Attitude estimation with accelerometers and gyros using fuzzy tuned Kalman filter

Kang, Chul Woo; Park, Chan Gook

doi:10.23919/ecc.2009.7074977

Cited by 45 publications

(22 citation statements)

References 11 publications

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“…Hence, over time the orientation measured by the gyroscope deviates from the actual orientation. A filter such as a Kalman filter could be used to combine the accelerometer and gyroscope readings, producing smoother and drift-free readings [4,5]. The yaw reading of the gyroscope, however, cannot be corrected for drift unless an additional inertial sensor is used (a magnetometer), as an accelerometer does not measure rotation about the yaw axis (see [6] for further information on inertial sensors) A gyroscope has been included in addition to an accelerometer in the control unit specifically so that yaw axis movement can be measured.…”

Section: Inertial Sensingmentioning

confidence: 99%

Inertial sensor based wireless control of a robotic arm

Sekhar¹,

Musalay²,

Krishnamurthy³

et al. 2012

2012 IEEE International Conference on Emerging Signal Processing Applications

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This paper describes and details the development of a wireless motion sensing control unit, whose operation is based on inertial sensors, and extends its application to the control of an anthropomorphic robotic arm. Accelerometers and a gyroscope are used to measure the orientation of the users lower arm and this data is transmitted wirelessly to a receiver where processing is carried out. The robotic arm is programmed to mimic the movements of the users arm. Data processing has been carried out on low-cost microcontrollers and the use of filters to smoothen the operation of the arm has been explored. The data transmitted from the control unit has been analysed and plotted in real time on a computer using MATLab, and the orientation of the control unit tracked and displayed. This system has potential application in fields including industrial operations, remote operations in hazardous areas, medicine and undersea recovery.

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Section: Inertial Sensingmentioning

confidence: 99%

Inertial sensor based wireless control of a robotic arm

Sekhar¹,

Musalay²,

Krishnamurthy³

et al. 2012

2012 IEEE International Conference on Emerging Signal Processing Applications

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“…The integration-based attitude estimation used in this work requires much less computational power than other estimators based on Bayesian statistics such as [5], [11], [14], [20]. However, it has a serious drawback; integration drift gradually accumulates biased measurements and generates a large amount of error as time elapses.…”

Section: Attitude Estimationmentioning

confidence: 99%

Flight control for target seeking by 13 gram ornithopter

Baek

Bermudez

Fearing

2011

2011 IEEE/RSJ International Conference on Intelligent Robots and Systems

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Abstract-Recent advances in small-scale flapping-wing micro aerial vehicles have extended the capabilities of flight control for a number of applications, such as intelligence, surveillance, and reconnaissance activities. In this work, we demonstrate autonomous flight control of a 13 gram ornithopter capable of flying toward a target without remote assistance. For autonomous flight control, we developed 1.0 gram control electronics integrated with a microcontroller, inertial and visual sensors, communication electronics, and motor drivers. We also developed a simplified aerodynamic model of ornithopter flight to reduce the order of the control system. With the aerodynamic model and the orientation estimation from on-board inertial sensors, we present flight control of an ornithopter capable of flying toward a target using onboard sensing and computation only. To this end, we developed a dead-reckoning algorithm to recover from the temporary loss of the target which can occur with a visual sensor with a narrow field of view. As a result, the 28 cm wing-span ornithopter flying toward a target landed within a radius of 0.5 m from the target with more than 85% success (N = 20).

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“…However, IAE and MMAE methods suffer from the disadvantage of large window requirement and lack in convergence due to false model selection respectively [lO]. Furthermore, there have been several advancements to these approaches engaging artificial intelligence techniques, like Fuzzy logic and Neural network as shown in [7,[11][12][13].…”

Section: Introductionmentioning

confidence: 99%

Tuning of Extended Kalman filter using Human Opinion Dynamics based optimization

Hussain

Poddar

Ailneni

et al. 2015

2015 International Conference on Industrial Instrumentation and Control (ICIC)

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Kalman filter is a well-known technique for optimal state estimation and is widely used for its applicability in different fields. Different derivatives of Kalman filter have been proposed in the past to consider the non-linear aspects of system and measurement model. However, these estimation techniques require precise tuning of process and measurement noise covariance matrices for a given system. This tuning is not only a non-trivial process, but also requires engineering intuition and huge number of Monte Carlo Simulations of the system noise, which at times takes days to freeze. In this paper, a Human Opinion Dynamics (HOD) based optimization of Extended Kalman Filter (EKF) has been proposed for obtaining the tuning parameters. Using these tuning parameters, EKF simulations are carried out for a permanent magnet synchronous motor system model, and thus obtained state estimates are compared with the state estimates obtained from Particle Swarm Optimization (PSO) based tuning method. The simulation results depicts HOD based technique being comparable with the PSO based technique on accuracy grounds and out performs in terms of convergence and ease of implementation.

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Attitude estimation with accelerometers and gyros using fuzzy tuned Kalman filter

Cited by 45 publications

References 11 publications

Inertial sensor based wireless control of a robotic arm

Inertial sensor based wireless control of a robotic arm

Flight control for target seeking by 13 gram ornithopter

Tuning of Extended Kalman filter using Human Opinion Dynamics based optimization

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