has powerful capability of continuously online learning, adaptation and tackling nonlinearity, brings us the novel nonlinear PID-based analog neural network controller. It is appropriate for a kind of plant with nonlinearity uncertainties and disturbances. Computer simulation for a differentially driven nonholonomic mobile robot is carried out in the velocity- and orientation-tracking control of the nonholonomic mobile robot. The effectiveness of the proposed control algorithm is demonstrated through the simulation experiment, which shows its superior performance and disturbance rejection.     

Adaptive control for robot manipulators using discrete timeidentification

An adaptive control scheme is proposed for rigid link robots where the control signal computations are performed continuously and the control coefficient computations are performed in discrete time. A global boundedness result is established for the resulting scheme, independent of the sampling rate. It is also shown that the position, velocity, and acceleration tracking errors are of the order of the sampling period. Furthermore, it is shown that, if the reference trajectory is persistently exciting (in a continuous-time sense), then, for a sufficiently fast sampling rate, the tracking errors decay to zero     

Tracking control of a nonholonomic mobile robot using compound cosine function neural networks

The purpose of this paper is to propose a compound cosine function neural network with continuous learning algorithm for the velocity and orientation angle tracking control of a nonholonomic mobile robot with nonlinear disturbances. Herein, two neural network (NN) controllers embedded in the closed-loop control system have the simple continuous learning and rapid convergence capability without the dynamics information of the mobile robot to realize the adaptive control of the mobile robot. The neuron function of the hidden layer in the three-layer feed-forward network structure is on the basis of combining a cosine function with a unipolar sigmoid function. The developed neural network controllers have simple algorithm and fast learning convergence because the weight values are only adjusted between the nodes in hidden layer and the output nodes, while the weight values between the input layer and the hidden layer are one, i.e. constant, without the weight adjustment. Therefore, the main advantages of this control system are the real-time control capability and the robustness by use of the proposed neural network controllers for a nonholonomic mobile robot with nonlinear disturbances. Through simulation experiments applied to the nonholonomic mobile robot with the nonlinear disturbances which are considered as dynamics uncertainty and external disturbances, the simulation results show that the proposed NN control system of nonholonomic mobile robots has real-time control capability, better robustness and higher control precision. The compound cosine function neural network provides us with a new way to solve tracking control problems for mobile robots.     

含驱动器动力学的移动机器人编队自适应控制

针对含有驱动器及编队动力学的多非完整移动机器人编队控制问题,基于领航者-跟随者[l-ψ]控制结构,通过反步法设计了一种将运动学控制器与驱动器输入电压控制器相结合的新型控制策略。采用径向基神经网络（RBFNN）对跟随者及领航者动力学非线性不确定部分进行在线估计,并通过自适应鲁棒控制器对神经网络建模误差进行补偿。该方法不但解决了移动机器人编队控制的参数与非参数不确定性问题,同时也确保了机器人编队在期望队形下对指定轨迹的跟踪;基于Lyapunov方法的设计过程,保证了控制系统的稳定与收敛;仿真结果表明了该方法的有效性。     

Adaptive dynamic surface control of flexible-joint robots using self-recurrent wavelet neural networks.

A new method for the robust control of flexible-joint (FJ) robots with model uncertainties in both robot dynamics and actuator dynamics is proposed. The proposed control system is a combination of the adaptive dynamic surface control (DSC) technique and the self-recurrent wavelet neural network (SRWNN). The adaptive DSC technique provides the ability to overcome the "explosion of complexity" problem in backstepping controllers. The SRWNNs are used to observe the arbitrary model uncertainties of FJ robots, and all their weights are trained online. From the Lyapunov stability analysis, their adaptation laws are induced, and the uniformly ultimately boundedness of all signals in a closed-loop adaptive system is proved. Finally, simulation results for a three-link FJ robot are utilized to validate the good position tracking performance and robustness against payload uncertainties and external disturbances of the proposed control system.     

Adaptive congestion control scheme in mobile ad-hoc networks

In the past, several authors have expressed their concerns over the poor congestion control in mobile wireless ad-hoc networks using traditional reference layer model. Many solutions were proposed to handle growing traffic and congestion in the network, using link layer information. Existing solutions have shown difficulties in dealing with congestion with varying packets drop. Moreover, ensuring the superior performance of congestion control schemes with traditional referenced layer model is a challenging issue, due to quick topology changes, dynamic wireless channel characteristics, link-layer contentions, etc. In this paper, we propose an effective cross-layer adaptive transmission method to handle the congestion in mobile wireless ad-hoc networks adequately. Simulation results exemplify the usefulness of the proposed method in handling congestion, and yields better results compared to existing approaches.     

Adaptive robust fuzzy control for path tracking of a wheeled mobile robot

This paper proposes an adaptive robust fuzzy control scheme for path tracking of a wheeled mobile robot with uncertainties. The robot dynamics including the actuator dynamics is considered in this work. The presented controller is composed of a fuzzy basis function network (FBFN) to approximate an unknown nonlinear function of the robot complete dynamics, an adaptive robust input to overcome the uncertainties, and a stabilizing control input. The stability and the convergence of the tracking errors are guaranteed using the Lyapunov stability theory. When the controller is designed, the different parameters for two actuator models in the dynamic equation are taken into account. The proposed control scheme does not require the accurate parameter values for the actuator parameters as well as the robot parameters. The validity and robustness of the proposed control scheme are demonstrated through computer simulations. This work was presented in part at the 13th International Symposium on Artificial Life and Robotics, Oita, Japan, January 31–February 2, 2008     

Control of a nonholonomic mobile robot using neural networks

A control structure that makes possible the integration of a kinematic controller and a neural network (NN) computed-torque controller for nonholonomic mobile robots is presented. A combined kinematic/torque control law is developed using backstepping and stability is guaranteed by Lyapunov theory. This control algorithm can be applied to the three basic nonholonomic navigation problems: tracking a reference trajectory, path following, and stabilization about a desired posture. Moreover, the NN controller proposed in this work can deal with unmodeled bounded disturbances and/or unstructured unmodeled dynamics in the vehicle. Online NN weight tuning algorithms do not require off-line learning yet guarantee small tracking errors and bounded control signals are utilized.     

Adaptive control for robot manipulators using multiple parameter models

In this paper, we propose multiple parameter models based adaptive switching control system for robot manipulators. We first uniformly distribute the parameter set into a finite number of smaller compact subsets. Then, distributed candidate controllers are designed for each of these smaller compact subsets. Using Lyapunov inequality, a candidate controller is identified from the finite set of distributed candidate controllers that best estimates the plant at each instant of time. The design reduced the observer-controller gains by reducing modeling errors and uncertainties via identifying an appropriate control/model via choosing largest guaranteed decrease in the value of the Lyapunov function energy function. Compared with CE based CAC design, the proposed design requires smaller observer-controller gains to ensure stability and tracking performance in the presence of large-scale modeling errors and disturbance uncertainties. In contrast with existing adaptive method, multiple model based distributed hybrid design can be used to reduce the energy consumption of the industrial robotic manipulator for large scale industrial automation by reducing actuator input energy. Finally, the proposed hybrid adaptive control design is experimentally tested on a 3-DOF Phantom^TM robot manipulator to demonstrate the theoretical development for real-time applications.

     

Adaptive TCP congestion control and routing schemes using cross-layer information for mobile ad hoc networks

Compared with traditional networks, ad hoc networks possess many unique characteristics. For example, ad hoc networks can drop a packet due to network events other than buffer overflow. Unfortunately, the current layered network architecture makes it impossible to pass the information specific to one layer to other layers. As a result, if a packet is lost due to reasons other than buffer overflow, TCP adversely invokes its congestion control procedure. Similarly, the routing algorithm may misinterpret that a path is broken and adversely invoke the route recovery procedure.This study addresses the limitations of the current layered network architecture by adopting a cross-layer protocol design for TCP and routing algorithms in ad hoc networks. The objective of this approach is to enable the lower-layered ad hoc network to detect and differentiate all possible network events, including disconnections, channel errors, buffer overflow, and link-layer contention, that may cause packet loss. Using the information exploited by lower layers, the upper layer-3 routing algorithm, and the layer-4 TCP can take various actions according to the types of network events. Simulation results demonstrate that the combination of the cross-layer optimized TCP and routing algorithms can effectively improve the performance of TCP and DSR, regardless of whether it is in a stationary or a mobile ad hoc network.     

Adaptive output feedback tracking control of a nonholonomic mobile robot

An adaptive output feedback tracking controller for nonholonomic mobile robots is proposed to guarantee that the tracking errors are confined to an arbitrarily small ball. The major difficulties are caused by simultaneous existence of nonholonomic constraints, unknown system parameters and a quadratic term of unmeasurable states in the mobile robot dynamic system as well as their couplings. To overcome these difficulties, we propose a new adaptive control scheme including designing a new adaptive state feedback controller and two high-gain observers to estimate the unknown linear and angular velocities respectively. It is shown that the closed loop adaptive system is stable and the tracking errors are guaranteed to be within the pre-specified bounds which can be arbitrarily small. Simulation results also verify the effectiveness of the proposed scheme.     

打滑状态下的多移动机器人编队自适应控制

本文针对由领航跟随控制策略协调运动的多移动机器人编队,研究跟随机器人存在打滑状态的自适应控制器设计问题.首先,通过移动机器人打滑状态的运动学特性分析,建立"距离–角度"编队控制模型.然后,利用径向基函数神经网络(RBF NN)对系统中由打滑引起的未知信息,构建非线性逼近器;并根据李雅普诺夫稳定性理论和非线性有界扰动稳定性理论,证明了设计的嵌入了RBF NN的自适应控制器能保证闭环控制系统状态的收敛和有界.通过分析编队误差控制模型,可将不打滑状态视为系统的一种特殊情况,而嵌入控制器中的RBF NN能自适应打滑和不打滑两种状态,从而使得控制器在两种状态下均有效.最后利用仿真研究,验证了本文所提方法的正确性和有效性.     

Adaptive motion control of wheeled mobile robot with unknown slippage

As a major representative nonholonomic system, wheeled mobile robot (WMR) is often used to travel across off-road environments that could be unstructured environments. Slippage often occurs when WMR moves in slopes or uneven terrain, and the slippage generates large accumulated position errors in the vehicle, compared with conventional wheeled mobile robots. An estimation of the wheel slip ratio is essential to improve the accuracy of locomotion control. In this paper, we propose an improved adaptive controller to allow WMR to track the desired trajectory under unknown longitudinal slip, where the stabilisation of the closed-loop tracking system is guaranteed by the Lyapunov theory. All system states use neural network online weight tuning algorithms, which ensure small tracking errors and no loss of stability in robot motion with bounded input signals. We demonstrate superior tracking results using the proposed control method in various Matlab simulations.     

Timing control of the mobile robot using tau-margin

Recently, autonomous robots which are designed on the basis of biological mechanism have attracted much attention. In this paper, we focus on the mechanism of timing control studied by ecological psychology, and apply the framework to timing control of a mobile robot. Experiments using real robots have been conducted and effective behaviors have been realized. This work was presented in part at the 13th International Symposium on Artificial Life and Robotics, Oita, Japan, January 31–February 2, 2008     

Nonlinear systems control using self-constructing wavelet networks

This paper describes a self-constructing wavelet network (SCWN) controller for nonlinear systems control. The proposed SCWN controller has a four-layer structure. We adopt the orthogonal wavelet functions as its node functions. An online learning algorithm, structure learning and parameter learning, allows the dynamic determining of the number of wavelet bases, and adjusting the shape of the wavelet bases and the connection weights. The SCWN controller is a highly autonomous system. Initially, there are no hidden nodes. They are created and begin to grow as learning proceeds. Computer simulations have been conducted to illustrate the performance and applicability of the proposed learning scheme.     

Adaptive robust control based on RBF neural networks for duct cleaning robot

International Journal of Control, Automation and Systems - In this paper, a control strategy for duct cleaning robot in the presence of uncertainties and various disturbances is proposed which...     

含有驱动器模型的移动机器人自适应跟踪控制

本文针对包含驱动器模型的移动机器人, 考虑到其在粗糙表面上运动过程中所受的摩擦力以及不可建模的动态的影响, 使用反步设计法(Backstepping)给出了一种自适应跟踪控制策略.其中对于不可建模的动态, 本文使用一种非线性函数对其影响进行抵消,使得机器人的路径跟踪对不确定具有鲁棒性; 对于摩擦力项, 使用径向基神经网络(RBFNN)对其进行逼近, 在控制器中能够根据逼近值给予相应的摩擦力补偿量, 从而使移动机器人比较适合在粗糙度大的路面(如沙地)上进行路径跟踪. 仿真结果验证了该控制方法的有效性.     

Navigation control for a mobile robot

This article describes a navigation method of a mobile robot which uses a single camera and a guide mark. A travel path is instructed to the robot by means of path drawn on a monitor screen. The image of the guide mark provides information regarding the robot's position and heading direction. The heading direction is adjusted while moving if any deviation from the specified path is detected. The proposed method has been implemented in a mobile robot which runs at the average speed of 2.5 ft/s. without deviating more than one foot from the specified path in an indoor environment. © 1994 John Wiley & Sons, Inc.     

自主移动机器人自适应室外道路检测

目的 为降低室外自主移动机器人视觉导航中遇到的阴影、裂纹及道路边界不规则造成的道路检测算法不鲁棒性,提出一种每帧灰度阈值可调的快速自适应道路检测方法。方法 先采用2维离散小波进行道路图像分解与重构,比较各级小波重构后的近似道路图像,确定出不影响“路-非路”灰度二分类的最佳分辨率等级;在低分辨率尺度空间中,用灰度类间最大方差和类内最小方差共同构造适应度函数,采用改进的遗传算法对各帧道路图像进行阈值自适应分割,找到准确的道路边界,最近两边界中心位置即机器人行驶方向。采用小型陆地自主车作为研究平台,并在卡耐基梅隆大学（CMU）提供的室外移动机器人道路视频中进行算法测试。结果 本文方法能够在具有阴影、裂纹、光照度变化的道路条件下鲁棒分割出道路边界,机器人可以平均30 km/h的速度在有较严重阴影干扰的校园道路上行驶,视觉系统的处理速度平均可达到20 ms/帧。结论 本文方法比传统的灰度直方图分割法表现出更强的环境自适应性,可实现较为鲁棒的室外道路检测,并可作为室外自主移动机器人非结构化道路检测的一种鲁棒性较强的方法加以推广。