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Boosted Flight Controller for Quadrotor Navigation under disturbances
Abstract
Lightweight Unmanned Aerial Vehicles (UAV) are usually very sensitive to the external disturbances during outdoor experimentations. These adverse conditions make both the dynamic modeling and the control tasks more complex. Thus, it is necessary to employ an efficient control technique with acceptable performance without a complete knowledge of the disturbed model. This is because, in the classic feedback linearization control, the deviations between the model and the real plant may produce poor performance. Throughout the present paper, Dynamic Sliding Mode Control (DSMC) technique is designed to deal with the disturbances and which has never been used for quadrotors. Unlike the existing sliding mode techniques, the designed one uses an input-dependent sliding surface in order to enhance the robustness level of the classical feedback linearization control law. The effectiveness of this approach that ensures 3D trajectory tracking of quadrotor is demonstrated through numerical simulations.
... Having the aerial vehicle exposed to those severe disturbances makes finding the optimum design, the navigation control, and the dynamic modeling very complicated. The outdoor experiment and exposure of the aerial vehicle to atmospheric disturbances like wind, hurricanes, and elevated heat affect the trajectory tracking of the autonomous system and can cause less stability if these issues have not been properly addressed [24]. Having a robust control system to navigate the UAV preciously, requires acquiring accurate flight data obtained from various implemented sensors. ...
... Then substituting (24) into (23) and considering the energy property of the Hamiltonian H ≤ 0 in order to make the Hamiltonian vanishes in time [25,26,66] yields: ...
Trajectory tracking of unmanned aerial vehicles (UAVs) has been studied nowadays because it is necessary to design new controllers under different conditions. Severe atmospheric conditions are one of the major problems to overcome according to the path of the UAV. Conditions such as wind speed that can vary according to the weather conditions can affect the flight performance and of course in extreme cases can lead to stability problems. Some kinds of severe conditions are storms, snow, and hurricanes. These conditions are considered as disturbances in the roll, pitch, and yaw orientations of the UAV. This topic has been studied since several decades ago for piloted aircraft, but it is very important that these new contributions be provided in this field of UAV considering plenty of new potential applications. In this chapter, fractional-order sliding mode techniques are provided for the trajectory tracking of UAVs under severe weather conditions. First, it is important to model the disturbances generated by severe atmospheric conditions at different altitudes, using weather data from a specific country or region. After disturbance modeling, the fractional-order sliding mode controller design is done by implementing a Lyapunov approach. Numerical experimental examples of the proposed control laws are tested for different trajectories under different weather conditions and altitudes.
A nonlinear proportional integral derivative (NLPID) controller is proposed to stabilize the translational and rotational motion of a six-degree of freedom (DOF) unmanned aerial vehicle (UAV) quadrotor system and enforce it to track a given trajectory with minimum energy and error. The complete nonlinear model of the 6-DOF quadrotor system is obtained using Euler–Newton formalism and used in the design process, taking into account the velocity and acceleration vectors, resulting in a more accurate 6-DOF quadrotor model and that more closely resembles the actual system. Six NLPID controllers are designed, each for roll, pitch, yaw, altitude, and the position subsystems, where their parameters are tuned using a genetic algorithm (GA) to minimize a multiobjective output performance index. The stability of the 6-DOF UAV subsystems has been analyzed in the sense of the Hurwitz stability theorem under certain conditions on the gains of the NLPID controllers. The simulations have been accomplished under the MATLAB®/Simulink environment and include three different trajectories, i.e., circular, helical, and square. The proposed NLPID controller for each of the six subsystems of the 6-DOF UAV quadrotor system has been compared with the linear PID one, and the simulations showed the effectiveness of the proposed NLPID controller in terms of speed, control energy, and steady state error.
This chapter presents the applications of an interval Type-2 (IT2) Takagi-Sugeno (T-S) fuzzy system for modeling and control the dynamics of a quad-copter unmanned aerial vehicle (UAV). In addition to being complex and non-linear, the dynamics of a quadcopter are under-actuated and uncertain, making the modeling and control tasks across its full flight envelope non-trivial. The popularity of fuzzy systems stems from the fact that they are a universal approximator, making them capable of explaining complex relations among variables in the form of fuzzy 'If-Then' rules. Addressing current research gaps, we performed a nonlinear system identification, leveraging the benefits of the T-S fuzzy system to model the attitude dynamics of a quadcopter drone. The data was collected from real-time flight tests in an indoor flight test facility, instrumented with a VICON motion capture system. We designed a robust interval Type-2 fuzzy logic controller (IT2FLC) for trajectory tracking and we improved the performance of the fixed IT2FLC by designing an adaptive control law, which was derived using the sliding mode control (SMC) theory. The efficacy of our fuzzy controller was investigated in the face of multiple external disturbances, where superior research outcomes were obtained compared to traditional methods.
Motion control design plays a crucial role in autonomous vehicles. Mainly, these systems operate in conditions of under-actuation, which make the control a serious task especially in presence of practical constraints. The main objective within this paper is to ensure the tracking of 3D reference trajectory overcoming some of the issues related to the control of multi-rotor vehicles (such as underactuation, robustness, limited power, accuracy, overshoot, etc.). Therefore, a control scheme for Vertical Take Off and Landing (VTOL) multi-rotor Unmanned Aerial Vehicle (UAV) is designed, applying the Interconnection and Damping Assignment-Passivity Based Control (IDA-PBC) technique. As reference model based technique, the control specifications are readily met by fixing a desired dynamic model, which is a major advantage of the technique. Moreover, a port −controlled Hamiltonian representation is exploited in order to point out the physical properties of the system such as its internal energy. This latter is exploited, as a fitness function for an optimization algorithm, in order to decrease the consumed energy especially at the take-off step and allows the tuning of the controller parameters. The numerical simulations have shown satisfactory results that support the claims using nominal system model or disturbed model. The designed controller has been implemented on a real vehicle for which one demonstrates, in an indoor area manipulation, the effectiveness of the proposed control strategy.
The model-free sliding mode control based on fractional order sliding surface is built upon: i) An absolutely
continuous control structure that does not require the exact dynamic model to induce a fractional sliding motion in finite time, and ii) A methodology to design fractional references with a clear counterpart in the frequency domain is proposed. This in order to improve the system response, in particular the transient period, and to generate a high-performance during the sliding motion. Numerical simulations support the proposal
and illustrates the closed-loop system, which provides a better insight of the proposed scheme.