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It has been a challenging task for a robotic arm to accurately reach and grasp objects, which has drawn much research attention. This article proposes a robotic hand–eye coordination system by simulating the human behavior pattern to achieve a fast and robust reaching ability. This is achieved by two neural-network-based controllers, including a rough reaching movement controller implemented by a pretrained radial basis function for rough reaching movements, and a correction movement controller built from a specifically designed brain emotional nesting network (BENN) for smooth correction movements. In particular, the proposed BENN is designed with high nonlinear mapping ability, with its adaptive laws derived from the Lyapunov stability theorem; from this, the robust tracking performance and accordingly the stability of the proposed control system are guaranteed by the utilization of the H∞ control approach. The proposed BENN is validated and evaluated by a chaos synchronization simulation, and the overall control system by object grasping tasks through a physical robotic arm in a real-world environment. The experimental results demonstrate the superiority of the proposed control system in reference to those with single neural networks. [ABSTRACT FROM AUTHOR]

Dynamic control, including robotic control, faces both the theoretical challenge of obtaining accurate system models and the practical difficulty of defining uncertain system bounds. To facilitate such challenges, this paper proposes a control system consisting of a novel type of fuzzy neural network and a robust compensator controller. The new fuzzy neural network is implemented by integrating a number of key components embedded in a Type-2 fuzzy cerebellar model articulation controller (CMAC) and a brain emotional learning controller (BELC) network, thereby mimicking an ideal sliding mode controller. The system inputs are fed into the neural network through a Type-2 fuzzy inference system (T2FIS), with the results subsequently piped into sensory and emotional channels which jointly produce the final outputs of the network. That is, the proposed network estimates the nonlinear equations representing the ideal sliding mode controllers using a powerful compensator controller with the support of T2FIS and BELC, guaranteeing robust tracking of the dynamics of the controlled systems. The adaptive dynamic tuning laws of the network are developed by exploiting the popular brain emotional learning rule and the Lyapunov function. The proposed system was applied to a robot manipulator and a mobile robot, demonstrating its efficacy and potential; and a comparative study with alternatives indicates a significant improvement by the proposed system in performing the intelligent dynamic control. [ABSTRACT FROM AUTHOR]

Fuzzy cerebellar model articulation controller (FCMAC) networks with excellent nonlinear appropriation ability and simple implementation are used to solve complex uncertainties problems in engineering applications. Both online and off-line learning algorithm of FCMAC networks usually applies the gradient-descent-type methods. However, such gradient-descent methods lead to the high possibility to converging into local minima. To cope with the local minimum problem, this paper alternatively proposes to apply harmony search algorithm to find optimal network parameters, so as to achieve better performances of FCMAC. The harmony search algorithm optimizes not only FCMAC network’s weight variables, but also optimizes network receptive field’s center position and standard deviation parameters. In order to obtain an optimal network, the weight values, center positions, and standard deviations are transformed to three data strings that can be processed by harmony search algorithm. In particular, the self-adaptive global best harmony search algorithm (SGHS) is used to search optimal parameter combinations of FCMAC within solution domains. The network’s performances are verified by approximating six nonlinear formulae. In order to compare the performances of the FCMAC networks optimized by the SGHS algorithm, a back-propagation trained network and another harmony search variant optimized networks are also tested in this work. The experimental results show that the networks optimized by SGHS perform the faster convergence speed and better accuracy. [ABSTRACT FROM AUTHOR]

States that the application of rare earth (RE) elements are able to increase yields of crops and stimulate some plants and animal growth. Details on the damage which can be caused to the body as a result of high levels of RE in foods consumed; Findings of experiments conducted.