A comparison between advanced model-free PID and model-based LQI attitude control of a quadcopter using asynchronous android flight data
01 Jul 2017-pp 1023-1028
TL;DR: This paper compares two control techniques for a DJI F450 quadcopter which is controlled and stabilized by a non-rooted onboard Android smartphone, without the aid of an external IMU, and introduces a LQI controller which is capable of satisfactorily tracking a reference command in the presence of errors, noise, and model uncertainties.
Abstract: In this paper, we compare two control techniques for a DJI F450 quadcopter which is controlled and stabilized by a non-rooted onboard Android smartphone, without the aid of an external IMU. Specifically, we compare an advanced modelfree PID and LQI controller. Since Android is not a realtime system, the control commands and sensor measurements are subject to significant latencies, and hence the PID controller is modified to account for non-trivial measurement asynchronicities. We also show that some features can be added to the widely used baseline PID to obtain better performance in the presence of latencies and noise. Finally, we introduce a LQI controller which is capable of satisfactorily tracking a reference command in the presence of errors, noise, and model uncertainties, and compare the results to the PID controller.
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TL;DR: In this paper, a Nonlinear PID (NLPID) controller is proposed to stabilize the translational and rotational motion of a 6-DOF UAV quadrotor system and enforce it to track a given trajectory with minimum energy and error.
58 citations
Posted Content•
TL;DR: The proposed NLPID controller for each of the six subsystems of the 6-DOF UAV quadrotor system has been compared with the Linear PID one and the simulations showed the effectiveness of the proposed N LPID controller in terms of speed, control energy, and steady-state error.
Abstract: A Nonlinear PID (NLPID) controller is proposed to stabilize the translational and rotational motion of a 6-DOF UAV quadrotor system and enforce it to track a given trajectory with minimum energy and error. The complete nonlinear model of the 6-DOF quadrotor system are obtained using Euler-Newton formalism and used in the design process, taking into account the velocity and acceleration vectors resulting in a more accurate 6-DOF quadrotor model and closer to the actual system. Six NLPID controllers are designed, each for Roll, Pitch, Yaw, Altitude, and the Position subsystems, where their parameters are tuned using GA to minimize a multi-objective Output Performance Index (OPI). The stability of the 6-DOF UAV subsystems has been analyzed in the sense of Hurwitz stability theorem under certain conditions on the gains of the NLPID controllers. The simulations have been accomplished under MATLAB/SIMULINK environment and included three different trajectories, i.e., circular, helical, and square. The proposed NLPID controller for each of the six subsystems of the 6-DOF UAV quadrotor system has been compared with the Linear PID (LPID) one and the simulations showed the effectiveness of the proposed NLPID controller in terms of speed, control energy, and steady-state error.
32 citations
01 Dec 2017
TL;DR: A variety of control methods were used to control the space robots attitude to obtain time response in order to minimize the EULERINT criterion.
Abstract: Space robots control and the advantages of controllers for these robots are active research topics. In this paper, a variety of control methods were used to control the space robots attitude to obtain time response in order to minimize the EULERINT criterion. EULERINT is in fact the time integral of the Euler angel between the body axis and the target axis over the maneuvers and thus is an interpretation of error trajectory, however control efforts have also been reviewed. First, the EULERINT criterion values were compared in the case of a PD controller applied to linear and nonlinear models using various kinematics descriptions (Euler angles, quaternion vector and direction cosine matrix). It was discovered that among the aforementioned kinematics terminologies, the quaternion exhibits the lowest value of the EULERINT criterion. Thus, the investigation of other control methods including LQR, pole placement and adaptive feedback linearization controls was conducted using the quaternion kinematics to determine which method yields the lowest EULERINT value, control effort and simulation elapsed time.
9 citations
Cites methods from "A comparison between advanced model..."
...In reference [21], an advanced model-free PID controller model and a LQI controller are compared in which the LQI controller demonstrated better performance in the presence of disturbances, noise and uncertainty compared to the PID controller....
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TL;DR: Experimental results show that robust LQR-FOPIλDµ controller has the best tracking dynamics among them with less overshoot, settling time as well as the robust structure against external disturbances and sensor noises.
8 citations
01 Jan 2021
TL;DR: The proposed NLPID controller for each of the six subsystems of the 6-DOF UAV quadrotor system has been compared with the linear PID one, and the simulations showed the effectiveness of the proposed N LPID controller in terms of speed, control energy, and steady state error.
Abstract: A nonlinear proportional integral derivative (NLPID) controller is proposed to stabilize the translational and rotational motion of a six-degree of freedom (DOF) unmanned aerial vehicle (UAV) quadrotor system and enforce it to track a given trajectory with minimum energy and error The complete nonlinear model of the 6-DOF quadrotor system is obtained using Euler–Newton formalism and used in the design process, taking into account the velocity and acceleration vectors, resulting in a more accurate 6-DOF quadrotor model and that more closely resembles the actual system Six NLPID controllers are designed, each for roll, pitch, yaw, altitude, and the position subsystems, where their parameters are tuned using a genetic algorithm (GA) to minimize a multiobjective output performance index The stability of the 6-DOF UAV subsystems has been analyzed in the sense of the Hurwitz stability theorem under certain conditions on the gains of the NLPID controllers The simulations have been accomplished under the MATLAB®/Simulink environment and include three different trajectories, ie, circular, helical, and square The proposed NLPID controller for each of the six subsystems of the 6-DOF UAV quadrotor system has been compared with the linear PID one, and the simulations showed the effectiveness of the proposed NLPID controller in terms of speed, control energy, and steady state error
6 citations
References
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Book•
01 Jun 1979TL;DR: In this article, an augmented edition of a respected text teaches the reader how to use linear quadratic Gaussian methods effectively for the design of control systems, with step-by-step explanations that show clearly how to make practical use of the material.
Abstract: This augmented edition of a respected text teaches the reader how to use linear quadratic Gaussian methods effectively for the design of control systems. It explores linear optimal control theory from an engineering viewpoint, with step-by-step explanations that show clearly how to make practical use of the material. The three-part treatment begins with the basic theory of the linear regulator/tracker for time-invariant and time-varying systems. The Hamilton-Jacobi equation is introduced using the Principle of Optimality, and the infinite-time problem is considered. The second part outlines the engineering properties of the regulator. Topics include degree of stability, phase and gain margin, tolerance of time delay, effect of nonlinearities, asymptotic properties, and various sensitivity problems. The third section explores state estimation and robust controller design using state-estimate feedback. Numerous examples emphasize the issues related to consistent and accurate system design. Key topics include loop-recovery techniques, frequency shaping, and controller reduction, for both scalar and multivariable systems. Self-contained appendixes cover matrix theory, linear systems, the Pontryagin minimum principle, Lyapunov stability, and the Riccati equation. Newly added to this Dover edition is a complete solutions manual for the problems appearing at the conclusion of each section.
3,254 citations
"A comparison between advanced model..." refers background in this paper
...The state space model (14) does not imply an integral action on the error (1), furthermore, robust tracking control can be achieved by extending (14) and introducing an integral state vector XI ∈ R(3) with the following time derivative to force the error to converge to zero [15] [16]...
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28 Sep 2004
TL;DR: The results of two model-based control techniques applied to an autonomous four-rotor micro helicopter called quadrotor are presented, a classical approach (PID) assumed a simplified dynamics and a modern technique based on a more complete model.
Abstract: The development of miniature flying robots has become a reachable dream, thanks to the new sensing and actuating technologies. Micro VTOL systems represent a useful class of flying robots because of their strong abilities for small-area monitoring and building exploration. In this paper, we present the results of two model-based control techniques applied to an autonomous four-rotor micro helicopter called quadrotor. A classical approach (PID) assumed a simplified dynamics and a modern technique (LQ) based on a more complete model. Various simulations were performed and several tests on the bench validate the control laws. Finally, we present the results of the first test in flight with the helicopter released. These developments are part of the OS4 project in our lab.
1,264 citations
"A comparison between advanced model..." refers background or methods in this paper
...Even though most of the related research work was only applied to the simulation level, some control approaches have been validated experimentally, such as LQ [1][4], backstepping [4], and sliding-mode control [4] methods for quadcopter hovering, although most of the validated methods are based on the PID framework [1][4][6]....
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...Many variations of classical and modern control algorithms can be found in literature that have been modified for quadcopters, such as PID control [1][2], LQ control [1], backstepping control [3][4], feedback linearisation [1], adaptive nonlinear control [5], nonlinear PD control [1], sliding-mode control [5] and PD(2) control [1]....
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DOI•
01 Jan 2007
TL;DR: In this article, a mathematical model for simulation and control of a minibrobot is presented. And the methodology is subsequently applied to design an autonomous quadrotor named OS4, which has all the necessary sensors for autonomous operation.
Abstract: This thesis is about modelling, design and control of Miniature Flying Robots (MFR) with a focus on Vertical Take-Off and Landing (VTOL) systems and specifically, micro quadrotors. It introduces a mathematical model for simulation and control of such systems. It then describes a design methodology for a miniature rotorcraft. The methodology is subsequently applied to design an autonomous quadrotor named OS4. Based on the mathematical model, linear and nonlinear control techniques are used to design and simulate various controllers along this work. The dynamic model and the simulator evolved from a simple set of equations, valid only for hovering, to a complex mathematical model with more realistic aerodynamic coefficients and sensor and actuator models. Two platforms were developed during this thesis. The first one is a quadrotor-like test-bench with off-board data processing and power supply. It was used to safely and easily test control strategies. The second one, OS4, is a highly integrated quadrotor with on-board data processing and power supply. It has all the necessary sensors for autonomous operation. Five different controllers were developed. The first one, based on Lyapunov theory, was applied for attitude control. The second and the third controllers are based on PID and LQ techniques. These were compared for attitude control. The fourth and the fifth approaches use backstepping and sliding-mode concepts. They are applied to control attitude. Finally, backstepping is augmented with integral action and proposed as a single tool to design attitude, altitude and position controllers. This approach is validated through various flight experiments conducted on the OS4.
631 citations
"A comparison between advanced model..." refers background or methods in this paper
...Even though most of the related research work was only applied to the simulation level, some control approaches have been validated experimentally, such as LQ [1][4], backstepping [4], and sliding-mode control [4] methods for quadcopter hovering, although most of the validated methods are based on the PID framework [1][4][6]....
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...However, hub forces, rolling moments, and gyroscopic effects can be neglected to remove the cross coupling and thus to simplify the dynamic equations [4]...
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...Many variations of classical and modern control algorithms can be found in literature that have been modified for quadcopters, such as PID control [1][2], LQ control [1], backstepping control [3][4], feedback linearisation [1], adaptive nonlinear control [5], nonlinear PD control [1], sliding-mode control [5] and PD(2) control [1]....
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TL;DR: The X-4 Flyer as mentioned in this paper is a 4.4 kg quadrotor with a 1.5 kg payload, which uses tuned plant dynamics with an onboard attitude controller to stabilise flight.
492 citations
"A comparison between advanced model..." refers background in this paper
...Many variations of classical and modern control algorithms can be found in literature that have been modified for quadcopters, such as PID control [1][2], LQ control [1], backstepping control [3][4], feedback linearisation [1], adaptive nonlinear control [5], nonlinear PD control [1], sliding-mode control [5] and PD(2) control [1]....
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05 Dec 2005
TL;DR: Integral sliding mode and reinforcement learning control are presented as two design techniques for accommodating the nonlinear disturbances of outdoor altitude control that result in greatly improved performance over classical control techniques.
Abstract: The Stanford Testbed of Autonomous Rotorcraft for Multi-Agent Control (STARMAC) is a multi-vehicle testbed currently comprised of two quadrotors, also called X4-flyers, with capacity for eight. This paper presents a comparison of control design techniques, specifically for outdoor altitude control, in and above ground effect, that accommodate the unique dynamics of the aircraft. Due to the complex airflow induced by the four interacting rotors, classical linear techniques failed to provide sufficient stability. Integral sliding mode and reinforcement learning control are presented as two design techniques for accommodating the nonlinear disturbances. The methods both result in greatly improved performance over classical control techniques.
291 citations
"A comparison between advanced model..." refers background in this paper
...Many variations of classical and modern control algorithms can be found in literature that have been modified for quadcopters, such as PID control [1][2], LQ control [1], backstepping control [3][4], feedback linearisation [1], adaptive nonlinear control [5], nonlinear PD control [1], sliding-mode control [5] and PD(2) control [1]....
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