Complementary Filter Algorithm Research Articles

Master-Slave Control System for Virtual-Physical Interactions Using Hands.

Among the existing technologies for hand protection, master-slave control technology has been extensively researched and applied within the field of safety engineering to mitigate the occurrence of safety incidents. However, it has been identified through research that traditional master-slave control technologies no longer meet current production and lifestyle needs, and they have even begun to pose new safety risks. To resolve the safety risks exposed by traditional master-slave control, this research fuses master-slave control technology for hands with virtual reality technology, and the design of a master-slave control system for hands based on virtual reality technology is investigated. This study aims to realize the design of a master-slave control system for virtual-physical interactions using hands that captures the position, orientation, and finger joint angles of the user's hand in real time and synchronizes the motion of the slave interactive device with that of a virtual hand. With amplitude limiting, jitter elimination, and a complementary filtering algorithm, the original motion data collected by the designed glove are turned into a Kalman-filtering-algorithm-based driving database, which drives the synchronous interaction of the virtual hand and a mechanical hand. As for the experimental results, the output data for the roll, pitch, and yaw were in the stable ranges of -0.1° to 0.1°, -0.15° to 0.15°, and -0.15° to 0.15°, respectively, which met the accuracy requirements for the system's operation under different conditions. More importantly, these data prove that, in terms of accuracy and denoising, the data-processing algorithm was relatively compatible with the hardware platform of the system. Based on the algorithm for the virtual-physical interaction model, the authors introduced the concept of an auxiliary hand into the research, put forward an algorithmic process and a judgement condition for the stable grasp of the virtual hand's, and solved a model-penetrating problem while enhancing the immersive experience during virtual-physical interactions. In an interactive experiment, a dynamic accuracy test was run on the system. As shown by the experimental data and the interactive effect, the system was satisfactorily stable and interactive.

Energy-efficient and reconfigurable complementary filter based on analog–digital hybrid computing with SnS2 memtransistor

Sensor fusion is a widely exploited technique that combines data from two or more sensors to improve the accuracy to a level that cannot be achieved using a single sensor alone. Algorithms for sensor fusion are generally executed on conventional digital computing platforms; however, these algorithms impose a burden on small electrical systems with limited battery capacities and computing resources. In this study, we demonstrated an analog–digital hybrid computing platform based on an SnS2 memtransistor for energy-efficient and reconfigurable sensor fusion with a complementary filter algorithm. We experimentally verified that the power consumption of our hybrid computing-based complementary filter is only half that of the traditional software-based complementary filter, even with the same accuracy.

A Two Wheel Self-Balancing Vehicle

Abstract This paper deals with the development of the equation of motion and practical implementation of low cost two-wheel self-balancing model of a Segway transporter. The experimental model of cart was designed and made under this study. Nonlinear equations of motion of real model and linearized model were derived. To develop the mathematical model, Matlab/Simulink was applied. The mechanical part was implemented into Simulink, and a DC motor was considered as a linear system. The real model was tested for its balance by implementation of a control algorithm consisting of a complementary filter and PID algorithm on an Arduino development board with peripheral devices. The fully functional self-balancing model was used as a demonstration in the teaching process of the Mechatronics courses.

Time–Frequency Extraction Model Based on Variational Mode Decomposition and Hilbert–Huang Transform for Offshore Oil Platforms Using MIMU Data

Time–frequency extraction is a key issue to understand structural symmetry of dynamic responses of offshore oil platforms for early warning during drilling operations. Current popular methods for signal characteristics extraction can only obtain the attributes with a single dimension or poor precision. To solve this, a combined Hilbert–Huang transform (HHT) and variational mode decomposition (VMD) method is proposed to extract multidimensional dynamic response characteristics of time, frequency, and energy of offshore oil platforms. Based on the extracted time–frequency–energy information, the frequency-domain integration approach (FDIA) can be applied to calculate the displacement using accelerometer in the micro inertial measurement unit (MIMU). A complementary filtering algorithm was designed to measure the torsion angle of platforms using six degrees of freedom data from the MIMU to obtain the torsion angle information. The performance of the proposed method was validated using a series of simulation shaking-table tests and a field test conducted on an offshore oil platform at Dongying City, Shandong Province, China. During the field test, seven out of eight collisions were detected in the frequency range 5 Hz to 12 Hz. The intensity of the fifth collision was the highest, and the maximum displacement obtained by the accelerometer was 6 mm. In addition, the results show a correlation between the axes of the accelerometer and gyroscope, and their combination can measure a torsion angle up to 1.1°.

Navigation control of Drone using Hand Gesture based on Complementary Filter Algorithm

One of the most important things to use UAV is navigation control. Navigation control is a way to adjust the direction of the quadcopter movements according to the command of the pilot. Natural User Interface (NUI) is a new way to interact with a system as navigation control. In this study, a wearable device was made that can detect hand gestures and gives instructions to the Dji Tello drone. The MPU6050 sensor is used to provide response in two dimensional axes. The complementary filter implements low pass filter on the accelerometer and integrates the value of the gyroscope with the previous angle output. After that, the value will be fed to the high pass filter. The results of the two filters will obtain stable angle, by adjusting the filter coefficient and the sampling time. The aim of complementary filter method is to reduce noise in angular transformation when the pilot makes hand gestures. Based on the experiments, the results show that hand gestures could give command for Dji Tello drone movements successfully. Therefore, it has been proven that the hand gesture can be used for the navigation control system on the Dji Tello drone.

Development of a Wearable Glove System with Multiple Sensors for Hand Kinematics Assessment.

In traditional hand function assessment, patients and physicians always need to accomplish complex activities and rating tasks. This paper proposes a novel wearable glove system for hand function assessment. A sensing system consisting of 12 nine-axis inertial and magnetic unit (IMMU) sensors is used to obtain the acceleration, angular velocity, and geomagnetic orientation of human hand movements. A complementary filter algorithm is applied to calculate the angles of joints after sensor calibration. A virtual hand model is also developed to map with the glove system in the Unity platform. The experimental results show that this glove system can capture and reproduce human hand motions with high accuracy. This smart glove system is expected to reduce the complexity and time consumption of hand kinematics assessment.

Implementasi Algoritma Complementary Filter untuk Merancang Bangun Interaksi Manusia dan Smartphone Menggunakan Gerakan Kepala

This study proposes an approach to design and build interaction systems for smartphone users with hand disabilities using the user's head movements through the implementation of the fusion complementary filter algorithm to get the angle of orientation and position of the user’s head. The main objective of this experimental research is to get the best constant function of complementary filters that have the highest accuracy result. The results of this study indicate that the fusion complementary filter algorithm can be used to measure the angle of the user's head movements using a combination of 3 sensors; namely, the accelerometer, gyroscope, and magnetometer, with the best constants function are 0.99 for the gyroscope and 0.01 for the accelerometer. Complementary filter performance results using fusion of 3 sensors to measure the user's head angle obtained from the calculation of the root mean squared error (RMSE) with an average value of 0.364 on the x-axis (roll), 0.578 on the y-axis (pitch), and 0.767 on the z-axis (yaw).

Indoor Positioning Integrating PDR/Geomagnetic Positioning Based on the Genetic-Particle Filter

This paper proposes a fusion indoor positioning method that integrates the pedestrian dead-reckoning (PDR) and geomagnetic positioning by using the genetic-particle filter (GPF) algorithm. In the PDR module, the Mahony complementary filter (MCF) algorithm is adopted to estimate the heading angles. To improve geomagnetic positioning accuracy and geomagnetic fingerprint specificity, the geomagnetic multi-features positioning algorithm is devised and five geomagnetic features are extracted as the single-point fingerprint by transforming the magnetic field data into the geographic coordinate system (GCS). Then, an optimization mechanism is designed by using gene mutation and the method of reconstructing a particle set to ameliorate the particle degradation problem in the GPF algorithm, which is used for fusion positioning. Several experiments are conducted to evaluate the performance of the proposed methods. The experiment results show that the average positioning error of the proposed method is 1.72 m and the root mean square error (RMSE) is 1.89 m. The positioning precision and stability are improved compared with the PDR method, geomagnetic positioning, and the fusion-positioning method based on the classic particle filter (PF).

Estimating Three-Dimensional Body Orientation Based on an Improved Complementary Filter for Human Motion Tracking.

Rigid body orientation determined by IMU (Inertial Measurement Unit) is widely applied in robotics, navigation, rehabilitation, and human-computer interaction. In this paper, aiming at dynamically fusing quaternions computed from angular rate integration and FQA algorithm, a quaternion-based complementary filter algorithm is proposed to support a computationally efficient, wearable motion-tracking system. Firstly, a gradient descent method is used to determine a function from several sample points. Secondly, this function is used to dynamically estimate the fusion coefficient based on the deviation between measured magnetic field, gravity vectors and their references in Earth-fixed frame. Thirdly, a test machine is designed to evaluate the performance of designed filter. Experimental results validate the filter design and show its potential of real-time human motion tracking.

Manned self‐balanced vehicle control system based on pressure sensors for steering

A new steering mechanical structure and an improved control method of the manned self-balanced vehicle are presented in this Letter. An effective method is introduced to calculate the real-time inclination of the vehicle. Then, the integrating inclination of the vehicle could be obtained by the complementary filter and proportional derivative (PD) algorithm. In addition, the control system could be regarded as a linear superposition of three parts, including the balance control, the speed control and the steering control. Moreover, the proportional, integral and derivative (PID) parameters of the whole control system are different under different operational conditions. Furthermore, the pressure sensors placed on the left and right pedals could detect the change of the weight, which simplifies the mechanical structure and improves the flexibility of the manned steering. The experiments in the prototype are designed to prove the steering structure and the improved method.

A Fast Adaptive-Gain Complementary Filter Algorithm for Attitude Estimation of an Unmanned Aerial Vehicle

A novel fast adaptive-gain complementary filter algorithm is developed for Unmanned Aerial Vehicle (UAV) attitude estimation. This approach provides an accurate, robust and simple method for attitude estimation with minimised attitude errors and reduced computation. UAV attitude data retrieved from accelerometer data is transformed to the solution of a linearly discrete dynamic system. A novel complementary filter is designed to fuse accelerometer and gyroscope data, with a self-adjusted gain to achieve a good performance in accuracy. The performance of the proposed algorithm is compared with an Adaptive-gain Complementary Filter (ACF) and Extended Kalman Filtering (EKF). Simulation and experimental results show that the accuracy of the proposed filter has the same performance as an EKF in high dynamic operating conditions. Therefore, the proposed algorithm can balance accuracy and time consumption, and it has a better price/performance ratio in engineering applications.

Sensor Fusion Algorithm by Complementary Filter for Attitude Estimation of Quadrotor with Low-Cost IMU

This paper proposes a sensor fusion algorithm by complementary filter technique for attitude estimation of quadrotor UAV using low-cost MEMS IMU. Angular rate from gyroscope tend to drift over a time while accelerometer data is commonly effected with environmental noise. Therefore, high frequency gyroscope signal and low frequency accelerometer signal is fused using complementary filter algorithm. The complementary filter scaling factor K1=0.98 and K2=0.02 are used to merge both gyro and accelerometer. The results show that the smooth roll, pitch and yaw attitude angle can be obtained from the low cost IMU by using proposed sensor fusion algorithm.

Multi-Sensor Based Online Attitude Estimation and Stability Measurement of Articulated Heavy Vehicles.

Articulated wheel loaders used in the construction industry are heavy vehicles and have poor stability and a high rate of accidents because of the unpredictable changes of their body posture, mass and centroid position in complex operation environments. This paper presents a novel distributed multi-sensor system for real-time attitude estimation and stability measurement of articulated wheel loaders to improve their safety and stability. Four attitude and heading reference systems (AHRS) are constructed using micro-electro-mechanical system (MEMS) sensors, and installed on the front body, rear body, rear axis and boom of an articulated wheel loader to detect its attitude. A complementary filtering algorithm is deployed for sensor data fusion in the system so that steady state margin angle (SSMA) can be measured in real time and used as the judge index of rollover stability. Experiments are conducted on a prototype wheel loader, and results show that the proposed multi-sensor system is able to detect potential unstable states of an articulated wheel loader in real-time and with high accuracy.

An error-based micro-sensor capture system for real-time motion estimation**Research supported by the National Natural Science Foundation of China (Nos. 61431017, 81272166).

A wearable micro-sensor motion capture system with 16 IMUs and an error-compensatory complementary filter algorithm for real-time motion estimation has been developed to acquire accurate 3D orientation and displacement in real life activities. In the proposed filter algorithm, the gyroscope bias error, orientation error and magnetic disturbance error are estimated and compensated, significantly reducing the orientation estimation error due to sensor noise and drift. Displacement estimation, especially for activities such as jumping, has been the challenge in micro-sensor motion capture. An adaptive gait phase detection algorithm has been developed to accommodate accurate displacement estimation in different types of activities. The performance of this system is benchmarked with respect to the results of VICON optical capture system. The experimental results have demonstrated effectiveness of the system in daily activities tracking, with estimation error 0.16 ± 0.06 m for normal walking and 0.13 ± 0.11 m for jumping motions.

Attitude determination by combining arrays of MEMS accelerometers, gyros, and magnetometers via quaternion‐based complementary filter

AbstractIn this study, an Attitude and Heading Reference System (AHRS) consisting of 5 modules is designed where each module has a triaxial gyroscope, accelerometer, and magnetometer. First, a method based on the Levenberg‐Marquardt algorithm (LMA) is utilized to correct the bias error, scale factor and axes nonorthogonality. Also, the data from the 5 modules of AHRS are ensemble averaged to reduce the adverse effects of high‐frequency noises. Then, the obtained trends are used in an orientation estimation algorithm based on a complementary filter algorithm. In this algorithm, the dynamical accelerations are first decreased via a low‐pass filter. Afterwards, it is determined using an algorithm whether the system is experiencing magnetic distortion or not. If distortion is verified, magnetometers' data are discarded, as it will introduce noticeable error in estimating heading angle. In this case, the heading angle starts to diverge; however, employing 5 modules in the system decreases divergence rate noticeably, such that after 2 minutes in quasi‐static conditions, the pitch, roll, and heading angles' errors decrease, respectively, from 36.195°, 23.201°, and 12.541° when only 1 module is used to 2.511°, 3.972°, and 0.984° when all 5 modules are used. Moreover, in dynamical conditions, these errors decrease from 37.916°, 13.633°, and 13.071° to 6.514°, 5.961°, and 0.284°. Once the distortion is removed from magnetic field, the magnetometers' data are used again to correct the heading error. The obtained results show that the root mean square (RMS) errors of pitch, roll, and heading angles in quasi‐static conditions are 0.536°, 0.323°, and 0.601°, whereas in dynamical condition, they are 1.267°, 1.535°, and 0.994°, respectively.

Altitude data fusion utilising differential measurement and complementary filter

In plenty of researches and real systems, barometer and accelerometer which can provide altitude and acceleration measurements, respectively, are utilised in altitude estimation of small unmanned aerial vehicle. These sensors can draw on their merits and improve the accuracy of altitude estimation. The altitude measurement of barometer has the drift problem as one of its disadvantages which could deteriorate the performance of altitude estimation. In addition, barometer and accelerometer both have stochastic error which reduce the accuracy of altitude estimation. A novel scenario is proposed by utilising differential altitude measurement and complementary filter to deal with above issues. It is accurate and suitable for real-time application. In this scenario, altitude drift is reduced by using differential altitude measurement, and the stochastic error of altitude is minimised by using complementary filter algorithm. Stair and flight experiments are implemented to test this scenario. The experimental results indicate that it can provide good dynamic performance and flexibility for altitude estimation.

Design and Implementation of an AHRS Based on Gauss Newton and Complementary Filtering Algorithm

This paper presents design and implementation of an attitude and heading reference system (AHRS) based on Gauss Newton and Complementary Filtering algorithm (CF). The algorithm uses data measured from the MEMS sensor which contains a three-axis magnetometer, a three-axis angular rate sensor, and a three-axis accelerometer. The filter represents rotations using quaternions rather than Euler angles, which eliminates the long-standing problem of singularities associated with attitude estimation. A process model for rigid body angular motions and angular rate measurements is defined. The process model converts angular rates into quaternion rates, which are integrated to obtain quaternions. The Gauss-Newton iteration algorithm is utilized to find the optimal quaternion that relates the measured accelerations and earth magnetic field in the body coordinate frame to calculated values in the earth coordinate frame. Then fuse the optimal quaternion with the quaternion updated from gyroscope and calculate the attitude angle based on the complementary filtering algorithm . Extensive testing of the filter have proved feasibility and acceptable performance of this AHRS design.

Balancing Robot Menggunakan Metode Kendali Proporsional Integral Derivatif

AbstrakPendulum terbalik memiliki pusat gravitasi yang berada diatas poros putar sehingga menyebabkan pendulum terbalik tidak seimbang. Suatu kendali khusus dibutuhkan agar pendulum seimbang dengan cara menggerakkan kereta beroda yang menjadi tumpuan dari pendulum. Penerapan pendulum terbalik dapat ditemui pada balancing robot. Tujuan dari penelitian ini adalah merancang bangun sebuah sistem pengendalian robot dengan dua roda menggunakan sistem kendali untuk membuat robot yang seimbang (balancing robot). Sistem ini mempunyai masukan akselerometer yang digunakan untuk mengukur percepatan sudut (m/s2) dan giroskop untuk mengukur kecepatan sudut (rad/s). Luaran dari akselerometer dan giroskop digabungkan dengan metode complementary filter untuk mendapatkan nilai sudut. Sudut yang diperoleh kemudian dibandingkan dengan set point yang nilainya 0o. Nilai selisih dari set point dan sudut complementary filter diolah menggunakan metode kendali Proporsional Integral Derivatif. Proses kendali PID ini diprogram pada Arduino IDE yang hasilnya diumpankan ke motor DC untuk mengatur kecepatan putar motor DC. Untuk arah putar motor DC ditentukan apabila sudut complementary filter kurang dari nol, maka motor akan berputar mundur. Sedangkan jika sudut complementary filter lebih dari nol, maka motor akan berputar maju. Nilai konstanta PID berdasarkan hasil tuning dengan metode Ziegler-Nichols metode osilasi adalah Kp=1.5, Ki=0.75, Kd=1.85 dan nilai koefisien pada algoritma complementary filter adalah a=0.96. Kata kunci—inverted pendulum, balancing robot, kendali PID, IMU, complementary filter Abstract Center of gravity’s inverted pendulum is located above its pivot point therefore inverted pendulum is unstable. Specific control is needed so that inverted pendulum stable which is by move the cart where the pendulum is mounted. Inverted pendulum application can be found in balancing robot. The purpose of this research is to design a system to control a two wheeled robot using the control system to balance it.The inputs are accelerometer to measure angular acceleration (m/s2) and gyroskop to measure angular velocity (rad/s). The output’s of accelerometer and gyroscope are fused by complementary filter algorithm method to get the actual angle. The actual angle is then compared to set point which is 0o. The differences between set point and actual angle are processed using Proportional Integral Derivative control method. The process of PID control is programmed using Arduino IDE which its result is fed to DC motors. The direction of DC motors are determined by two conditions, if actual angle less than zero then DC motors will spin backwards. Whereas if actual angle more than zero then DC motors will spin forward. The PID control’s constans value based on Ziegler-Nichols Oscillation tuning method are Kp=1.5, Ki=0.75, Kd=1.875 and complementary filter’s coefficient is a=0.96. Keywords— inverted pendulum, balancing robot, PID control, IMU, complementary filter

A Kind of Small Quadcopter

This paper describes the Quadcopter’s body structure; principles of flight, hardware configuration, and analyzes the process of attitude algorithm adopting the complementary filter algorithm as well as the flight control algorithms.

Reliable orientation estimation for mobile motion capturing in medical rehabilitation sessions based on inertial measurement units

Fully mobile and wireless motion capturing is a mandatory requirement for undisturbed and non-reactive analysis of human movements. Inertial sensor platforms are used in applications like training session analysis in sports or rehabilitation, and allow non-restricted motion capturing. The computation of the required reliable orientation estimation based on the inertial sensor RAW data is a demanding computational task. Therefore, an analysis of the computational costs and achievable accuracy of a Kalman filter and a complementary filter algorithm is provided. Highly customized and thus low-power, wearable computation platforms require low-level, platform independent communication protocols and connectivity. State-of-the-art small sized commercial inertial sensors either lack the availability of an open, platform independent protocol, wireless connectivity or extension interfaces for additional sensors. Therefore, an extensible, wireless inertial sensor called Institute of Microelectronic Systems Inertial Measurement Unit (IM)2SU, featuring onboard inertial sensor fusion, for use in home based stroke rehabilitation is presented. Furthermore, a Quaternion based, singularity free orientation estimation accuracy error measure is proposed and applied. To evaluate orientation estimation accuracy an optical system is used as golden reference. Orientation estimation based on a Kalman filter and a complementary filter algorithm is evaluated. The proposed IMU provides high orientation estimation accuracy, is platform independent, offers wireless connection and extensibility and is low cost.