Adaptive Modified RISE-based Quadrotor Trajectory Tracking with Actuator Uncertainty Compensation

This paper presents an adaptive robust nonlinear control method, which achieves reliable trajectory tracking control for a quadrotor unmanned aerial vehicle in the presence of gyroscopic effects, rotor dynamics, and external disturbances. Through novel mathematical manipulation in the error system development, the quadrotor dynamics are expressed in a control-oriented form, which explicitly incorporates the uncertainty in the gyroscopic term and control actuation term. An adaptive robust nonlinear control law is then designed to stabilize both the position and attitude loops of the quadrotor system. A rigorous Lyapunov-based analysis is utilized to prove asymptotic trajectory tracking, where the region of convergence can be made arbitrarily large through judicious control gain selection. Moreover, the stability analysis formally addresses gyroscopic effects and actuator uncertainty. To illustrate the performance of the control law, comparative numerical simulation results are provided, which demonstrate the improved closed-loop performance achieved under varying levels of parametric uncertainty and disturbance magnitudes

Adaptive trajectory tracking control for quadrotors with disturbances by using generalized regression neural networks

In this document, the development and experimental validation of a nonlinear controller with an adaptive disturbance compensation system applied on a quadrotor are presented. The introduced scheme relies on a generalized regression neural network (GRNN). The proposed scheme has a structure consisting of an inner control loop inaccessible to the user (i.e., an embedded controller) and an outer control loop which generates commands for the inner control loop. The adaptive GRNN is applied in the outer control loop. The proposed approach lies in the aptitude of the GRNN to estimate the disturbances and unmodeled dynamic effects without requiring accurate knowledge of the quadrotor parameters. The adaptation laws are deduced from a rigorous convergence analysis ensuring asymptotic trajectory tracking. The proposed control scheme is implemented on the QBall 2 quadrotor. Comparisons with respect to a PD-based control, an adaptive model regressor-based scheme, and an adaptive neural-network controller are carried out. The experimental results validate the functionality of the novel control scheme and show a performance improvement since smaller tracking error values are produced.Fil: Lopez Sanchez, Ivan. INSTITUTO POLITÉCNICO NACIONAL (IPN);Fil: Rossomando, Francisco Guido. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan. Instituto de Automática. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Automática; ArgentinaFil: Pérez Alcocer, Ricardo. INSTITUTO POLITÉCNICO NACIONAL (IPN);Fil: Soria, Carlos Miguel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan. Instituto de Automática. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Automática; ArgentinaFil: Carelli, Ricardo. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Automática; ArgentinaFil: Moreno Valenzuela, Javier. INSTITUTO POLITÉCNICO NACIONAL (IPN)

Neural Moving Horizon Estimation for Robust Flight Control

Estimating and reacting to disturbances is crucial for robust flight control of quadrotors. Existing estimators typically require significant tuning for a specific flight scenario or training with extensive ground-truth disturbance data to achieve satisfactory performance. In this paper, we propose a neural moving horizon estimator (NeuroMHE) that can automatically tune the key parameters modeled by a neural network and adapt to different flight scenarios. We achieve this by deriving the analytical gradients of the MHE estimates with respect to the weighting matrices, which enables a seamless embedding of the MHE as a learnable layer into neural networks for highly effective learning. Interestingly, we show that the gradients can be computed efficiently using a Kalman filter in a recursive form. Moreover, we develop a model-based policy gradient algorithm to train NeuroMHE directly from the quadrotor trajectory tracking error without needing the ground-truth disturbance data. The effectiveness of NeuroMHE is verified extensively via both simulations and physical experiments on quadrotors in various challenging flights. Notably, NeuroMHE outperforms a state-of-the-art neural network-based estimator, reducing force estimation errors by up to 76.7%, while using a portable neural network that has only 7.7% of the learnable parameters of the latter. The proposed method is general and can be applied to robust adaptive control of other robotic systems

Real-time UAV Complex Missions Leveraging Self-Adaptive Controller with Elastic Structure

The expectation of unmanned air vehicles (UAVs) pushes the operation environment to narrow spaces, where the systems may fly very close to an object and perform an interaction. This phase brings the variation in UAV dynamics: thrust and drag coefficient of the propellers might change under different proximity. At the same time, UAVs may need to operate under external disturbances to follow time-based trajectories. Under these challenging conditions, a standard controller approach may not handle all missions with a fixed structure, where there may be a need to adjust its parameters for each different case. With these motivations, practical implementation and evaluation of an autonomous controller applied to a quadrotor UAV are proposed in this work. A self-adaptive controller based on a composite control scheme where a combination of sliding mode control (SMC) and evolving neuro-fuzzy control is used. The parameter vector of the neuro-fuzzy controller is updated adaptively based on the sliding surface of the SMC. The autonomous controller possesses a new elastic structure, where the number of fuzzy rules keeps growing or get pruned based on bias and variance balance. The interaction of the UAV is experimentally evaluated in real time considering the ground effect, ceiling effect and flight through a strong fan-generated wind while following time-based trajectories.Comment: 18 page

L1\mathcal{L}_1Quad: L1\mathcal{L}_1 Adaptive Augmentation of Geometric Control for Agile Quadrotors with Performance Guarantees

Quadrotors that can operate safely in the presence of imperfect model knowledge and external disturbances are crucial in safety-critical applications. We present L1Quad, a control architecture for quadrotors based on the L1 adaptive control. L1Quad enables safe tubes centered around a desired trajectory that the quadrotor is always guaranteed to remain inside. Our design applies to both the rotational and the translational dynamics of the quadrotor. We lump various types of uncertainties and disturbances as unknown nonlinear (time- and state-dependent) forces and moments. Without assuming or enforcing parametric structures, L1Quad can accurately estimate and compensate for these unknown forces and moments. Extensive experimental results demonstrate that L1Quad is able to significantly outperform baseline controllers under a variety of uncertainties with consistently small tracking errors.Comment: The first two authors contributed equally to this wor

Adaptive Artificial Time Delay Control for Quadrotors under State-dependent Unknown Dynamics

Quadrotors are becoming more and more essential for applications such as payload delivery, inspection and search-and-rescue. Such operations pose considerable control challenges, especially when various (a priori unbounded) state-dependent unknown dynamics arises from payload variations, aerodynamic effects and from reaction forces while operating close to the ground or in a confined space. However, existing adaptive control strategies for quadrotors cannot handle unknown state-dependent uncertainties. We address such unsolved control challenge in this work via a novel adaptive method for artificial time delay control,where unknown dynamics is robustly compensated by using input and state measurements collected at immediate past time instant (i.e., artificially delayed). Closed-loop stability is established via Lyapunov theory. The effectiveness of this controller is validated using experimental results

Geometric Active Disturbance Rejection Control for Autonomous Rotorcraft in Complex Atmospheric Environment

This dissertation presents several novel robust tracking control schemes of rotorcraft unmanned aerial vehicles under realistic atmospheric turbulence. To achieve fast converging and stable performance of the rotorcraft control scheme, a new H\ {o}lder-continuous differentiator, similar to the super-twisting algorithm used in the second-order sliding model control scheme, is proposed with guaranteed fast finite-time stability. Unlike the super-twisting algorithm, which uses a sliding-mode structure to achieve finite-time stability, the proposed differentiator maintains its fast finite-time stability with H\ {o}lder continuity, theoretically eliminating the harmful chattering phenomenon in practical control applications. Perturbation and noise robustness analyses are conducted for the proposed differentiator. The dissertation formulates the rotorcraft tracking control and disturbance estimation problems separately. The rotorcraft aerial vehicle is modeled as a rigid body with control inputs that actuate all degrees of freedom of rotational motion and only one degree of freedom of translational motion. The motion of the aircraft is globally represented on \TSE, which is the tangent bundle of the special Euclidean group \SE. The translational and attitude control schemes track the desired position and attitude on \SE. The disturbance estimation problem is formulated as an extended states observer on \TSE. Next, two rotorcraft control schemes on \SE with disturbance rejection mechanisms are presented. The proposed disturbance rejection control systems comprise two parts: an extended states observer for disturbance estimation and a tracking control scheme containing the disturbance rejection term to track the trajectory. The first disturbance rejection control scheme comprises an exponentially stable extended states observer and an asymptotically stable tracking control scheme. The second system comprises a fast finite-time stable extended state observer and a fast finite-time stable tracking control scheme. The fast finite-time stable extended state observer uses the \textup{H\ {o}}lder-continuous differentiator to estimate the resultant external disturbance force and disturbance torque acting on the vehicle. It ensures stable convergence of disturbance estimation errors in finite time when the disturbances are constant. Software-in-the-loop simulation is carried out for the active disturbance rejection control scheme with an open-source autopilot and a physics-based simulation tool. The simulation utilizes simulated wind gusts, propeller aerodynamics, actuator limitation, and measurement noise to validate the disturbance rejection control systems in a simulated environment with high fidelity. Two sets of flight experiments are conducted to investigate the autonomous rotorcraft flight control performance under turbulent income flows. A wind tunnel composed of fan arrays is involved in both experiments to provide different turbulent incoming flows by adjusting the duty of individual fans. The first set of experiments conducts income flow measurements for wind tunnel calibration. For the turbulent flows generated by different fan configurations, their steady velocity field and unsteady turbulence characteristics are measured by a pressure scanner and hot-wire anemometer. The second set of experiments involves flight tests of a rotorcraft within the turbulent environment measured and calibrated in the first experiment set. The proposed extended states observer is implemented onto a rotorcraft by customizing an open-source autopilot software. With this implementation, the flight control performance of the proposed disturbance rejection control schemes is presented and compared with the autopilot without customization. The experimental results show that the proposed disturbance rejection control scheme enhanced by the disturbance estimation schem

Synchronization of multiple rigid body systems: a survey

The multi-agent system has been a hot topic in the past few decades owing to its lower cost, higher robustness, and higher flexibility. As a particular multi-agent system, the multiple rigid body system received a growing interest since its wide applications in transportation, aerospace, and ocean exploration. Due to the non-Euclidean configuration space of attitudes and the inherent nonlinearity of the dynamics of rigid body systems, synchronization of multiple rigid body systems is quite challenging. This paper aims to present an overview of the recent progress in synchronization of multiple rigid body systems from the view of two fundamental problems. The first problem focuses on attitude synchronization, while the second one focuses on cooperative motion control in that rotation and translation dynamics are coupled. Finally, a summary and future directions are given in the conclusion