Panos Marantos

UAV State Estimation Using Adaptive Complementary Filters

We address the complete state estimation problem of unmanned aerial vehicles, even under high-dynamic 3-D aerobatic maneuvers, while using low-cost sensors with bias variations and higher levels of noise. In such conditions, the control demand, for a robust real-time data fusion filter with minimal lag and noise, is addressed with the efficiency of a complementary filter scheme. First, the attitude is directly estimated in Special Orthogonal Group (SO(3)) by complementing the noisy accelerometer/magnetometer vector basis with a gyro propagated vector basis. Data fusion follows a least square minimization in SO(3) (Wahbas problem) solved in an analytic nonrecursive manner. Stability of the proposed filter is shown and performance metrics are extracted, whereas the computational complexity has been minimized with an appropriate reference frame and a custom singular value decomposition algorithm. An adaptation scheme is proposed to allow unhindered operation of the filter to erroneous inputs introduced by the high dynamics of a 3-D flight. Finally, the velocity/position estimation is mainly constructed by complementary filters combining multiple sensors. In addition to the low complexity and the filtering of the noise, the proposed observer is aided through a developed vision algorithm, enabling the use of the filter in Global-Positioning-System-denied environments. Extensive experimental results and comparative studies with state-of-the-art filters, either in the laboratory or in the field using high-performance autonomous helicopters, demonstrate the efficacy of the proposed scheme in demanding conditions.

Quadrotor landing on an inclined platform of a moving ground vehicle

In this work we study the problem of landing a quadrotor on an inclined moving platform. The aerial robot employs an forward looking on-board camera to detect and observe the landing platform, which is carried by a mobile robot moving independently on an inclined surface. The platform may also be tilted with respect to the mobile robot. The overall goal is to design the aerial robots control inputs such that it initially approaches the platform, while maintaining it within the cameras field of view and finally lands on it, in a way that minimizes the errors in position, attitude and velocity, while avoiding collision. Owing to the inclined ground and landing surface, the desired final state of the aerial robot is not an equilibrium state, which complicates significantly the control design. In that respect, a discrete-time non-linear model predictive controller was developed that optimizes both the trajectories and the time horizon, towards achieving the aforementioned objectives while respecting the input constraints as well. Finally, an extensive experimental study, with a Pioneer mobile robot and a Parrot ARDrone quadrotor, clarifies and verifies the theoretical findings.

Autonomous model-free landing control of small-scale flybarless helicopters

This paper proposes an autonomous landing scheme for a small-scale flybarless helicopter equipped with low-cost navigation sensors. The main contribution of this paper is the design of a model-free motion controller that guarantees autonomous landing with prescribed transient and steady state response, despite the presence of external disturbances acting on the vehicle. The proposed control scheme is of low complexity and does not require any knowledge of the helicopter dynamic parameters. Hence, it can be easily implemented in embedded control platforms integrated on small-scale helicopters. In order to provide the controller with accurate estimation of the vehicles state vector during the landing procedure, an asynchronous sensor fusion and state estimation algorithm, based on an Unscented Kalman Filter (UKF), has been also implemented. The performance and the efficiency of the overall scheme are experimentally verified using a small-scale flybarless helicopter in a real autonomous landing process.

Robust stabilization control of unknown small-scale helicopters

In this paper, we address the attitude and vertical stabilization problem for small-scale helicopters. An emergency controller that would successfully stabilize the helicopter in a safe flight mode when a pilot/autopilot fails to control it, owing to unexpected reasons, is of outmost importance in flight control systems. In this direction, we propose a low complexity nonlinear control scheme that drives the angles and the vertical speed to zero with prescribed transient and steady state response, without incorporating any knowledge of the dynamic model parameters in the control design. The stereographic coordinates were employed to model the attitude state of the helicopter in an attempt to guarantee the safe stabilization for every possible initial orientation without introducing any representation singularities as in the Euler angles representation or increasing complexity as in conventional four element quaternions. Moreover, the transient and steady state performance of the proposed scheme is a priori determined even in the presence of external disturbances. Furthermore, the overall control scheme can be easily implemented on embedded flight systems equipped with low-cost sensors. Finally, simulation and experimental results on a realistic platform verify the efficacy of the proposed method.

Robust Trajectory Tracking Control for Small-Scale Unmanned Helicopters With Model Uncertainties

This paper addresses the trajectory tracking control problem for small-scale unmanned helicopters with model uncertainties. In particular, we propose a robust control scheme that is decomposed into a position and an attitude control modules, operating in a cascaded form, and does not depend on the explicit knowledge of the value of the model parameters. Exploiting the concepts and techniques of prescribed performance control, each module guarantees trajectory tracking with prescribed transient and steady-state response, despite the presence of external disturbances, such as wind gusts and ground effect phenomena. Additionally, a novel reformulation of the translational dynamics reveals a sufficient condition for closed-loop stability and allows us to extract the appropriate attitude commands straightforwardly. Moreover, the derived control algorithms are of low complexity and thus can be efficiently implemented on low-cost, aerial embedded systems of limited power and computational resources. Finally, the efficacy of the proposed control scheme is verified via extensive experimental studies using an autonomous small-scale helicopter.

A Prescribed Performance Robust Nonlinear Model Predictive Control framework

In this paper we propose a novel approach in designing Robust Model Predictive Controllers (abbr. MPC) for systems with a Prescribed Performance in the states. In particular, general continuous-time nonlinear systems which are constrained in the states by prescribed performance functions and are affected by bounded, persistent, additive disturbances, are considered in this paper. With the proposed approach the constrained plant can be transformed into an unconstrained one, thus the optimization problem of the MPC becomes less complex, the computational burden is significantly reduced and the closed-loop system is proven to be Input-to-State Stable with respect to disturbances, while the inputs and states strictly remain in the predefined sets. The efficacy of the theoretic results is depicted by an academic simulation example and through comparison results.

Vision-based Autonomous Landing Control for Unmanned Helicopters

This paper presents the design of a complete control system for the autonomous landing of unmanned flybarless helicopters on known stationary visual landmarks. A state estimator based on the complementary filters notion, estimates the position, translational velocity and attitude vectors of the vehicle by fusing data acquired from the on–board camera and an Inertial Measurement Unit. A vision-aided nonlinear model predictive controller is designed for the landing motion of the helicopter, assuming that the on–board camera is rigidly (i.e., no additional Degrees of Freedom (DOF)) attached on the vehicle. Although the under–actuated character of the helicopter dynamics introduces counter–goals for minimizing the error between the vehicle and the landmark, the proposed control scheme guarantees, via hard nonlinear constraints, that the landmark will always be kept inside the camera field of view during the landing procedure. In order to simplify the derived algorithm without violating the robustness of the proposed controller, we reformulate the translational helicopter dynamics in order to reduce the number of the unknown model parameters to a minimum. Moreover, a parameter/disturbance observer is designed for estimating simultaneously the vehicle’s unknown dynamic parameters as well as the induced disturbances. The efficacy of the proposed landing scheme is evaluated via a set of experimental and simulation results, using a small–scale flybarless helicopter.

A competitive differential game between an unmanned aerial and a ground vehicle using model predictive control

In this work a non-cooperative competitive differential continuous game between an unmanned aerial and an unmanned ground vehicle is studied. Each player acts independently trying to satisfy its own objective function. Specifically the Unmanned Aerial Vehicle (UAV) is trying to reduce the relative distance and orientation by the Unmanned Ground Vehicle (UGV), while the latter is trying to increase it. For this purpose a controller is designed using the concepts of Non-Linear Model Predictive Control (NL-MPC), for each player, to calculate in real time its optimal trajectory by solving its Minimax objective function with double optimization assuming the other player moves optimal (worst case scenario) and taking into account the complete model dynamics. Furthermore, we solve iteratively the above optimization in order to increase the levels of the thinking, making the players more capable of predicting opponents best move, thus changing their optimal trajectory for their benefit. Various conclusions are made for the strategy that each agent follows in a realistic simulation game of these two “rational” players, where one player is fast (UAV) and the other is slower (UGV) but more maneuverable.