Iterative Learning Consensus Control for Nonlinear Partial Difference Multiagent Systems with Time Delay

This paper considers the consensus control problem of nonlinear spatial-temporal hyperbolic partial difference multiagent systems and parabolic partial difference multiagent systems with time delay. Based on the system’s own fixed topology and the method of generating the desired trajectory by introducing virtual leader, using the consensus tracking error between the agent and the virtual leader agent and neighbor agents in the last iteration, an iterative learning algorithm is proposed. The sufficient condition for the system consensus error to converge along the iterative axis is given. When the iterative learning number k approaches infinity, the consensus error in the sense of the L2 norm between all agents in the system will converge to zero. Furthermore, simulation results illustrate the effectiveness of the algorithm

Three-Dimensional Path Planning Based on Six-Direction Search Scheme

In order to solve the problem of how to perform path planning for AUVs with multiple obstacles in a 3D underwater environment, this paper proposes a six-direction search scheme based on neural networks. In known environments with stationary obstacles, the obstacle energy is constructed based on a neural network and the path energy is introduced to avoid a too-long path being generated. Based on the weighted total energy of obstacle energy and path energy, a six-direction search scheme is designed here for path planning. To improve the efficiency of the six-direction search algorithm, two optimization methods are employed to reduce the number of iterations and total path search time. The first method involves adjusting the search step length dynamically, which helps to decrease the number of iterations needed for path planning. The second method involves reducing the number of path nodes, which can not only decrease the search time but also avoid premature convergence. By implementing these optimization methods, the performance of the six-direction search algorithm is enhanced in favor of path planning with multiple underwater obstacles reasonably. The simulation results validate the effectiveness and efficiency of the six-direction search scheme

Four-direction search scheme of path planning for mobile agents

This paper presents a neural network-based four-direction search scheme of path planning for mobile agents, given a known environmental map with stationary obstacles. Firstly, the map collision energy is modeled for all the obstacles based on neural network. Secondly, for the shorted path-search purpose, the path energy is considered. Thirdly, to decrease the path-search time, a variable step-length is designed with respect to collision energy of the previous iteration path. Simulation results demonstrate that the variable step-length is effective and can decrease the iteration time substantially. Lastly, experimental results show that the mobile agent tracks the generated path well. Both the simulation and experiment results substantiate the feasibility and realizability of the presented scheme

Bipartite Consensus of Heterogeneous Multiagent Systems with Diverse Input Delays

This paper investigates the bipartite consensus problem of heterogeneous multiagent systems with diverse input delays. Based on the systems composed of first-order and second-order agents, the novel control protocols are designed. Using frequency-domain analysis and matrix theory, the corresponding upper bounds of the allowable delays are obtained under the undirected topology and directed topology, respectively. Finally, simulation examples are given to verify the theoretical analysis

Fault-tolerant motion planning of redundant manipulator with initial position error

In the robotic manipulator operation practice, it is necessary to adjust the manipulator initial state to an accurate configuration for executing a given path tracking task. However, it is difficult to achieve a desired accurate configuration, which would lead to an unexpected initial position error of the end-effector. In this paper, based on a new neural-dynamic design method, i.e., Zhang dynamics, a fault-tolerant motion planning scheme is presented to diminish the initial position error arising in the manipulator state adjustment. Such a motion planning scheme of redundant manipulators can rapidly and smoothly diminish the initial position error during the task execution. Computer simulations are presented to illustrate the validity and advantages of the fault-tolerant motion planning scheme with an initial position error based on a four-link manipulator model