Obstacle avoidance is an important issue when a wheeled mobile redundant robot manipulator (WMRRM) completes given tasks in complex environment. In this article, a novel vector-based constrained obstacle avoidance (VOA) scheme is designed and analyzed for motion planning of the WMRRM. Specifically, the VOA scheme is first described as a quadratic programming (QP) problem subject to an equality constraint, an inequality constraint, and a bound constraint. Compared with the traditional obstacle avoidance scheme, the proposed VOA scheme can not only achieve subtasks, such as obstacle avoidance and physical limit avoidance when the robot completes the main end-effector task, but also greatly expands the range of feasible spaces. In addition, the VOA scheme is transformed into piecewise linear projection equations (PLPEs) and solved by a linear-variational-inequality-based primal-dual neural network (LVI-PDNN), which can efficiently obtain the optimal solutions to the VOA scheme. Computer simulations demonstrate the reliability, accuracy, and effectiveness of the proposed VOA scheme.

Dynamic (or say, time-varying) problems have been a hot spot of research recently. As a general form of matrix inverse, dynamic Moore-Penrose inverse solving has received more and more attentions owing to its broad applications. The approaches based on neural networks have become a popular solution to various dynamic matrix-related problems including dynamic Moore-Penrose inverse. However, existing neural models either only achieve infinite-time instead of finite-time convergence, or are sensitive to noises. Therefore, finite-time convergent neural model which is simultaneously capable of addressing the noises is desperately needed for dynamic Moore-Penrose inverse solving. To do that, in this paper, a novel evolution formula is designed based on the widely-investigated ZNN (Zhang neural network). Accordingly, two modified ZNN models (MZNN), namely MZNN-R and MZNN-L models, are proposed and analyzed for the right and left dynamic Moore-Penrose inversion of full-rank matrices, respectively. In addition to provide detailed theoretical analyses on the desired finite-time convergence and noise-depression properties of the proposed two models, we also perform two numerical examples for further verification. Furthermore, to illustrate the potential of MZNN models in practical applications, two path-tracking control examples are also presented via a 2D planar three-link and a 3D Kinova Jaco2 redundant robot manipulators. The feasibility, extraordinary efficacy and superiority of the proposed MZNN models for dynamic Moore-Penrose inverse solving are corroborated by both theoretical results and simulation observations.

Redundant robot manipulators are usually applied to various complex scenarios where harmful noise especially periodic calculating noise always exists. In order to avoid the task failure affected by the periodic noise, a circadian rhythms neural network (CRNN) is applied to solve motion planning problems of redundant robot manipulators suffering from the periodic noise. First, in this article, we formulate a tracking problem of redundant manipulators as a convex quadratic programming (QP) problem. Second, the QP problem is converted into a matrix equation. Third, based on the neural dynamic design method, a CRNN model is exploited and developed to solve the matrix equation of the tracking problem. Comparative simulations between the proposed CRNN and the traditional zeroing neural network show that the CRNN has better robustness and performance on solving the end-effector tracking task. Two physical experiments are conducted to further certify the effectiveness, robustness, and practicability of the proposed CRNN.

Via the Euler forward-difference rule, an Euler-type discrete-time circadian rhythms neural network model (E-DTCRNN) is proposed, developed, and investigated for motion planning of the redundant robot manipulator affected by periodic noises. In this article, an Euler-type discrete-time zeroing neural network model (E-DTZNN) is presented as comparison. The E-DTCRNN model is 0-stable, consistent, and convergent. In addition, through a hybrid torque and velocity optimization scheme synthesized by the proposed E-DTCRNN and the traditional E-DTZNN, a tracking trajectory is designed and applied to the motion planning of the redundant robot manipulator. Finally, groups of simulations and physical experiments verify the efficacy and noise suppression ability of the proposed E-DTCRNN model for motion planning of the manipulator.

Brain–computer interface (BCI) P300 speller can help severely disabled patients communicate and control with external machines or robots, so that the classification methods of P300 electroencephalogram (EEG) signal play an important role in the development of BCI system and technology. In this article, a novel support vector neural network (SVNN) is proposed and developed to obtain more accurate and effective EEG classification results. It is the first time to combine linear variational inequality based primal-dual neural network with convex quadratic programming problem based on support vector machine to solve the classification problem. It has been proved that the SVNN globally converges to the optimal solution of convex optimization problem and iterates the parameters in the form of matrix, which means that the method has global convergence and parallelism. The proposed SVNN method is used to solve the classification problem of P300 EEG signals. Experimental results on dataset IIb from BCI competition II and dataset II from BCI competition III show that the accuracy of the proposed SVNN method is 100% and 98%, respectively. Compared with most of the state-of-the-art algorithms, SVNN has the highest recognition accuracy and information transfer rate.