Abstract
Most of the mechanical dynamic systems are subjected to parametric uncertainty, unmodeled dynamics, and undesired external vibrating disturbances while are motion controlled. In this regard, new adaptive and robust, advanced control theories have been developed to efficiently regulate the motion trajectories of these dynamic systems while dealing with several kinds of variable disturbances. In this work, a novel adaptive robust neural control design approach for efficient motion trajectory tracking control tasks for a considerably disturbed non-linear under-actuated quadrotor system is introduced. Self-adaptive disturbance signal modeling based on Taylor-series expansions to handle dynamic uncertainty is adopted. Dynamic compensators of planned motion tracking errors are then used for designing a baseline controller with adaptive capabilities provided by three layers B-spline artificial neural networks (Bs-ANN). In the presented adaptive robust control scheme, measurements of position signals are only required. Moreover, real-time accurate estimation of time-varying disturbances and time derivatives of error signals are unnecessary. Integral reconstructors of velocity error signals are properly integrated in the output error signal feedback control scheme. In addition, the appropriate combination of several mathematical tools, such as particle swarm optimization (PSO), Bézier polynomials, artificial neural networks, and Taylor-series expansions, are advantageously exploited in the proposed control design perspective. In this fashion, the present contribution introduces a new adaptive desired motion tracking control solution based on B-spline neural networks, along with dynamic tracking error compensators for quadrotor non-linear systems. Several numeric experiments were performed to assess and highlight the effectiveness of the adaptive robust motion tracking control for a quadrotor unmanned aerial vehicle while subjected to undesired vibrating disturbances. Experiments include important scenarios that commonly face the quadrotors as path and trajectory tracking, take-off and landing, variations of the quadrotor nominal mass and basic navigation. Obtained results evidence a satisfactory quadrotor motion control while acceptable attenuation levels of vibrating disturbances are exhibited.
Highlights
It is known that, in motion control systems, it is required that the system move to match some desired features of acceleration, velocity, position, or a combination of them.Unmanned aerial vehicles (UAVs) are dynamic systems where the controlled motion is fundamental to complete specific applications
In order to highlight the performance of the introduced novel adaptive robust control strategy, it is illustrated the applicability of offline training of
A novel adaptive robust neural motion control scheme for quadrotor systems has been introduced in this study
Summary
In motion control systems, it is required that the system move to match some desired features of acceleration, velocity, position, or a combination of them.Unmanned aerial vehicles (UAVs) are dynamic systems where the controlled motion is fundamental to complete specific applications. Diverse types of UAVs vehicles have been developed, with fixed-wing unmanned aerial vehicles (FW-UAVs) being the most common and most developed These aircraft are similar to passenger aircraft, with a pair of wings to provide lift, a propellant system to provide thrust, and aerodynamic surfaces to control the motion. Their efficiency is higher compared to other UAVs, allowing it to perform long flights. Their indoors use is exclude since they do not have the ability to hovering and can not turn at reduced distances [1] For their part, rotary-wing unmanned aerial vehicles (RW-UAVs) have various configurations including the conventional helicopter, the coaxial helicopter, and multi-rotors, which can sustain hover flight and take-off-landing vertically (VTOL). The FW-UAVs and RW-UAVs are the classic configurations most used in the applications assigned to unmanned aerial vehicles
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