Redundancy resolution is a critical problem in the control of parallel Stewart platform. The redundancy endows us with extra design degree to improve system performance. In this paper, the kinematic control problem of Stewart platforms is formulated to a constrained quadratic programming. The Karush-Kuhn-Tucker conditions of the problem is obtained by considering the problem in its dual space, and then a dynamic neural network is designed to solve the optimization problem recurrently. Theoretical analysis reveals the global convergence of the employed dynamic neural network to the optimal solution in terms of the defined criteria. Simulation results verify the effectiveness in the tracking control of the Stewart platform for dynamic motions.

No abstract

No abstract

No abstract

No abstract

Seaweb is an acoustic communication technology that enables communication between sensor nodes. Seaweb technology utilizes the commercially available telesonar modems that has developed link and network layer firmware to provide a robust undersea communication capability. Seaweb interconnects the underwater nodes through digital signal processing-based modem by using acoustic links between the neighboring sensors. In this paper, we design and investigate a global positioning system-free passive localization protocol by integrating the innovations of levelling and localization with the Seaweb technology. This protocol uses the range data and planar trigonometry principles to estimate the positions of the underwater sensor nodes. Moreover, for precise localization, we consider more realistic conditions namely, (a) small displacement of sensor nodes due to watch circles and (b) deployment of sensor nodes over non-uniform water surface. Once the nodes are localized, we divide the whole network field into circular levels and sectors to minimize the traffic complexity and thereby increases the lifetime of the sensor nodes in the network field. We then form the mesh network inside each of the sectors that increases the reliability. The algorithm is designed in such a way that it overcomes the ambiguous nodes errata and reflected paths and therefore makes the algorithm more robust. The synthetic network geometries are so designed which can evaluate the algorithm in the presence of perfect or imperfect ranges or in case of incomplete data. A comparative study is made with the existing algorithms which proves the efficiency of our newly proposed algorithm.

No abstract

Resource allocation problem in a wireless sensor network can be formulated as time-varying optimization, which can be further converted as time-varying linear equation system (TVLES). Hybrid multilayered time-varying linear equation system (HMTVLES) involving hybrid multilayers and time-variation characteristic is a complicated and challenging problem. Recently it has been solved by zeroing neural dynamics (ZND) method under ideal conditions, i.e., without noises. However, noises are ubiquitous, immanent and unavoidable in real-time systems. In this work, we propose a noise-tolerant zeroing neural dynamics (NTZND) model for solving HMTVLES. It can deal with different kinds of noises such as constant noise, linear-increasing noise, and random noise. Theoretical analyses guarantee the precision of NTZND model in the presence of different kinds of noises. In addition, a general NTZND model is proposed based on general activation function. Besides, classical ZND method and gradient neural dynamics (GND) method are also investigated and compared. Numerical experimental results are presented to verify the theoretical results of proposed models.