Unmanned Aerial Vehicle (UAV)-type Quadrotors are highly nonlinear systems that are difficult to control and stabilize outdoors, especially in a windy environment. Many algorithms have been proposed to solve the problem of trajectory tracking using UAVs. However, current control systems face significant hurdles, such as parameter uncertainties, modeling errors, and challenges in windy environments. Sensitivity to parameter variations may lead to performance degradation or instability. Modeling errors arise from simplifications, causing disparities between assumed and actual behavior. Classical controls may lack adaptability to dynamic changes, necessitating adaptive strategies. Limited robustness in handling uncertainties can result in suboptimal performance. Windy environments introduce disturbances, impacting system dynamics and precision. The complexity of wind modeling demands advanced estimation and compensation strategies. Tuning challenges may necessitate frequent adjustments, posing practical limitations. Researchers have explored advanced control paradigms, including robust, adaptive, and predictive control, aiming to enhance system performance amidst uncertainties in a scientifically rigorous manner. Our approach does not require knowledge of UAVs and noise models. Furthermore, the use of the Type-2 controller makes our approach robust in the face of uncertainties. The effectiveness of the proposed approach is clear from the obtained results. In this paper, robust and optimal controllers are proposed, validated, and compared on a quadrotor navigating an outdoor environment. First, a Type-2 Fuzzy Logic Controller (FLC) combined with a PID is compared to a Type-1 FLC and Backstepping controller. Second, a Genetic Algorithm (GA) is proposed to provide the optimal PID-Type-2 FLC tuning. The Backstepping, PID-Type-1 FLC, and PID-Type-2 FLC with GA optimization are validated and evaluated with real scenarios in a windy environment. Deep robustness analysis, including error modeling, parameter uncertainties, and actuator faults, is considered. The obtained results clearly show the robustness of the optimal PID-Type-2 FLC compared to the Backstepping and PID-Type-1 FLC controllers. These results are confirmed by the numerical index of each controller compared to the PID-type-2 FLC, with 12% for the Backstepping controller and 51% for the PID-Type-1 FLC.

In this paper, a full dynamic model for mobile robot with wheel slip is developed for robust navigation. The type 2 fuzzy logic controller algorithm is implemented and compared to the type 1 fuzzy controller using a differential drive wheels mobile robot (DDWMR) in the presence of wheel slipping from the perspective of control design. We present a dynamic model that explicitly relates perturbations to the vehicle slipping and parameters uncertainties. Both controllers are validated on different behaviors of navigation to evaluate their performances. Furthermore, many advanced robustness tests (modeling errors, localization uncertainties, loss of efficiency of motors, sliding ground) are established to compare the efficiency of each controller. Type 1 and interval type 2 fuzzy logic controllers are validated using V-REP robotic simulator on MATLAB® environment and real scenario are considered. The obtained results show clearly the performances of the IT2FLC comparing to the T1FLC controller especially when significant wheel slip and parameters uncertainty are considered.

Target tracking process consists of tracking and measuring a quantity over time, which is a difficult estimation problem. The latter gives erroneous measurements that are affected by the unknown number of targets to be tracked, as well as, measurements that are corrupted by noise. Thus, overcoming the target tracking problem includes basically solving the filtering problem which leads to a data association problem. Tracking problems that force lightly nonlinear models are solved appropriately with the Extended Kalman Filter. Although, problems that use nonlinear models, applying analytical expressions of the Jacobian of dynamic systems, or measurement vector functions are potentially solved by the Unscented Kalman Filter. The first technique is well built, but not precise, while the second technique yields very accurate results, but in terms of complexity, it is not suitable for real-time applications. Another filter called Particle Filter used a Monte Carlo method based on probability distribution over the state is proposed. Subsequently, we used a Gaussian Mixture Model which is based on the Expectation Maximization algorithm for fitting mixture-of-Gaussian models is considered to guarantee the estimation of optimal parameters set for each target. In order to achieve better trade between optimality and robustness of the tracking effect, the Smooth Variable Structure Filter is proposed as an alternative. This latter filter does not make any assumptions about noise characteristics and provides accurate estimation results. Furthermore, in this article, we propose a new target tracking algorithm based on the second form of Smooth Variable Structure Filter, which is implemented in a simulation using different scenarios. Experimental scenarios, under realistic conditions, are presented and the obtained results confirm the effectiveness of the Smooth Variable Structure Filter approach compared to other filters.

Simultaneous localization and mapping is very essential for autonomous navigation when the mobile robot is navigating in unknown environment without a global positioning system. Various techniques to solve the simultaneous localization and mapping problem have been extensively studied using the combination of low-cost sensors. Most of the work in mobile robotics still consists of finding solutions to problems in data exchange between mobile robot and communication control station, which is a challenging task. In fact, communication systems impose severe constraints in terms of channel capacity and transmission quality, because the transmission channel in communication systems is undergoing at the different physical phenomena like scattering, diffusion and diffraction, which occur interference and multiple path effects in wireless communications, while keeping these effects levels low. This article describes a simultaneous localization and mapping problem based on second-order smooth variable structure filter embedded in mobile robot equipped with a sensor for data wireless collection. The inclusion of the control in environments outside the mobile robot field of view can make the wireless communication simultaneous localization and mapping process much more difficult to find a solution under realistic conditions. In order to solve the simultaneous localization and mapping issue and to mitigate the fading phenomena, which affect the quality of service in advanced wireless communication systems, we use a new approach to combat the fading effect without requiring any statistical knowledge of the propagation channel parameters. Several experiments have been done in real-world applications, and good performances were obtained using a second-order smooth variable structure filter–simultaneous localization and mapping algorithm–based wireless communication.

Although nonlinear H∞ (NH∞) filters offer good performance without requiring assumptions concerning the characteristics of process and/or measurement noises, they still require additional tuning parameters that remain fixed and that need to be determined through trial and error. To address issues associated with NH∞ filters, a new SINS/GPS sensor fusion scheme known as the Fuzzy Adaptive Nonlinear H∞ (FANH∞) filter is proposed for the Unmanned Aerial Vehicle (UAV) localization problem. Based on a real-time Fuzzy Inference System (FIS), the FANH∞ filter continually adjusts the higher order of the Taylor development thorough adaptive bounds and adaptive disturbance attenuation , which significantly increases the UAV localization performance. The results obtained using the FANH∞ navigation filter are compared to the NH∞ navigation filter results and are validated using a 3D UAV flight scenario. The comparison proves the efficiency and robustness of the UAV localization process using the FANH∞ filter.