This paper addresses the problem of surrounding and tracking dynamic groups of moving targets with unknown velocities using a team of mobile robots. We introduce a control strategy for multi-robot systems that continuously maintains a safety distance between each robot and the convex hull of the target group. In particular, these safety distances are defined based on social constraints, and the proposed control strategy enforces them while simultaneously maintaining a balanced geometric distribution. The stability of the distribution is studied formally and the effectiveness of the proposed method is validated through numerical simulations and real-world tests using unicycle-model robots. The results show that the method provides suitable performance in dynamic scenarios, and it exhibits higher safety and adaptability than alternative approaches.

A Passivity-based Sliding Mode Controller (P-SMC) is designed in this paper to control a quadrotor (UAV) that tracks its trajectory using the Ant Colony Optimization algorithm. As a starter, feedback pacification is used to create a sliding surface, with passivity ensuring stability. A Conventional Sliding Mode method is also developed, the proposed control strategy has a faster reaching time, high accuracy, and reduces chattering. Additionally, the ant colony optimization algorithm (ACO) was used to ensure optimal performance, such as low energy consumption and small errors, finally, the validity of the proposed method is demonstrated by simulation results.

Quadrotor unmanned aerial vehicles (UAVs) have emerged as versatile platforms applicable to various fields such as surveillance, reconnaissance, and mapping. However, the limited energy capacity inherent in these vehicles poses a significant challenge. This limitation is a critical concern hindering their widespread adoption across diverse applications. Despite various proposed technological solutions addressing the energy consumption issue, the central challenge for the control community lies in developing controllers that ensure stability, robustness, and energy efficiency. In this paper, we present a passivity-based sliding mode controller (P-SMC) designed specifically for quadrotor UAVs. The suggested controller capitalizes on the robustness of the SMC and the energy efficiency of passivity-based control theory. Feedback passification is employed to create a sliding surface with passivity, ensuring stability. The inclusion of a noncontinuity term in the proposed P-SMC guarantees global asymptotic convergence to the sliding surface. To address the multi-faceted nature of the problem, a multi-objective optimization criterion is introduced. This criterion, which combines tracking error and control energy, is solved using the ant colony optimization (ACO) algorithm. The optimization process aims to identify control parameters that strike a balance between tracking accuracy and energy efficiency. Additionally, a conventional sliding mode method is developed to respond to conditions of attempted reach and sliding. Finally, the effectiveness of the proposed method is demonstrated through simulation results considering piecewise constant external disturbances. The outcomes based on the proposed control strategy indicate a notable reduction in energy consumption (ranging from 23% to 27%), faster reaching times (5-8 times), higher accuracy approximately (99%), and reduced chattering compared to the conventional sliding mode technique.

To boost the autonomy of Unmanned Aerial Vehicles (UAVs), this research focuses on creating a reliable solution to the Simultaneous Localization and Mapping (SLAM) problem. The idea of a novel filter-based on Smooth Variable Structure Filter (SVSF) as opposed to Extended Kalman Filter (EKF) to tackle the Inertial Navigation Systems (INS)/3D laser UAV navigation problem is the work’s original contribution. To show the benefits of hybrid SVSF localization over an EKF-based localization technique, simulation results for a 3D flight scenario are shown. The innovative form of SVSF is suggested as an alternative to improve the trade-off between robustness and optimality of UAV navigation. This second method yields reliable estimation results while making no assumptions about the noise properties. In addition, a new SLAM technique with high robustness against face parameter uncertainties and modeling errors is suggested in this study. The collected results support the efficiency of the SVSF strategy in comparison to EKF in experimental scenarios conducted under realistic conditions.

The Optimized controller is established for controlling the reconfigurable UAV, which has been designed to enable various morphologies whilst keeping the performance of a standard quadrotor. These design adjustments allow the quadrotor to negotiate restricted spaces horizontally or vertically while also altering its speed to the task and surroundings and enhancing energy autonomy. The method of Ant Colony-based Optimization determines the ISM controller gains and ensures optimal exchange between errors tracking and energy consumption.

The Optimized Multiple Inputs and Multiple Outputs Sliding Mode controller (MIMO-SMC) is intended to control the reconfigurable UAV, which can take on a variety of configurations while maintaining the efficiency of a conventional quadrotor in terms of hovering and precision handling. The Ant Colony optimization algorithm is used to calculate controller gains and to ensure optimal performance, such as small errors and lower energy consumption. The control goal is to allow the proposed UAV to track its trajectory for various configurations in the face of uncertain parameters to impose the position, altitude, and yaw angle while stabilizing its roll and pitch angles. Finally, simulation results are used to demonstrate the efficiency and performance of the proposed controller, and we calculate the energy consumption for each configuration.

In this paper, an optimized nonlinear robust Multi-Input Multi-Output (MIMO) Sliding Mode Controller is designed to control and stabilize a Quadrotor Unmanned Aerial Vehicle (UAV) in the presence of sensor noises. To achieve asymptotic linearisation of the nonlinear differential input-output model of the Quadrotor, the proposed method employs a novel sliding surface choice. The parameters of the proposed controller have been selected using the Ant Colony Optimization (ACO) algorithm considering a performance criteria in order to achieve a trade-off between the tracking error and the consumed energy. A generalized Lyapunov approach is used to evaluate the overall system's stability, which is a canonical state space with dynamic feedback. Simulation results are provided to validate the pro-posed controller and evaluate its performance.

: This paper propose a robust approach based on vision and sliding mode controller for searching and tracking an uncooperative and unidentified mobile ground target using a quadcopter UAV (QUAV). The proposed strategy is an Image-Based Visual Servoing (IBVS) approach using target’s visual data projected in a virtual camera combined with the information provided by the QUAV’s internal sensors. For an effective visual target searching, a circular search trajectory is followed, with a high altitude using the Camera Coverage Area (CCA). A Sliding Mode Controller (SMC) based on Exponential Reaching Law (ERL) is used to ensure the QUAV control in the presence of external disturbances and measurement uncertainties. Simulation results are presented to assess the proposer strategy considering different scenarios.

An adaptive finite-time robust sliding mode controller is presented in this work to regulate the trajectories of an upper limb exoskeleton rehabilitation robot. The goal is to build a controller that can handle constraints that alter the achievement of the rehabilitation process using the robot, such as modeling errors, uncertainties, and external disturbances. The suggested technique maintains the fundamental robustness of sliding mode control while also allowing for global finite-time convergence of the system's states. Furthermore, the presented controller is homogeneous and uses adaptive gains to eliminate requirements of preliminary knowledge on uncertainties and external disturbances bounds. The Lyapunov theory is used to determine stability. Simulations of passive trajectory tracking exercises are illustrated to check the efficacy of the developed control approach.

The study of control techniques for exoskeleton robots used in upper-extremity rehabilitation is gaining popularity. These robots are connected to the human upper limb at multiple points, allowing for smooth and independent joint movements. Therefore, providing a robust, precise, and safe control system is necessary. Sliding control-based approaches are well reputed for their robustness to parameter uncertainties, modeling errors, and external disturbances. Nevertheless, their major disadvantage is chattering. This paper describes two controllers based on sliding mode theory to reduce this undesirable effect: the generalized variable structure control and the higher-order finite-time sliding mode control. The former incorporates the derivative of the torque input in the model, while the latter is based on homogeneity and higher-order sliding modes to diminish chattering. A comparison between the two controllers is achieved by simulating passive rehabilitation mode.

This paper addresses the design of an adaptive sliding mode controller via geometric approach for a quadrotor unmanned aerial vehicle (UAV), suffering from inertial parameter uncertainty and disturbances. For this aim, the quadrotor dynamics described on the special Euclidean group SE(3) is decomposed into two cascaded subsystems, where the backstepping controller is developed during the position subsystem to obtain the necessary thrust magnitude and the reference orientation for the attitude subsystem. Then we develop a robust controller for the attitude subsystem via an exponential reaching law using the gain adaptation approach which has the advantage of robustness to bounded uncertainties/perturbations with unknown bounds in which the exponential coordinates are employed to parameterize the rotation matrix that pertains to a natural parameterization about the neighborhood of the identity. And this neighborhood covers all of SO(3) except a zero measure set. Finally, the simulation results are given in order to show the performances and the effectiveness of the proposed method.

Localisation technology is one of the most important challenges of underwater vehicle applications that accomplish any scheduled mission in the complex underwater environment. Currently, the Simultaneous Localisation and Mapping (SLAM) of the Autonomous Underwater Vehicle (AUV) is becoming a hotspot research. AUVs have, only recently, received more attention and underwater platforms continue to dominate the research. To ensure the success of an accurate AUV localisation mission, the problem of drift on the estimated trajectory must be overcome. In order to improve the positioning accuracy of the AUV localisation, a new filter referred to as the Adaptive Smooth Variable Structure Filter (ASVSF) based SLAM positioning algorithm is proposed. To verify the improvement of this filter, the combined SVSF and the Extended Kalman Filter (EKF) are presented. Experimental results based on dataset for underwater SLAM algorithm show the accuracy and stability of the ASVSF AUV localisation position. Several experiments were tested under real-life conditions with an autonomous underwater vehicle based on different filters. The results of these filters have been compared based on Root Mean Squared Error (RMSE) and in terms of localisation and map building errors. It is shown that the adaptive SVSF-SLAM strategy obtains the best performance compared to other algorithms.

Search and tracking of non-cooperative mobile targets, maneuvering in an area monitored by a quadrotor UAV is investigated. In order to reach this aim, We propose a robust approach based on vision and sliding mode controller. The proposed strategy is an Image-Based Visual Servoing (IBVS) approach using targets visual data projected in a virtual camera. For an effective visual target searching, a circular search trajectory is followed, with a high altitude using the Camera Coverage Area (CCA). A Sliding Mode Controller (SMC) based on Reaching Law with a Power Rate (RLPR) is applied to ensure the QUAV control in the presence of external disturbances and measurement uncertainties. Simulation results are presented to assess the proposed strategy considering different scenarios.

This paper presents the design of a robust architecture for the tracking of an unmanned ground vehicle (UGV) by an unmanned aerial vehicle (UAV). To enhance the robustness of the ground vehicle in the face of external disturbances and handle the non-linearities due to inputs saturation, an integral sliding mode controller was designed for the task of trajectory tracking. Stabilization of the aerial vehicle is achieved using an integral-backstepping solution. Estimation of the relative position between the two agents was solved using two approaches: the first solution (optimal) is based on a Kalman filter (KF) the second solution (robust) uses a smooth variable structure filter (SVSF). Simulations results, based on the full non-linear model of the two agents are presented in order to evaluate the performance and robustness of the proposed tracking architecture.

In this work, continuous third-order sliding mode controllers are presented to control a five degrees-of-freedom (5-DOF) exoskeleton robot. This latter is used in physiotherapy rehabilitation of upper extremities. The aspiration is to assist the movements of patients with severe motor limitations. The control objective is then to design adept controllers to follow desired trajectories smoothly and precisely. Accordingly, it is proposed, in this work, a class of homogeneous algorithms of sliding modes having finite-time convergence properties of the states. They provide continuous control signals and are robust regardless of non-modeled dynamics, uncertainties and external disturbances. A comparative study with a robust finite-time sliding mode controller proposed in literature is performed. Simulations are accomplished to investigate the efficacy of these algorithms and the obtained results are analyzed.

Unmanned aerial vehicles are used today in many real-world applications. In all these applications, the vehicle endurance (flight time) is an important constraint that affects mission success. This study investigates the limitations of embedded energy for a quadrotor aerial vehicle. We consider a quadrotor simple tasked to travel from an initial hover configuration to a final hover configuration. In order to have a precise approximation of the consumed energy, we propose a power consumption model with battery dynamic, motor dynamic, and rotor efficiency function. We then introduce an optimization algorithm to minimize the energy consumption during quadrotor aerial vehicle mission. The proposed algorithm is based on an optimal control problem formulated for the quadrotor model and solved using nonlinear programming. In the optimal control problem, we seek to find control inputs (rotor velocity) and vehicle trajectory between initial and final configurations that minimize the consumed energy during a point-to-point mission. We extensively test in simulation experiments the proposed algorithm under normal and windy weather conditions. We compare the proposed optimization method with a nonlinear adaptive control approach to highlight the saved amount of energy.

Quadrotor unmanned aerial vehicles have a limited quantity of embedded energy. To preserve and guaranty the success of the UAV mission, we should manage energy consumption during the mission. In this study, we introduce an optimization algorithm to minimize the consumed energy in the flying vehicle mission under windy conditions. In order to calculate the energy consumed by the quadrotor, we present a power loss model, where the energy is formulated as a function of rotor speed and acceleration. Then, we formulate the energy minimization problem as an optimal control problem and solve this problem in order to calculate minimum energy for a point-to-point quadrotor mission under windy conditions. In order to highlight the proposed optimization approach, we compare energy consumption obtained by optimization algorithm with an adaptive control approach in simulation experiment.

The paper deals with the water level control of a single tank process associated feedback linearization (FL) with a novel fractional order integral controller (FOIC) which is based on the Bode’s ideal transfer function. The first controller is used to cancel the nonlinearities of the single tank process and the latter used to solve the tracking problem. The new controller is implemented on a single tank process and the results are compared with a Linear Quadratic Regulator (LQR).

This paper focuses on developing a robust solution for the Simultaneous Localization and Mapping (SLAM) problem to increase the autonomy of Unmanned Aerial Vehicles (UAV). The investigated topics are related to data fusion, localization, features extraction and matching, map building, 3D pose estimation and SLAM. One important aspect of the autonomous navigation which should be investigated as well as the fusion of data from the different sensor. The data fusion algorithms are very important and their performances are closely dependent on both performances of the constructed map and the accuracy of the UAV position within this map. Optimal and robust filter using centred Gaussian noises is implemented. The original contribution of this work is the proposition and the adaptation of the Extended Kalman Filter (EKF) to solve the Inertial Navigation Systems INS/3D laser UAV navigation problem. Simulation results for 3D flight scenario are presented to demonstrate the advantages of the hybrid localization based on EKF compared with results of the INS relative localization based technique. Good results were obtained with the EKF using noise process and/or measurement noise characteristics, particularly in the case of centred Gaussian noises.

This paper presents the control of 3D crane system by using a decoupled adaptive neuro-fuzzy controller based on the sliding mode theory. The considered 3D crane involves a planar motion in conjunction with a hoisting motion. The control inputs are three (trolley and hoisting forces), whereas the variables to be controlled are five (the trolley position in the XOY plane, the length of the lifting cable, and the two angles of swing). The interactions between each control subsystem are not taken account explicitly, but are considered to be disturbances in control of each individual subsystem. In the proposed approach, a conventional controller (PD) is used in parallel with the neurofuzzy controller, the PD controller ensures the asymptotic stability in compact space, the parameter update rules of the fuzzy neural network are derived, and the proof of the online learning algorithm is verified by using the Lyapunov stability method. Experimental results are given to solve the crane position control problem of 3D crane system laboratory equipment.