This paper investigates the robust finite-time rendezvous maneuver control for spacecraft via sliding mode control technology. Two control architectures are devised for realizing the control objective, where a novel-developed sliding mode surface (SMS) is designed by resorting to the hyperbolic tangent function. Without considering the chattering problem inherent in sliding mode control, a basis control scheme is constructed to force the tracking errors entering a compact set in finite time. To reduce the effect of the chattering phenomenon, a modified controller is established by resorting to the well-designed adaptive laws. Both of these two controllers can ensure finite-time convergence for the entire system. Theoretical analysis and numerical simulations have shown the effectiveness and superiorities of the proposed methods. INDEX TERMS Spacecraft rendezvous maneuver, finite-time control, sliding mode control, chattering problem, adaptive control.
An obstacle avoidance and path planning algorithm for a multi-joint manipulator in a space robot is presented in this paper. In this paper, the end-effector of the manipulator is used to capture some special target in a space environment with obstacles. To ensure the safety of the operation, a collisionfree path from the initial position to the target position is essential. Therefore, an obstacle avoidance and path planning algorithm based on the Rapidly-Exploring Random Tree (RRT) algorithm and the Forward and Backward Reaching Inverse Kinematics (FABRIK) algorithm is presented in this paper. First, a path planning algorithm based on the Rapidly-Exploring Random Tree (RRT) algorithm is designed for the multi-joint manipulator. Further, a method to generate a random point by artificial guidance is introduced for a higher searching speed. The RRT algorithm can effectively explore the entire workspace and find a feasible path without collision for the end-effector. To calculate the positions of each joint, the Forward and Backward Reaching Inverse Kinematics (FABRIK) algorithm is introduced and improved for the problem of inverse kinematics. The FABRIK algorithm avoids the use of rotational angles or matrices, and instead finds each joint position by locating a point on a line, and thus, it has a low computational cost. Therefore, the improved obstacle avoidance and path planning algorithm can quickly plan a feasible path for the multijoint manipulator in a space environment with obstacles. A numerical simulation is carried out to analyze the proposed obstacle avoidance and path planning method. It is observed that the method finds a feasible path without collision for the multi-joint manipulator with a low computational cost. These results validated the effectiveness of the proposed method for path planning to avoid the obstacles. INDEX TERMS FABRIK, multi-joint manipulator, obstacle avoidance, path planning, rapidly-exploring random tree.
A reliable nonlinear dynamic model of the quadrotor is presented. The nonlinear dynamic model includes actuator dynamic and aerodynamic effect. Since the rotors run near a constant hovering speed, the dynamic model is simplified at hovering operating point. Based on the simplified nonlinear dynamic model, the PID controllers with feedback linearization and feedforward control are proposed using the backstepping method. These controllers are used to control both the attitude and position of the quadrotor. A fully custom quadrotor is developed to verify the correctness of the dynamic model and control algorithms. The attitude of the quadrotor is measured by inertia measurement unit (IMU). The position of the quadrotor in a GPS-denied environment, especially indoor environment, is estimated from the downward camera and ultrasonic sensor measurements. The validity and effectiveness of the proposed dynamic model and control algorithms are demonstrated by experimental results. It is shown that the vehicle achieves robust vision-based hovering and moving target tracking control.
The mission route plays an essential role for the mission security and reliability of an unmanned system. This paper gives a route planning method for autonomous underwater vehicles (AUVs) based on the hybrid of particle swarm optimization (PSO) algorithm and radial basis function (RBF). In the improved PSO algorithm, metropolis criterion is used to prevent the improved PSO algorithm from falling into local optimum and RBF is used to smooth the path planned by PSO algorithm. Compared with classic PSO algorithm, the hybrid algorithm of PSO and RBF can avoid falling into the local optimum effectively and plan an anti-collision route. Moreover, based on the simulation results, it can be seen that the approach presented here is more efficient in convergence performance, and the planned route requires lower performance of AUVs.
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