244 research outputs found

    Model Predictive Control for Micro Aerial Vehicles: A Survey

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    This paper presents a review of the design and application of model predictive control strategies for Micro Aerial Vehicles and specifically multirotor configurations such as quadrotors. The diverse set of works in the domain is organized based on the control law being optimized over linear or nonlinear dynamics, the integration of state and input constraints, possible fault-tolerant design, if reinforcement learning methods have been utilized and if the controller refers to free-flight or other tasks such as physical interaction or load transportation. A selected set of comparison results are also presented and serve to provide insight for the selection between linear and nonlinear schemes, the tuning of the prediction horizon, the importance of disturbance observer-based offset-free tracking and the intrinsic robustness of such methods to parameter uncertainty. Furthermore, an overview of recent research trends on the combined application of modern deep reinforcement learning techniques and model predictive control for multirotor vehicles is presented. Finally, this review concludes with explicit discussion regarding selected open-source software packages that deliver off-the-shelf model predictive control functionality applicable to a wide variety of Micro Aerial Vehicle configurations

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

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    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 H2/H\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

    Model-Based Development and Evaluation of Control for Complex Multi-Domain Systems: Attitude Control for a Quadrotor UAV

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    A Cyber-Physical System (CPS) incorporates sensing, actuating, computing and communicative capabilities, which are often combined to control the system. The development of CPSs poses a challenge, since the complexity of the physical system dynamics must be taken into account when designing the control application. The physical system dynamics are often defined within mechanical and electrical engineering domains, with the control application residing in software and control engineering domains. Therefore, such a system can be considered multi-domain.With the constant increase in the complexity of such systems, caused by technological advances in all domains, new ways of approaching multi-domain system development are needed. One methodology, which excels in complexity management, is model-based development. Multidomain systems require collaborative modeling, where the physical system dynamics are captured in the Continuous Time (CT) modeling domain and the digital control is captured in the Discrete Event (DE) modeling domain.This thesis demonstrates how an extended CT-first model-based development approach can be applied to a complex multi-domain system. A collaborative model of a quadrotor Unmanned Aerial Vehicle (UAV) has been constructed and used to develop an attitude controller based on Model Predictive Control (MPC). The MPC controller has been compared to an existing open source Proportional Integral Derivative (PID) attitude controller.This thesis contributes to the discipline of model-based development with a methodological extension to the CT-first approach, which extends the conventional approach by expanding the physical modeling process into three consecutive steps. An evaluation of the extension is presented, describing how and when the extended methodology provides increased value

    One-Shot Learning of Manipulation Skills with Online Dynamics Adaptation and Neural Network Priors

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    One of the key challenges in applying reinforcement learning to complex robotic control tasks is the need to gather large amounts of experience in order to find an effective policy for the task at hand. Model-based reinforcement learning can achieve good sample efficiency, but requires the ability to learn a model of the dynamics that is good enough to learn an effective policy. In this work, we develop a model-based reinforcement learning algorithm that combines prior knowledge from previous tasks with online adaptation of the dynamics model. These two ingredients enable highly sample-efficient learning even in regimes where estimating the true dynamics is very difficult, since the online model adaptation allows the method to locally compensate for unmodeled variation in the dynamics. We encode the prior experience into a neural network dynamics model, adapt it online by progressively refitting a local linear model of the dynamics, and use model predictive control to plan under these dynamics. Our experimental results show that this approach can be used to solve a variety of complex robotic manipulation tasks in just a single attempt, using prior data from other manipulation behaviors

    Modeling Dynamic Systems for Multi-Step Prediction with Recurrent Neural Networks

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    This thesis investigates the applicability of Recurrent Neural Networks (RNNs) and Deep Learning methods for multi-step prediction of robotic systems. The unmodeled dynamics and simplifying assumptions in classic modeling methods result in models that yield rapidly diverging predictions when the model is used in an iterative fashion, i.e., for multi-step prediction. However, the effect of the unmodeled dynamics can be captured by collecting datasets of the system. Deep Learning provides a strong set of tools to extract patterns from data, however, large datasets are commonly required for the methods to work well. Collecting a large amount of data from a robotic system can be a cumbersome and expensive approach. In this work, Deep Learning methods, particularly RNNs, are studied and employed for the purpose of learning models of two aerial vehicles from experimental data. The feasibility of employing RNNs is first studied to learn a model of a quadrotor based on a simulated dataset, which yields a Multi Layer Fully Connected (MLFC) architecture. Models can be learned for multi-step prediction, recovering excellent predictions over 500 timesteps in the presence of simulated disturbances to the robot and noise on the measurements. To learn models from experimental data, the RNN state initialization problem is defined and formulated. It is shown that the RNN state initialization problem can be addressed by creating and training an initialization network jointly with the multi-step prediction network, and the combination can be used in a black-box modeling approach such that the model produces predictions which are immediately accurate. The RNN based black-box methods are trained on an experimental dataset gathered from a quadrotor and a publicly available helicopter dataset. The quadrotor dataset, which encompasses approximately 4 hours of flight data in various regimes, has been released and is now available publicly online. Finally, a hybrid network, which combines the proposed RNN based black-box models with a physics based quadrotor model into a single RNN-based modeling system is introduced. The proposed hybrid network solves many of the limitations of the existing state of the art in long-term prediction for robotics systems. Trained on the quadrotor dataset, the hybrid model provides accurate body angular rate and velocity predictions of the vehicle over almost 2 seconds which is suitable to be used in a variety of model-based controller applications
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