Suspended Load Path Tracking Control Using a Tilt-rotor UAV Based on Zonotopic State Estimation

This work addresses the problem of path tracking control of a suspended load using a tilt-rotor UAV. The main challenge in controlling this kind of system arises from the dynamic behavior imposed by the load, which is usually coupled to the UAV by means of a rope, adding unactuated degrees of freedom to the whole system. Furthermore, to perform the load transportation it is often needed the knowledge of the load position to accomplish the task. Since available sensors are commonly embedded in the mobile platform, information on the load position may not be directly available. To solve this problem in this work, initially, the kinematics of the multi-body mechanical system are formulated from the load's perspective, from which a detailed dynamic model is derived using the Euler-Lagrange approach, yielding a highly coupled, nonlinear state-space representation of the system, affine in the inputs, with the load's position and orientation directly represented by state variables. A zonotopic state estimator is proposed to solve the problem of estimating the load position and orientation, which is formulated based on sensors located at the aircraft, with different sampling times, and unknown-but-bounded measurement noise. To solve the path tracking problem, a discrete-time mixed $\mathcal{H}_2/\mathcal{H}_\infty$ controller with pole-placement constraints is designed with guaranteed time-response properties and robust to unmodeled dynamics, parametric uncertainties, and external disturbances. Results from numerical experiments, performed in a platform based on the Gazebo simulator and on a Computer Aided Design (CAD) model of the system, are presented to corroborate the performance of the zonotopic state estimator along with the designed controller

Agile load transportation systems using aerial robots

In this dissertation, we address problems that can occur during load transport using aerial robots, i.e., small scale quadrotors. First, detailed models of such transportation system are derived. These models include nonlinear models of a quadrotor, a model of a quadrotor carrying a fixed load and a model of a quadrotor carrying a suspended load. Second, the problem of quadrotor stabilization and trajectory tracking with changes of the center of gravity of the transportation system is addressed. This problem is solved using model reference adaptive control based on output feedback linearization that compensates for dynamical changes in the center of gravity of the quadrotor. The third problem we address is a problem of a swing-free transport of suspended load using quadrotors. Flying with a suspended load can be a very challenging and sometimes hazardous task as the suspended load significantly alters the flight characteristics of the quadrotor. In order to deal with suspended load flight, we present a method based on dynamic programming which is a model based offline method. The second investigated method we use is based on the Nelder-Mead algorithm which is an optimization technique used for nonlinear unconstrained optimization problems. This method is model free and it can be used for offline or online generation of the swing-free trajectories for the suspended load. Besides the swing-free maneuvers with suspended load, load trajectory tracking is another problem we solve in this dissertation. In order to solve this problem we use a Nelder-Mead based algorithm. In addition, we use an online least square policy iteration algorithm. At the end, we propose a high level algorithm for navigation in cluttered environments considering a quadrotor with suspended load. Furthermore, distributed control of multiple quadrotors with suspended load is addressed too. The proposed hierarchical architecture presented in this doctoral dissertation is an important step towards developing the next generation of agile autonomous aerial vehicles. These control algorithms enable quadrotors to display agile maneuvers while reconfiguring in real time whenever a change in the center of gravity occurs. This enables a swing-free load transport or trajectory tracking of the load in urban environments in a decentralized fashion

A Hybrid Control Approach for the Swing Free Transportation of a Double Pendulum with a Quadrotor

In this article, a control strategy approach is proposed for a system consisting of a quadrotor transporting a double pendulum. In our case, we attempt to achieve a swing free transportation of the pendulum, while the quadrotor closely follows a specific trajectory. This dynamic system is highly nonlinear, therefore, the fulfillment of this complex task represents a demanding challenge. Moreover, achieving dampening of the double pendulum oscillations while following a precise trajectory are conflicting goals. We apply a proportional derivative (PD) and a model predictive control (MPC) controllers for this task. Transportation of a multiple pendulum with an aerial robot is a step forward in the state of art towards the study of the transportation of loads with complex dynamics. We provide the modeling of the quadrotor and the double pendulum. For MPC we define the cost function that has to be minimized to achieve optimal control. We report encouraging positive results on a simulated environmentcomparing the performance of our MPC-PD control circuit against a PD-PD configuration, achieving a three fold reduction of the double pendulum maximum swinging angle.This work has been partially supported by FEDER funds through MINECO project TIN2017-85827-P, and project KK-202000044 of the Elkartek 2020 funding program of the Basque Government. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 777720

Design of a neuro-sliding mode controller for interconnected quadrotor UAVs carrying a suspended payload

summary:In this study, a generalized system model is derived for interconnected quadrotor UAVs carrying a suspended payload. Moreover, a novel neural network-based sliding mode controller (NSMC) for the system is suggested. While the proposed controller uses the advantages of the robust structure of sliding mode controller (SMC) for the nonlinear system, the neural network component eliminates the chattering effects in the control signals of the SMC and increases the efficiency of the SMC against time-varying dynamic uncertainties. After the controller design is carried out, a comprehensive stability analysis based on Lyapunov theory is given to assure the asymptotic stability of the system. Finally, extensive numerical simulations with detailed comparisons are used to verify the effectiveness of the proposed controller

Payload Grasping and Transportation by a Quadrotor with a Hook-Based Manipulator

The paper proposes an efficient trajectory planning and control approach for payload grasping and transportation using an aerial manipulator. The proposed manipulator structure consists of a hook attached to a quadrotor using a 1 DoF revolute joint. To perform payload grasping, transportation, and release, first, time-optimal reference trajectories are designed through specific waypoints to ensure the fast and reliable execution of the tasks. Then, a two-stage motion control approach is developed based on a robust geometric controller for precise and reliable reference tracking and a linear--quadratic payload regulator for rapid setpoint stabilization of the payload swing. The proposed control architecture and design are evaluated in a high-fidelity physical simulator with external disturbances and also in real flight experiments.Comment: Submitted to IEEE Robotics and Automation Letters (2023

Adaptive fuzzy proportional-integral-derivative control for micro aerial vehicle

With multiple industries employing Micro Aerial Vehicles (MA V) to accomplish various tasks comprising agricultural spraying, package delivery and disaster monitoring, the MA V system has attracted researchers towards resolving its stability issue as emerged from external disturbances. Disruptions caused by both wind and payload change disturbances have prevailed as natural mishaps which degrade performance of the quadrotor MA V system at the horizontal and vertical positions in the aspects of overshoot (OS), rise time (Tr), settling time (Ts)and steady-state error (ess)· Such adversities then cause increased error between the system's desired and actual positions, with a longer rise time and settling time towards reaching its steady-state condition. Adopting the rotary wing quad-rotor MAV system with 'X' configuration as the groundwork, the current study has especially set to explore a new approach for the system's robust positional control in the concurrent presence of wind and payload change disturbances. Earlier literatures have simultaneously suggested the adoptions of linear, nonlinear and hybrid approaches towards handing stability challenge of the quad-rotor MA V. Notably, most hybrid approaches are unable to account for current changes in the system's environment, whilst incapable of concomitantly handle multiple disturbances. An instance being the Fuzzy-PID (FPID) method which merely adjusts the Proportional-Integral-Derivative (PID) gains ensuing discovered positional error from emergence of system's overshoot. Acknowledging such incompetency, this research further proposed Adaptive Fuzzy-PlD (AFPID) controller as the contemporary hybrid approach that includes adaptability function for overcoming nonlinearity of the quad-rotor MA V system, while maintaining the system's robust performance facing current environmental changes from simultaneous wind and payload change disturbances. With the proposed adaptive fuzzy control being adopted to adjust the PID gains in accordance to surrounding changes, undertaken improvement is hereby targeted to eliminate the effect of wind and payload change disturbances amidst stabilizing the employed system. In return, encountered error on both the quad-rotor MA V's horizontal and vertical positions is expected to decline despite concurrent bombardment of multiple external disturbances, following a decrease to the system's overshoot (OS), rise time (Tr), settling time (Ts)and steady-state error (ess). In simulation, performance of the proposed AFPID controller on the horizontal, y position as studied under circumstances of different incoming wind velocities and water flow rates with respect to OS, Tr, Ts and e55 is placed in comparison to the performance of the PID and FPID methods. Improvement is observed in the system's ess for the AFPID controller on the horizontal, y position amid disruption of combined disturbances, with respective reductions of0.93 x 10-3 % and 1.35 X 10-3 % over the performances of PID and FPID controllers. Obtained results then confirm corresponding decline of 27.5% and 21.70% in OS for the AFPID controller over the PID and FPID controllers. A decline of 13 7.50 s and 13.40 s in Ts is further recorded for the AFPID controller as compared to the respective PID and FPID controllers. Accumulated findings, thus, validate AFPID as an effective controller for minimized positional error, smaller overshoot (OS) and steady-state error (esJ, as well as shorter settling time (Ts) and rise time (Tr) as compared to the earlier PID and FPID controllers when faced with uncertain situations of wind and payload change disturbances

Observer-based fuzzy tracking control for an unmanned aerial vehicle with communication constraints

We investigate the trajectory tracking problem of underactuated aerial vehicles with unknown mass in the presence of unknown non-vanishing disturbances using an event-triggered approach, while considering the constraint that the derivative of the reference trajectory is not available. In contrast to existing references where the derivative of the reference trajectory is needed, here we first introduce a high-gain observer to estimate the unknown derivative solely from the reference trajectory. A disturbance observer is designed to compensate for non-vanishing disturbances, such as wind, etc. Fuzzy logic systems are used to approximate the model uncertainty arising from the unknown mass of the vehicle, and then we derive a thrust command law that follows from a desired stabilizing force. Additionally, unlike traditional fixed and relative threshold strategies that rely solely on control signals, we develop a new time-varying eventtriggered mechanism linked to the performance of the controlled system, taking into account factors such as tracking errors, to develop angular velocity commands, enhancing tracking accuracy while efficiently conserving communication resources, especially in the absence of Zeno behavior. We present simulation results to demonstrate the efficacy of the proposed approach and validate the theoretical findings.</p