Xiaoming Sun

Bio: Xiaoming Sun is an academic researcher from Shanghai Ocean University. The author has contributed to research in topics: Lyapunov stability & Adaptive control. The author has an hindex of 1, co-authored 3 publications receiving 3 citations.

Adaptive operation-space control of redundant manipulators with joint limits avoidance

Abstract: In this paper, adaptive operation-space control with joint limits avoidance is proposed for a redundant robot manipulator. Utilizing redundant properties of the robotic manipulator, joint limits avoidance is achieved without interfering the main-task objective in the operation-space. Two control objectives are unified under one common control framework. To cope with the unknown and unstructured dynamic nonlinearities of the robot model, neural networks (NNs) are constructed under the proposed torque control design as to approximate the unknown dynamics and achieve small tracking errors. Simulation studies are carried out to verify the effectiveness of the proposed framework.

Adaptive region reaching control of fully actuated ocean surface vessels

Abstract: In this paper, region reaching controller is designed for fully actuated ocean surface vessels to reach a desired target region instead of a point. There are not the requirements for both the pre-s...

Neural network based adaptive region tracking control for a swarm of ships in constrained space

Abstract: Region tracking controllers are investigated for a swarm of ships which have the limited sensing range. In this control method, all ships can synchronously the moving desired area while avoiding obstacles on the track. In order to keep the inter-connection of dynamic interaction systems, barrier potential function is included which can approach infinity when the argument approaches special limits. Decentralized controllers are designed via function approximation technique, backstepping recursive design methodology, potential functions, and Lyapunov stability analysis theory. Moreover, we introduced the dynamic and constrained target zone to make this problem much more applicable for various situations. The simulated examples are represented to demonstrate the validity of the proposed algorithm.

A novel foraging algorithm for swarm robotics based on virtual pheromones and neural network

Abstract: Swarm robotics is an emerging interdisciplinary field that has many potential real-world applications. Swarm robotics aims to produce robust, scalable, and flexible self-organizing behaviors through local interactions from a large number of simple robots. In this paper, a novel pheromone model of swarm foraging behavior is developed based on a neural network. The output of a single neuron corresponds to the density of a pheromone, which diffuses to neighboring neurons through their local connections. A neural network is updated based on the proposed evaporation model. Neural networks can often mimic the dynamics and features of pheromones. Therefore, in this work, we develop an optimization method to determine the key parameters of cooperative foraging based on mathematical modeling. The differential equation variables represent the number of foraging robots assigned different tasks. The solutions of the differential equations represent the dynamics of the foraging behavior. The key parameters that affect task allocation are determined to make optimal decision rules. Simulation experiments are conducted under different foraging scenarios. The experimental results demonstrate the effectiveness of the proposed pheromone model.

Adaptive Impedance Control of Robots With Reference Trajectory Learning

Abstract: In this paper, we propose an adaptive impedance control with reference trajectory learning for the robots interacting with unknown environment. A cost function considering its tracking errors and interaction force is introduced and a reference trajectory learning law based on iterative learning is presented to minimize it. Also, an adaptive impedance control is designed to follow the target impedance model with the adaptive reference trajectory to implement the convergence of tracking errors and interaction force. Through simulation and experimental studies, we find that the robot can autonomously adjust its trajectory or interaction mode when the environment types or cost function parameters are tuned, so that a more humanized and intelligent interaction can be realized.

Tri-Criteria Optimization Motion Planning at Acceleration-Level of Dual Redundant Manipulators

Abstract: In order to solve joint-angle drift problem of dual redundant manipulators at acceleration-level, an acceleration-level tri-criteria optimization motion planning (ALTC-OMP) scheme is proposed, which combines the minimum acceleration norm, repetitive motion planning, and infinity-norm acceleration minimization solutions via weighting factor. This scheme can resolve the joint-angle drift problem of dual redundant manipulators which will arise in single criteria or bi-criteria scheme. In addition, the proposed scheme considers joint-velocity joint-acceleration physical limits. The proposed scheme can not only guarantee joint-velocity and joint-acceleration within their physical limits, but also ensure that final joint-velocity and joint-acceleration are near to zero. This scheme is realized by dual redundant manipulators which consist of left and right manipulators. In order to ensure the coordinated operation of manipulators, two motion planning problems are reformulated as two general quadratic program (QP) problems and further unified into one standard QP problem, which is solved by a simplified linear-variational-inequalities-based primal-dual neural network at the acceleration-level. Computer-simulation results based on dual PUMA560 redundant manipulators further demonstrate the effectiveness and feasibility of the proposed ALTC-OMP scheme to resolve joint-angle drift problem arising in the dual redundant manipulators.

Recurrent neural networks as kinematics estimator and controller for redundant manipulators subject to physical constraints

Abstract: Redundant manipulators could be efficient tools in industrial production as a result of their dexterity. However, existing kinematic control methods for redundant manipulators have two main disadvantages. On one hand, model uncertainties or unknown kinematic parameters may degrade the performance of existing model-based control methods subject to joint limits. On the other hand, existing model-free control methods ignore the existence of joint limits although they do not need to know kinematic models of redundant manipulators. In this paper, a quadratic programming (QP) scheme is elaborated to achieve the primary tracking control task of redundant manipulators as well as joint limits avoidance task. Besides, a gradient neurodynamics (GND) model is utilized to estimate the kinematics of redundant manipulators. Then, a primal dual neural network, which is employed to solve the QP problem, and the GND model are integrated towards developing a model-free control method constrained by joint angle and velocity limits for redundant manipulators. The visual sensory feedback is fed to the two neural networks. The efficacy of the proposed control method is demonstrated by extensive simulations and experiments, and the merits of the proposed method are also substantiated by comparisons.

Secure Adaptive Trajectory Tracking Control for Nonlinear Robot Systems Under Multiple Dynamic Obstacles: Safety Barrier Certificates

Abstract: This article studies the problem of obstacle-avoidance tracking control for a class of uncertain nonlinear robot systems in multiple-dynamic-obstacles environment. The main challenge focuses on how to simultaneously ensure tracking performance and obstacle avoidance. The existing collision-avoidance tracking control schemes cannot guarantee the tracking performance inside the obstacle detection region, since the use of additive Lyapunov-barrier function (LBF) generates the dynamic mismatching. To overcome this difficulty, a novel integral-multiplicative LBF is constructed. The adaptive mechanism is designed to compensate for the mismatching uncertainties. By incorporating the barrier function into backstepping procedure, a secure adaptive tracking control scheme is proposed. Compared with the existing results, the proposed control scheme can simultaneously achieve the obstacle avoidance and tracking performance regardless of being inside the obstacle sensing region and unknown nonlinear uncertainties.