Jing Wang

Bio: Jing Wang is an academic researcher from University of Science and Technology Beijing. The author has contributed to research in topics: Pendulum & Inverted pendulum. The author has an hindex of 3, co-authored 3 publications receiving 28 citations. Previous affiliations of Jing Wang include Bethune-Cookman University.

Cascaded control for balancing an inverted pendulum on a flying quadrotor

Abstract: This paper is focused on the flying inverted pendulum problem, i.e., how to balance a pendulum on a flying quadrotor. After analyzing the system dynamics, a three loop cascade control strategy is proposed based on active disturbance rejection control (ADRC). Both the pendulum balancing and the trajectory tracking of the flying quadrotor are implemented by using the proposed control strategy. A simulation platform of 3D mechanical systems is deployed to verify the control performance and robustness of the proposed strategy, including a comparison with a Linear Quadratic Controller (LQR). Finally, a real quadrotor is flying with a pendulum to demonstrate the proposed method that can keep the system at equilibrium and show strong robustness against disturbances.

An adaptive neural network approach to the tracking control of micro aerial vehicles in constrained space

Abstract: This paper presents an adaptive neural network approach to the trajectory tracking control of micro aerial vehicles especially when they are flying in a limited indoor area. Differing from conventional controllers, the proposed controller employs the outer position loop to directly generate angular velocity commands in the presence of unknown aerodynamics and disturbances and then the fast inner loop to handle the angular rate control. Adaptive neural networks are deployed to estimate all the uncertain factors with the adaptation law derived from the Lyapunov function. To achieve a real-time performance, a norm estimation approach of ideal weights is designed to achieve a high bandwidth and lighten the burden of computation burden. Meanwhile, a barrier Lyapunov function is introduced to guarantee the constraint of vehicle positions as well as the validity of the neural network estimation. Simulations and practical flight tests are conducted to verify the feasibility and effectiveness of the propos...

Target attraction-based ant colony algorithm for mobile robots in rescue missions

Abstract: After an earthquake, the road conditions are usually unknown and hazardous, which poses a great challenge for mobile robots to plan paths and reach the goal position safely for rescue operations. This paper presents a target attraction-based ant colony (TAAC) algorithm for the dynamic path planning of mobile robots operated in rescue missions. The global information of the road map is deployed to establish a target attraction function so that the probability of selecting an optimal path to the goal node is improved and the probability of converging to a local minimum path is reduced. Simulation results show that the proposed TAAC algorithm has a better dynamic performance and a faster convergence speed, compared with the existing max-min ant system algorithm.

A review: On path planning strategies for navigation of mobile robot

Abstract: This paper presents the rigorous study of mobile robot navigation techniques used so far. The step by step investigations of classical and reactive approaches are made here to understand the development of path planning strategies in various environmental conditions and to identify research gap. The classical approaches such as cell decomposition (CD), roadmap approach (RA), artificial potential field (APF); reactive approaches such as genetic algorithm (GA), fuzzy logic (FL), neural network (NN), firefly algorithm (FA), particle swarm optimization (PSO), ant colony optimization (ACO), bacterial foraging optimization (BFO), artificial bee colony (ABC), cuckoo search (CS), shuffled frog leaping algorithm (SFLA) and other miscellaneous algorithms (OMA) are considered for study. The navigation over static and dynamic condition is analyzed (for single and multiple robot systems) and it has been observed that the reactive approaches are more robust and perform well in all terrain when compared to classical approaches. It is also observed that the reactive approaches are used to improve the performance of the classical approaches as a hybrid algorithm. Hence, reactive approaches are more popular and widely used for path planning of mobile robot. The paper concludes with tabular data and charts comparing the frequency of individual navigational strategies which can be used for specific application in robotics.

Error-Constrained LOS Path Following of a Surface Vessel With Actuator Saturation and Faults

Abstract: This paper presents an error-constrained line-of-sight (ECLOS) path-following control method for a surface vessel subject to uncertainties, disturbances, and actuator saturation and faults. Based on a cascaded three degrees-of-freedom model of surface vessel, the backstepping technique is adopted as the main control framework. Error constraint of the vessel position is handled by integrating a novel tan-type barrier Lyapunov function. The proposed ECLOS method is in accordance with the classical line-of-sight method where no constraint is imposed. A nonlinear disturbance observer is developed to estimate the lumped disturbance that comprises the effects of parametric uncertainties, external environment disturbances, and actuator saturation and faults. It is proved that under the proposed control, the constrained requirements on the vessel position error are never violated and all closed-loop signals are uniformly ultimately bounded, regardless of fully actuated or under-actuated control configuration. Simulation results and comparisons illustrate the effectiveness and advantages of the proposed ECLOS path-following method.

Quadrotor trajectory tracking and obstacle avoidance by chaotic grey wolf optimization-based active disturbance rejection control

Abstract: In this paper, a new active disturbance rejection control (ADRC) scheme based on swarm intelligent method is proposed for quadrotors to achieve trajectory tracking and obstacle avoidance. First, the finite-time convergent extended state observer (FTCESO) is designed to enhance the performance of ADRC controller. Then, the chaotic grey wolf optimization (CGWO) algorithm is developed with chaotic initialization and chaotic search to obtain the optimal parameters of attitude and position controllers. Further, a novel virtual target guidance approach is proposed to achieve obstacle avoidance for quadrotors. Comparative simulations are presented to demonstrate the effectiveness and robustness of the CGWO-based ADRC scheme and the virtual target guidance approach.

Neural Network-based Robust Adaptive Certainty Equivalent Controller for Quadrotor UAV with Unknown Disturbances

Abstract: In this paper, a robust adaptive neural network certainty equivalent controller for a quadrotor unmanned aerial vehicle is proposed, which is applied in the outer loop for position control to directly generate the desired roll and pitch angles commands and then to the inner loop for attitude control. The newly proposed controller takes into account the vehicle’s kinematic and modelling error uncertainties which are associated with external disturbances, inertia, mass, and nonlinear aerodynamic forces and moments. The control method integrates an adaptive radial basis function neural networks to approximate the unknown nonlinear dynamics with certainty equivalent control technique, in this way leading to the fact that precise dynamic model and prior information of disturbances are not needed. The adaptation law was derived by using a Lyapunov theory to verify the stability and superiority of the new algorithms. The performance and effectiveness are also verified by carrying out several simulations. It was shown from the analysis that the altitude, position, and attitude tracking errors are converged to zero and the closed loop stability is guaranteed under extreme conditions.

Renyi’s Entropy Based Multilevel Thresholding Using a Novel Meta-Heuristics Algorithm

Abstract: Multi-level image thresholding is the most direct and effective method for image segmentation, which is a key step for image analysis and computer vision, however, as the number of threshold values increases, exhaustive search does not work efficiently and effectively and evolutionary algorithms often fall into a local optimal solution. In the paper, a meta-heuristics algorithm based on the breeding mechanism of Chinese hybrid rice is proposed to seek the optimal multi-level thresholds for image segmentation and Renyi’s entropy is utilized as the fitness function. Experiments have been run on four scanning electron microscope images of cement and four standard images, moreover, it is compared with other six classical and novel evolutionary algorithms: genetic algorithm, particle swarm optimization algorithm, differential evolution algorithm, ant lion optimization algorithm, whale optimization algorithm, and salp swarm algorithm. Meanwhile, some indicators, including the average fitness values, standard deviation, peak signal to noise ratio, and structural similarity index are used as evaluation criteria in the experiments. The experimental results show that the proposed method prevails over the other algorithms involved in the paper on most indicators and it can segment cement scanning electron microscope image effectively.