In this paper, a new model-free adaptive integral sliding-mode-constrained control scheme is presented for autonomous four-wheeled mobile vehicle (4WMV) parking systems. The proposed control strategy includes: 1) online identification for the object model based on a data-driven technique is presented for 4WMV and 2) an integral sliding-mode controller with control input constraints and stability analysis is provided. For online data-driven model identification, a compact-form dynamic linearization-based observer formulation is constructed for 4WMV. For integral sliding-mode controller design, a dynamic antiwindup compensator is introduced to solve integral saturation and actuator saturation problems in the autonomous 4WMV parking system. The designed control scheme only utilizes the body angle and steering angle of the vehicle under the condition that the precise mechanism model of the autonomous 4WMV parking system is not available to us. Finally, simulation comparisons between the model-free adaptive integral sliding-mode-constrained control scheme, PID control, and model-free adaptive control algorithms with coordinate compensation are given for two different vehicles. The simulation results show that a better control performance is achieved with the proposed new sliding-mode controller than the traditional control method when actuator or control input saturation exists in the autonomous 4WMV parking system.

Model-Free Adaptive Discrete-Time Integral Sliding-Mode-Constrained-Control for Autonomous 4WMV Parking Systems

Due to the complex physical constraints in working space of robot, optimization for mobile robot operations satisfies not only one criterion but also several criteria. In this paper, a novel multi-objective method for optimal mobile robot path planning based on Whale optimization algorithm (WOA) is proposed. In the proposed method, two criteria of distance and smooth path of the robot path planning issue are transformed into a minimization one. The positions of the target and the obstacles in the environment are considered the fitness for the solution in WOA. The position of the globally best whale in each iteration is selected, and reached by the robot in sequence. In addition, the robot processor updates its information during the motion, and the environment is partially unknown for the robot due to the limit of the detection range of its sensors. Series of simulations are implemented in different static environments for the optimal path when the robot reaches its target. The results show that the proposed method provides the robot reaches its target with colliding free obstacles, and the proposed method may be the alternative method of optimization for robot planning.

A multi-objective optimal mobile robot path planning based on whale optimization algorithm

In the inevitable trends of the modern aerial equipment, unmanned aerial vehicle (UAV) is one of the most concerned components to research. This paper presents a saving memory optimization algorithm of Compact artificial bee colony (cABC) for UAVs route planning problem. In the proposed method, route length and danger exposure are modeled mathematically as the objective function, and the compact algorithm concept is implemented to accommodate the route planning situation. In the compact algorithm, actual design variable of solutions search space of artificial bee colony algorithm is replaced with a probabilistic representation of the population. A probabilistic representation random of the collection behavior of bees is inspired to employ for this proposed algorithm. The real population is replaced with the probability vector updated based on single competition. The computational results compared with other algorithms in the literature shows that the proposed method can provide the effective way of using a modest memory for UAV route planning problem.

An Unmanned Aerial Vehicle Optimal Route Planning Based on Compact Artificial Bee Colony

The paper addresses un-crewed aerial vehicles (UAV) route planning with its tracking trajectory of preventing obstacles by implementing a chaotic genetic algorithm (CGA). In complex environments, the dynamic 2D-grid model of low-altitude aircraft is discretized according to the constraints of 3D-space, divided into several 2D spaces. The improved CGA code is used to optimize the UAV's route planning phase for obstacles in space. The experimental results depict that the proposed scheme can effectively design an estimated optimal path and overcome route planning in the 3D space by converting engineering practicability spaces.

A Solution to UAV Route Planning Problem Based on Chaotic Genetic Algorithm

A new second-order joint attitude control method for Small Unmanned Aerial Vehicles (SUAVs) is presented in this paper. Based on lyapunov theory, a second-order backstepping control law is developed. An incremental control approach called Incremental Backstepping (IB) is used to increase the robust performance of the second-order control system. A new joint incremental backstepping attitude controller is proposed for SUAVs based on the movement equations. A prediction filter is added to enhance the accuracy of the sensors data and eliminate the time delay. The simulation results show the approach presented is valid.

Tingting Xie

Papers

A Joint Attitude Control Method for Small Unmanned Aerial Vehicles Based on Prediction Incremental Backstepping.