Feel lonely? What about reading books? Book is one of the greatest friends to accompany while in your lonely time. When you have no friends and activities somewhere and sometimes, reading book can be a great choice. This is not only for spending the time, it will increase the knowledge. Of course the b=benefits to take will relate to what kind of book that you are reading. And now, we will concern you to try reading modelling and control of robot manipulators as one of the reading material to finish quickly.

Modelling and Control of Robot Manipulators

The utilization of machine vision and its associated algorithms improves the efficiency, functionality, intelligence, and remote interactivity of harvesting robots in complex agricultural environments. Machine vision and its associated emerging technology promise huge potential in advanced agricultural applications. However, machine vision and its precise positioning still have many technical difficulties, making it difficult for most harvesting robots to achieve true commercial applications. This article reports the application and research progress of harvesting robots and vision technology in fruit picking. The potential applications of vision and quantitative methods of localization, target recognition, 3D reconstruction, and fault tolerance of complex agricultural environment are focused, and fault-tolerant technology designed for utilization with machine vision and robotic systems are also explored. The two main methods used in fruit recognition and localization are reviewed, including digital image processing technology and deep learning-based algorithms. The future challenges brought about by recognition and localization success rates are identified: target recognition in the presence of illumination changes and occlusion environments; target tracking in dynamic interference-laden environments, 3D target reconstruction, and fault tolerance of the vision system for agricultural robots. In the end, several open research problems specific to recognition and localization applications for fruit harvesting robots are mentioned, and the latest development and future development trends of machine vision are described.

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Recognition and Localization Methods for Vision-Based Fruit Picking Robots: A Review.

This paper presents a robust adaptive sliding mode control strategy using radial basis function (RBF) neural network (NN) for a class of time varying system in the presence of model uncertainties and external disturbance. Adaptive RBF neural network controller that can learn the unknown upper bound of model uncertainties and external disturbances is incorporated into the adaptive sliding mode control system in the same Lyapunov framework. The proposed adaptive sliding mode controller can on line update the estimates of system dynamics. The asymptotical stability of the closed-loop system, the convergence of the neural network weight-updating process, and the boundedness of the neural network weight estimation errors can be strictly guaranteed. Numerical simulation for a MEMS triaxial angular velocity sensor is investigated to verify the effectiveness of the proposed adaptive RBF sliding mode control scheme.

Adaptive sliding mode control of dynamic system using RBF neural network

The bilateral teleoperation system has raised expansive concern as its excellent behaviors in executing the tasks in the remote, unstructured, and dangerous areas via the cooperative operation systems. In this article, an radial basis function (RBF) neural network based adaptive robust control design is proposed for nonlinear bilateral teleoperation manipulators to cope with the main issues including the communication time delay, various nonlinearities, and uncertainties. Specifically, the slave environmental dynamics is modeled by a general RBF neural network, and its parameters are estimated and then transmitted for the environmental torque reconstruction in the master side. Since the parameters of the neural network (which are nonpower signals) are transmitted instead of the traditional environmental torque in the communication channel, the previous existing passivity problem under time delay is avoided. In both of master and slave sides, the trajectory creators are applied to generate the desired trajectories, and the RBF-neural-network-based adaptive robust controllers are designed subsequently to handle the nonlinearities and uncertainties. Theoretically, the proposed control algorithm can guarantee the global stability of bilateral teleoperation manipulators under time delay, and the good transparency performance with both position tracking and force feedback is also achieved simultaneously. The real platform comparative experiments are carried out, and the results show the good position tracking to execute precise operation and the good force feedback to detect the sudden disturbance in the environment dynamics.

RBF-Neural-Network-Based Adaptive Robust Control for Nonlinear Bilateral Teleoperation Manipulators With Uncertainty and Time Delay

Multilateral telerobotic system has potential applications in the industry environments with the advantages of cooperative manipulation for the remote and hazardous tasks, and its control design is quite challenging due to several coupling issues such as stability, position tracking, force feedback, and cooperative manipulation under time delays, various uncertainties, and external disturbance. In this paper, a novel radial basis function neural network (RBFNN) based adaptive sliding mode control design is proposed for nonlinear multilateral telerobotic system with n -master– n -slave manipulators. The environment force is modeled with a general form via the RBFNN-based environment parameters estimation in the slave side. The estimated environment parameters (nonpower signals) are transmitted to rebuild the environment dynamics in the master side and provide the good force feedback for the human operators. The RBFNN-based adaptive sliding mode controllers are designed separately for master and slave manipulators to achieve good position tracking under parameter variations and external disturbance. The coordinated force distribution algorithm is designed to achieve cooperative manipulation with the balance of force acting on the target object. The theoretical analysis is given and the comparative experiment for a nonlinear multilateral telerobotic system with 2-master–2-slave manipulators is implemented. The results show the good performance of our design.

RBFNN-Based Adaptive Sliding Mode Control Design for Delayed Nonlinear Multilateral Telerobotic System With Cooperative Manipulation

This paper proposes a novel approach for bilateral teleoperation systems with a multi degrees-of-freedom (DOF) nonlinear robotic system on the master and slave side with constant time delay in a communication channel. We extend the passivity based architecture to improve position and force tracking and consequently transparency in the face of offset in initial conditions, environmental contacts and unknown parameters such as friction coefficients. The proposed controller employs a stable neural network on each side to approximate unknown nonlinear functions in the robot dynamics, thereby overcoming some limitations of conventional controllers such as PD or adaptive controllers and guaranteeing good tracking performance. Moreover, we show that this new neural network controller preserves the control passivity of the system. Simulation results show that NN controller tracking performance is superior to that of conventional controllers.

Adaptive neural network control of bilateral teleoperation with constant time delay

This paper presents a novel teleoperation controller for a nonlinear master–slave robotic system with constant time delay in communication channel. The proposed controller enables the teleoperation system to compensate human and environmental disturbances, while achieving master and slave position coordination in both free motion and contact situation. The current work basically extends the passivity based architecture upon the earlier work of Lee and Spong (2006) [14] to improve position tracking and consequently transparency in the face of disturbances and environmental contacts. The proposed controller employs a PID controller in each side to overcome some limitations of a PD controller and guarantee an improved performance. Moreover, by using Fourier transform and Parseval’s identity in the frequency domain, we demonstrate that this new PID controller preserves the passivity of the system. Simulation and semi-experimental results show that the PID controller tracking performance is superior to that of the PD controller tracking performance in slave/environmental contacts.

Bilateral control of master-slave manipulators with constant time delay.

This paper proposed a novel frame work for bilateral teleoperation systems with a multi-degree-of-freedom (DOF) nonlinear robotic system in master and slave side with constant time delay in communication channel. We actually extend the passivity based architecture upon the earlier work of [6] to improve position tracking and consequently transparency in the face of environmental contacts. The proposed controller employs a PID controller in each side to overcome some limitation of PD controller and guarantee good performance. Besides, we show that this new PID controller preserve the control passivity of the system. Simulation and experimental results show that PID controller tracking performance is better than PD controller tracking performance in slave/ environment contacts.

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Bilateral control of master-slave manipulators with constant time delay

This study addresses the control problem of a teleoperation system with the slave flexible-link in the face of time delay, dynamic uncertainties, disturbances, and actuator faults. In the presence of actuator failures or when unknown disturbances act on the master and slave manipulators, the teleoperation system will be more likely to face the performance degradation and even instability. This paper proposes a simple proportional-derivative controller in conjunction with a disturbance observer and an auxiliary controller. Advantages of the proposed control law include its simple structure and no requirement for fault detection and isolation mechanism; thus, it is appropriate for implementation. Additive appealing features are that the controller can compensate dynamic uncertainties, disturbances, and actuator failures. Using a Lyapunov function, it is demonstrated that all the signals in the closed-loop system are ensured to be ultimately bounded. Simulation results are presented to verify the effectiveness of the proposed controller.

Fault-Tolerant Control of Teleoperation Systems with Flexible-Link Slave Robot and Disturbance Compensation

Forecasting the price of gold and its changes as an economic event has long been within the interest area of investors and financial analysts. This study aims to make gold -price forecast modelling using GMDH neural network and Multilayer Perceptron neural network (MLP), as well as determination of top model using performance evaluation criteria. Necessary data of the research was collected through Internet from Central Bank website (www.cbi.ir) and in particular from Novin Rah Avard [modern approach] software from 1386 [2007] till1391 [2012]. In this research, weekly means of eight effective indexes on gold price were selected for the mentioned interval. Good and acceptable results of performance evaluation criteria obtained from GMDH neural network indicate high capability of this network for identification of ruling patterns on data as well as unique characteristics of fast convergence, high accuracy, and approximation ability of strong function of the network confirming suitability of GMDH neural network for prediction of gold price.

Ahmad Forouzantabar

Papers

Adaptive neural network control of bilateral teleoperation with constant time delay

Bilateral control of master-slave manipulators with constant time delay.

Bilateral control of master-slave manipulators with constant time delay

Fault-Tolerant Control of Teleoperation Systems with Flexible-Link Slave Robot and Disturbance Compensation

Modelling and forecasting gold price using gmdh neural network