Online Deep Learning for Improved Trajectory Tracking of Unmanned Aerial Vehicles Using Expert Knowledge

This work presents an online learning-based control method for improved trajectory tracking of unmanned aerial vehicles using both deep learning and expert knowledge. The proposed method does not require the exact model of the system to be controlled, and it is robust against variations in system dynamics as well as operational uncertainties. The learning is divided into two phases: offline (pre-)training and online (post-)training. In the former, a conventional controller performs a set of trajectories and, based on the input-output dataset, the deep neural network (DNN)-based controller is trained. In the latter, the trained DNN, which mimics the conventional controller, controls the system. Unlike the existing papers in the literature, the network is still being trained for different sets of trajectories which are not used in the training phase of DNN. Thanks to the rule-base, which contains the expert knowledge, the proposed framework learns the system dynamics and operational uncertainties in real-time. The experimental results show that the proposed online learning-based approach gives better trajectory tracking performance when compared to the only offline trained network.Comment: corrected version accepted for ICRA 201

Input uncertainty sensitivity enhanced non-singleton fuzzy logic controllers for long-term navigation of quadrotor UAVs

Input uncertainty, e.g., noise on the on-board camera and inertial measurement unit, in vision-based control of unmanned aerial vehicles (UAVs) is an inevitable problem. In order to handle input uncertainties as well as further analyze the interaction between the input and the antecedent fuzzy sets (FSs) of non-singleton fuzzy logic controllers (NSFLCs), an input uncertainty sensitivity enhanced NSFLC has been developed in robot operating system (ROS) using the C++ programming language. Based on recent advances in non-singleton inference, the centroid of the intersection of the input and antecedent FSs (Cen-NSFLC) is utilized to calculate the firing strength of each rule instead of the maximum of the intersection used in traditional NSFLC (Tra-NSFLC). An 8-shaped trajectory, consisting of straight and curved lines, is used for the real-time validation of the proposed controllers for a trajectory following problem. An accurate monocular keyframe-based visual-inertial simultaneous localization and mapping (SLAM) approach is used to estimate the position of the quadrotor UAV in GPS denied unknown environments. The performance of the Cen-NSFLC is compared with a conventional proportional integral derivative (PID) controller, a singleton FLC (SFLC) and a Tra-NSFLC. All controllers are evaluated for different flight speeds, thus introducing different levels of uncertainty into the control problem. Visual-inertial SLAM-based real time quadrotor UAV flight tests demonstrate that not only does the Cen-NSFLC achieve the best control performance among the four controllers, but it also shows better control performance when compared to their singleton counterparts. Considering the bias in the use of model based controllers, e.g. PID, for the control of UAVs, this paper advocates an alternative method, namely Cen-NSFLCs, in uncertain working environments

Similarity-based non-singleton fuzzy logic control for improved performance in UAVs

As non-singleton fuzzy logic controllers (NSFLCs) are capable of capturing input uncertainties, they have been effectively used to control and navigate unmanned aerial vehicles (UAVs) recently. To further enhance the capability to handle the input uncertainty for the UAV applications, a novel NSFLC with the recently introduced similarity-based inference engine, i.e., Sim-NSFLC, is developed. In this paper, a comparative study in a 3D trajectory tracking application has been carried out using the aforementioned Sim-NSFLC and the NSFLCs with the standard as well as centroid composition-based inference engines, i.e., Sta-NSFLC and Cen-NSFLC. All the NSFLCs are developed within the robot operating system (ROS) using the C++ programming language. Extensive ROS Gazebo simulation-based experiments show that the Sim-NSFLCs can achieve better control performance for the UAVs in comparison with the Sta-NSFLCs and Cen-NSFLCs under different input noise levels

Learning control of unmanned aerial vehicles using artificial intelligence-based methods

In recent years, many research activities have focused on the developments for unmanned aerial vehicles (UAVs) due to their usefulness in providing cost-effective solutions to dangerous, dirty and dull tasks. In many applications, it is crucial for UAVs to be able to fly autonomously in uncertain environments under variable operating conditions. In such circumstances, an intelligent capability of the flight controller is a must rather than a choice. Model-free controllers propose alternative solutions to the model-based controllers without requiring a precise system's model which is often either unavailable or time-consuming to obtain. One branch of model-free methods is composed by fuzzy logic controllers (FLCs) due to their capability of delivering excellent control in the presence of uncertainties. However, one weakness of FLCs is that their parameters have to be tuned to deal efficiently with uncertainties. On the other hand, neural networks are computing models which progressively improve their performance by learning from training examples. Hence, artificial neural networks (ANNs) and deep neural networks (DNNs) propose learning approaches to enhance control strategies. Nevertheless, the main disadvantage of neural networks is that their inner workings are difficult to interpret. The limitations of fuzzy logic and neural networks were a driving force behind the creation of hybrid systems where the combination of DNN and FLC can overcome the drawbacks of each individual method. This thesis focuses on the aforementioned artificial intelligence-based control methods that enable UAVs to accurately track 3D trajectories. The investigation starts from the simplest static type-1 FLC, through interval type-2 FLC, to the most efficient novel fuzzy mapping-based controllers. In this thesis, it was demonstrated that the analytical representation of the fuzzy mapping facilitates the tuning of the parameters in FLCs. Next, the controllers based on ANNs and DNNs with learning capabilities were investigated. In this thesis, it was verified experimentally that the proposed approaches can improve real-time control performance. Finally, a novel deep fuzzy neural network framework which profoundly fuses DNN and FLC for online training was proposed and validated under a variety of operating conditions.Doctor of Philosoph

Online deep learning for improved trajectory tracking of unmanned aerial vehicles using expert knowledge

This work presents an online learning-based control method for improved trajectory tracking of unmanned aerial vehicles using both deep learning and expert knowledge. The proposed method does not require the exact model of the system to be controlled, and it is robust against variations in system dynamics as well as operational uncertainties. The learning is divided into two phases: offline (pre-)training and online (post-)training. In the former, a conventional controller performs a set of trajectories and, based on the input-output dataset, the deep neural network (DNN)-based controller is trained. In the latter, the trained DNN, which mimics the conventional controller, controls the system. Unlike the existing papers in the literature, the network is still being trained for different sets of trajectories which are not used in the training phase of DNN. Thanks to the rule-base, which contains the expert knowledge, the proposed framework learns the system dynamics and operational uncertainties in real-time. The experimental results show that the proposed online learning-based approach gives better trajectory tracking performance when compared to the only offline trained network.NRF (Natl Research Foundation, S’pore)MOE (Min. of Education, S’pore)Accepted versio

Intuit before tuning : type-1 and type-2 fuzzy logic controllers

Although a considerable amount of effort has been put in to show that fuzzy logic controllers have exceptional capabilities of dealing with uncertainty, there are still noteworthy concerns, e.g., the design of fuzzy logic controllers is an arduous task due to the lack of closed-form input–output relationships which is a limitation to interpretability of these controllers. The role of design parameters in fuzzy logic controllers, such as position, shape, and height of membership functions, is not straightforward. Motivated by the fact that the availability of an interpretable relationship from input to output will simplify the design procedure of fuzzy logic controllers, the main aims in this work are derive fuzzy mappings for both type-1 and interval type-2 fuzzy logic controllers, analyse them, and eventually benefit from such a nonlinear mapping to design fuzzy logic controllers. Thereafter, simulation and real-time experimental results support the presented theoretical findings.Accepted versio

An intelligent hybrid artificial neural network-based approach for control of aerial robots

In this work, a learning model-free control method is proposed for accurate trajectory tracking and safe landing of unmanned aerial vehicles (UAVs). A realistic scenario is considered where the UAV commutes between stations at high-speeds, experiences a single motor failure while surveying an area, and thus requires to land safely at a designated secure location. The proposed challenge is viewed solely as a control problem. A hybrid control architecture – an artificial neural network (ANN)-assisted proportional-derivative controller – is able to learn the system dynamics online and compensate for the error generated during different phases of the considered scenario: fast and agile flight, motor failure, and safe landing. Firstly, it deals with unmodelled dynamics and operational uncertainties and demonstrates superior performance compared to a conventional proportional-integral-derivative controller during fast and agile flight. Secondly, it behaves as a fault-tolerant controller for a single motor failure case in a coaxial hexacopter thanks to its proposed sliding mode control theory-based learning architecture. Lastly, it yields reliable performance for a safe landing at a secure location in case of an emergency condition. The tuning of weights is not required as the structure of the ANN controller starts to learn online, each time it is initialised, even when the scenario changes – thus, making it completely model-free. Moreover, the simplicity of the neural network-based controller allows for the implementation on a low-cost low-power onboard computer. Overall, the real-time experiments show that the proposed controller outperforms the conventional controller.NRF (Natl Research Foundation, S’pore)MOE (Min. of Education, S’pore)Accepted versio

Type-2 fuzzy logic controllers made even simpler : from design to deployment for UAVs

This paper aims to provide a clear explanation of the role of the footprint of uncertainty (FOU) parameters on the control signal generation and, thus, to increase the interpretability of specially structured interval type-2 (IT2) fuzzy logic controllers, namely single input IT2 fuzzy PID (SI-IT2-FPID) controller. In this context, we extend the analysis performed for SI-IT2-FPID controllers by providing the effect of the FOU parameters on control surface (CS) generation. We show that, by only adjusting a single parameter that shapes the FOU, it is possible to generate commonly employed CSs without a requirement of an optimization assistance. In order to validate our theoretical analysis, we present comparative real world quadcopter flight tests. The real-time experimental results show that the SI-IT2-FPID controller can achieve better control performance in the presence of uncertainties and strong wind conditions when compared to its type-1 and conventional counterparts. We believe that the results of this study will open the doors to a wider use of SI-IT2-FPID controllers in real world control applications as the proposed structure is easy to design and feasible to deploy, especially in real-time control systems