Explicit Complementary Filter Research Articles

Position Control Using Trajectory Tracking and State Estimation of a Quad-rotor

Quad-rotor aircrafts are unmanned aerial vehicles that have gained significant popularity in recent years and have been developed for use in many areas. Such vehicles are capable of vertical take-off and landing and are used in various applications. To operate a quad-rotor aircraft efficiently and safely, fundamental issues such as mathematical modeling, control, and state estimation need to be studied. Mathematical modeling involves creating a holistic model of the various subsystems of the aircraft including aerody-namic, kinematic, dynamic and control systems. The control system is a mechanism used for the aircraft to perform the desired movements. State estimation techniques are used to obtain and predict information about the state of the aircraft. This study includes position control using a trajectory generation algorithm. Attitude estimation of the quad-rotor is improved with the Explicit Complementary Filter (ECF) and the state estimations is improved with the Extended Kalman Filter (EKF). Different from other studies, the results are obtained by feeding the model with a state estimation filter. The performances of the filters used for state estimation are compared.

Yawing stability and manipulative approach design for maglev car based on active disturbance rejection control

AbstractIn the present work, to realize the stable static suspension of a new type of electrodynamic wheel (EDW) magnetic levitation (maglev) car without track constraints on the conductor plate. Design and analysis of the active disturbance rejection control and explicit complementary filter (ADRC‐ECF) have been presented to solve the maglev car's yaw angle disturbance rejection under low lateral damping characteristics. Simple and efficient ECF helps to filter the high and low frequency noise generated by EDWs when rotating at high speed, to obtain accurate data in real‐time. Design and build the hardware control system for maglev car, and conduct experimental comparisons with PID controller to verify the effectiveness of suppressing yaw angle disturbances. Based on simulation and experimental results, it is shown that both PID and ADRC‐ECF can improve the stable yaw angle convergence of maglev car under external disturbances. However, ADRC performs better in resisting disturbances than PID and can achieve more accurate fixed angle steering. This paper is the first attempt to control maglev car on a trackless conductor plate for precise steering and anti‐disturbance, which is anticipated to have some reference significance in this field.

How Magnetic Disturbance Influences the Attitude and Heading in Magnetic and Inertial Sensor-Based Orientation Estimation.

With the advancements in micro-electromechanical systems (MEMS) technologies, magnetic and inertial sensors are becoming more and more accurate, lightweight, smaller in size as well as low-cost, which in turn boosts their applications in human movement analysis. However, challenges still exist in the field of sensor orientation estimation, where magnetic disturbance represents one of the obstacles limiting their practical application. The objective of this paper is to systematically analyze exactly how magnetic disturbances affects the attitude and heading estimation for a magnetic and inertial sensor. First, we reviewed four major components dealing with magnetic disturbance, namely decoupling attitude estimation from magnetic reading, gyro bias estimation, adaptive strategies of compensating magnetic disturbance and sensor fusion algorithms. We review and analyze the features of existing methods of each component. Second, to understand each component in magnetic disturbance rejection, four representative sensor fusion methods were implemented, including gradient descent algorithms, improved explicit complementary filter, dual-linear Kalman filter and extended Kalman filter. Finally, a new standardized testing procedure has been developed to objectively assess the performance of each method against magnetic disturbance. Based upon the testing results, the strength and weakness of the existing sensor fusion methods were easily examined, and suggestions were presented for selecting a proper sensor fusion algorithm or developing new sensor fusion method.

Implementation of a Nonlinear Attitude Estimator for Aerial Robotic Vehicles

Attitude estimation is a key component of the avionics suite of any aerial robotic vehicle. This paper details theoretical and practical solutions in order to obtain a robust nonlinear attitude estimator for flying vehicles equipped with low-cost sensors. The attitude estimator is based on a nonlinear explicit complementary filter that has been significantly enhanced with an effective gyro-bias compensation via the design of an anti-windup nonlinear integrator. A measurement decoupling strategy is proposed in order to make roll and pitch estimation robust to magnetic disturbances that are known to cause errors in yaw estimation. In addition, this paper discusses the fixed-point numerical implementation of the algorithm. Finally, simulation and experimental results confirm the performance of the proposed method.

Integration Filter for APS, DVL, IMU and Pressure Gauge for Underwater Vehicles

High-accuracy underwater navigation is important in order to automate motion control of remotely operated vehicles (ROVs). An observer that estimates the vehicle states (position, velocity, attitude and turn rates) is proposed for closed-loop control. Measurements from an acoustic positioning system (APS), a Doppler velocity log (DVL), an inertial measurement unit (IMU) and a pressure gauge (PG) are used in the proposed observer. The observer is divided into an attitude observer and a translational motion observer with interconnections. The attitude observer is an explicit complementary filter (ECF). Results from simulation are presented.

Nonlinear Complementary Filters on the Special Orthogonal Group

This paper considers the problem of obtaining good attitude estimates from measurements obtained from typical low cost inertial measurement units. The outputs of such systems are characterized by high noise levels and time varying additive biases. We formulate the filtering problem as deterministic observer kinematics posed directly on the special orthogonal group SO (3) driven by reconstructed attitude and angular velocity measurements. Lyapunov analysis results for the proposed observers are derived that ensure almost global stability of the observer error. The approach taken leads to an observer that we term the direct complementary filter. By exploiting the geometry of the special orthogonal group a related observer, termed the passive complementary filter, is derived that decouples the gyro measurements from the reconstructed attitude in the observer inputs. Both the direct and passive filters can be extended to estimate gyro bias online. The passive filter is further developed to provide a formulation in terms of the measurement error that avoids any algebraic reconstruction of the attitude. This leads to an observer on SO(3), termed the explicit complementary filter, that requires only accelerometer and gyro outputs; is suitable for implementation on embedded hardware; and provides good attitude estimates as well as estimating the gyro biases online. The performance of the observers are demonstrated with a set of experiments performed on a robotic test-bed and a radio controlled unmanned aerial vehicle.