The ant colony optimization (ACO) is the most exceptionally fundamental swarm-based solver for realizing discrete problems. In order to make it also suitable for solving continuous problems, a variant of ACO (ACOR) has been proposed already. The deep-rooted ACO always stands out in the eyes of well-educated researchers as one of the best-designed metaheuristic ways for realizing the solutions to real-world problems. However, ACOR has some stochastic components that need to be further improved in terms of solution quality and convergence speed. Therefore, to effectively improve these aspects, this in-depth research introduced horizontal crossover search (HCS) and vertical crossover search (VCS) into the ACOR and improved the selection mechanism of the original ACOR to form an improved algorithm (CCACO) for the first time. In CCACO, the HCS is mainly intended to increase the convergence rate. Meanwhile, the VCS and the developed selection mechanism are mainly aimed at effectively improving the ability to avoid dwindling into local optimal (LO) and the convergence accuracy. To reach next-level strong results for image segmentation and better illustrate its effectiveness, we conducted a series of comparative experiments with 30 benchmark functions from IEEE CEC 2014. In the experiment, we compared the developed CCACO with well-known conventional algorithms and advanced ones. All experimental results also show that its convergence speed and solution quality are superior to other algorithms, and its ability to avoid dropping into local optimum (LO) is more reliable than that of its peers. Furthermore, to further illustrate its enhanced performance, we applied it to image segmentation based on multi-threshold image segmentation (MTIS) method with a non-local means 2D histogram and Kapur's entropy. In the experiment, it was compared with existing competitive algorithms at low and high threshold levels. The experimental results show that the proposed CCACO achieves excellent segmentation results at both low and high threshold levels. For any help and guidance regarding this research, readers, and industry activists can refer to the background info at http://aliasgharheidari.com/ .

Ant colony optimization with horizontal and vertical crossover search: Fundamental visions for multi-threshold image segmentation

Wind turbines are increasingly growing larger, becoming more complex, and being located in more remote locations, especially offshore. Interest in advanced controllers for normal operation has expanded in recent years, but fault detection and fault tolerant control for wind turbines is a less well-developed area of interest. In this benchmark challenge, we have reworked a previous challenge paper to present a more sophisticated wind turbine model - a modern 5 MW turbine implemented in the FAST software - and updated fault scenarios. These updates enhance the realism of the challenge and will therefore lead to solutions that are significantly more useful to the wind industry. This paper presents the challenge model and the requirements for challenge participants. In addition, it provides additional information about the faults selected for the challenge and their basis in field data.

Wind turbine fault detection and fault tolerant control

An adaptive neural sliding mode control with ESO for uncertain nonlinear systems is proposed to improve the stability of the control system. Any control system inevitably exists uncertain disturbances and nonlinearities which severely affect the control performance and stability. Neural network can be utilized to approximate the uncertain nonlinearities. Nevertheless, it produces approximate errors, which will become more difficult to deal with as the order of the system increases. Moreover, these errors and uncertain disturbances will result in a consequence that the control system can be unable to converge quickly, and has to deal with a lot of calculations. Therefore, in order to perfect the performance and stability of the control system, this paper combines sliding mode control and ESO, and designs an adaptive neural control method. The simulation results illustrate that the improved system has superior tracking performance and anti-interference ability.

An Adaptive Neural Sliding Mode Control with ESO for Uncertain Nonlinear Systems

This article deals with methods of navigation and mapping of mobile robots in an indoor environment, for example, laboratories, building corridors, and so on. It explains the proposed solution of g...

Development of simulation software for mobile robot path planning within multilayer map system based on metric and topological maps

In this paper, a fuzzy adaptive output feedback dynamic surface sliding-mode control scheme is presented for a class of quadrotor unmanned aerial vehicles (UAVs). The framework of the controller design process is divided into two stages: the attitude control process and the position control process. The main features of this work are (1) a nonlinear observer is employed to predict the motion velocities of the quadrotor UAV; therefore, only the position signals are needed for the position tracking controller design; (2) by using the minimum learning technology, there is only one parameter which needs to be updated online at each design step and the computational burden can be greatly reduced; (3) a performance function is introduced to transform the tracking error into a new variable which can make the tracking error of the system satisfy the prescribed performance indicators; (4) the sliding-mode surface is introduced in the process of the controller design, and the robustness of the system is improved. Stability analysis proved that all signals of the closed-loop system are uniformly ultimately bounded. The results of the hardware-in-the-loop simulation validate the effectiveness of the proposed control scheme.

Output Feedback Adaptive Dynamic Surface Sliding-Mode Control for Quadrotor UAVs with Tracking Error Constraints

The study of model predictive control algorithm based on the force/position control scheme of the 5-DOF redundant actuation parallel robot

This paper addresses the trajectory tracking and obstacle avoidance control problems for a class of mobile robot systems. Two classes of controllers are designed for the mobile robot system in the free motion, respectively. A new hybrid position virtual-force controller is designed to adjust the distance between the mobile robot and the obstacles. Since the uncertainties between the mobile robot dynamics model and obstacles degrade the performance of the obstacle avoidance system, a multi-layer feed-forward neural networks (NNs) deep learning method with hybrid position and virtual-force is proposed, such that the distance between the mobile robot and the obstacles converges to an adjustable bounded region. It is shown that the proposed controller in this paper is smooth, effective, and only uses the system output. The control design conditions are relaxed because of the developed multi-layer feed-forward NNs deep learning compensator. The simulation results and obstacle avoidance cases are performed to show the effectiveness of the proposed method.

Multi-layer Feed-forward Neural Network Deep Learning Control with Hybrid Position and Virtual-force Algorithm for Mobile Robot Obstacle Avoidance

Stability analysis and dynamic output feedback controller design of T–S fuzzy systems with time-varying delays and external disturbances

This paper addresses distributed formation control for a group of quadrotor unmanned aerial vehicles (UAVs) under Markovian switching topologies with partially unknown transition rates. Instead of the general stochastic topology, the graph is governed by a set of Markov chains to the edges, which can recover the traditional Markovian switching topologies in line with the practical communication network. Extended high gain observers (EHGOs) are constructed with a two-time-scale format to deal with the issue of nonlinear input coefficients, so that there could be a higher estimation precision of the system uncertainties. To impel multiple quadrotor UAVs to achieve a predesigned formation shape, a modified integral sliding mode (ISM) control protocol is proposed here with a multi-time-scale structure, which allows independent analysis of the dynamics in each time scale. The stability proof for the system state space origin is derived from the singular perturbation method and Lyapunov stability theory. In addition, the introduced ISM controller can deal with the time varying desired references with the bounded accelerations and is robust to the disturbances. Finally, simulations on six quadrotor UAVs are given to verify the effectiveness of the theoretical results.

Distributed formation control for multiple quadrotor UAVs under Markovian switching topologies with partially unknown transition rates

In this paper, an event-triggered integral sliding mode fixed-time control method for trajectory tracking problem of autonomous underwater vehicle (AUV) with disturbance is investigated. Initially,...

Hongbin Wang

Papers

The study of model predictive control algorithm based on the force/position control scheme of the 5-DOF redundant actuation parallel robot

Multi-layer Feed-forward Neural Network Deep Learning Control with Hybrid Position and Virtual-force Algorithm for Mobile Robot Obstacle Avoidance

Stability analysis and dynamic output feedback controller design of T–S fuzzy systems with time-varying delays and external disturbances

Distributed formation control for multiple quadrotor UAVs under Markovian switching topologies with partially unknown transition rates

Event-triggered integral sliding mode fixed time control for trajectory tracking of autonomous underwater vehicle: