Extended Kalman Filtering Applied to a Two-Axis Robotic Arm with Flexible Links

Lertpiriyasuwat, V.; Berg, Martin C.; Buffinton, Keith W.

doi:10.1177/02783640022066851

Cited by 22 publications

(12 citation statements)

References 27 publications

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“…(15) can be rewritten as:χ χ χ e = F (χ χ χ e , U) + M ω ω ω (27) with M = Z J ∈ IR 18×15 . The observation model of the system is directly expressed in a discrete domain [24]- [36] as:…”

Section: Model Extension: Augmented State Representationmentioning

confidence: 99%

Nonlinear Control of a Multilink Aerial System and ASEKF-Based Disturbances Compensation

Castillo-Zamora

Escareño

Boussaada

et al. 2021

IEEE Trans. Aerosp. Electron. Syst.

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The actual paper presents the modeling and control of a multi-link unmanned aerial system whose dynamics is computed by means of the Euler-Lagrange energy-based approach. The aforementioned system is subjected to lumped disturbances which comprise external disturbances and parametric uncertainties. An Augmented-State Extended Kalman Filter intended to estimate endogenous and exogenous uncertainties is conceived and trajectory-tracking controller fulfilling Lyapunov asymptotic stability is synthesized. A simulation stage is conducted to validate the effectiveness of the proposal.

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Section: Model Extension: Augmented State Representationmentioning

confidence: 99%

Nonlinear Control of a Multilink Aerial System and ASEKF-Based Disturbances Compensation

Castillo-Zamora

Escareño

Boussaada

et al. 2021

IEEE Trans. Aerosp. Electron. Syst.

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“…where x k (to be estimated) and z k (measured by the sensors) are the actual state and measurement vectors and A, W , H , and V are Jacobian matrices defined at each iteration. Let us define the prediction error (16) and the measurement residual…”

Section: Extended Kalman Filtermentioning

confidence: 99%

Design of a Kalman filter for rotary shape memory alloy actuators

Elahinia

Ahmadian

Ashrafiuon

2004

Smart Mater. Struct.

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Measuring the state variables of systems actuated by shape memory alloys (SMAs) is normally a difficult task because of the small diameter of the SMA wires. In such cases, as an alternative, observers are used to estimate the state vector. This paper presents an extended Kalman filter (EKF) for estimation of the state variables of a single-degree-of-freedom rotary manipulator actuated by an SMA wire. This model-based state estimator has been chosen because it works well with noisy measurements and model inaccuracies. The SMA phenomenological models, that are mostly used in engineering applications, have both model and parameter uncertainties; this makes the EKF a natural choice for SMA-actuated systems. A state space model for the SMA manipulator is presented. The model includes nonlinear dynamics of the manipulator, a thermomechanical model of the SMA, and the electrical and heat transfer behavior of the SMA wire. In an experimental setup the angular position of the arm is the only state variable that is measured besides the voltage applied to the SMA wire. The other state variables of the system are the arm's angular velocity and the SMA wire's stress and temperature, which are not available experimentally due to difficulty in measuring them. Accurate estimation of the state variables enables design of a control system that provides better system performance. At each time step, the estimator uses the SMA wire's voltage measurement to predict the state vector which is corrected as necessary according to the measured angular position of the arm. The input and output of the model are used for the EKF simulations. The state variables collected through model simulations are also used to evaluate the performance of the EKF. Several EKF simulations presented in this paper show accurate and robust performance of the estimator, for different control inputs.

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“…For flexible link robots the Kalman filter has been investigated in Li and Chen [2001] for a single link, where the joint angle and the acceleration of the tool are used as measurements. Moreover, in Lertpiriyasuwat et al [2000] the extended Kalman filter has been used for a two link manipulator using the joint angles and the tool position as measurements. In both cases, the manipulator is operating in a plane perpendicular to the gravity field.…”

Section: Introductionmentioning

confidence: 99%

Sensor Fusion and Control Applied to Industrial Manipulators

Axelsson

2014

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One of the main tasks for an industrial robot is to move the end-effector in a predefined path with a specified velocity and acceleration. Different applications have different requirements of the performance. For some applications it is essential that the tracking error is extremely small, whereas other applications require a time optimal tracking. Independent of the application, the controller is a crucial part of the robot system. The most common controller configuration uses only measurements of the motor angular positions and velocities, instead of the position and velocity of the end-effector. The development of new cost optimised robots has introduced unwanted flexibilities in the joints and the links. The consequence is that it is no longer possible to get the desired performance and robustness by only measuring the motor angular positions.This thesis investigates if it is possible to estimate the end-effector position using Bayesian estimation methods for state estimation, here represented by the extended Kalman filter and the particle filter. The arm-side information is provided by an accelerometer mounted at the end-effector. The measurements consist of the motor angular positions and the acceleration of the end-effector. In a simulation study on a realistic flexible industrial robot, the angular position performance is shown to be close to the fundamental Cramér-Rao lower bound. The methods are also verified in experiments on an abb irb4600 robot, where the dynamic performance of the position for the end-effector is significantly improved. There is no significant difference in performance between the different methods. Instead, execution time, model complexities and implementation issues have to be considered when choosing the method. The estimation performance depends strongly on the tuning of the filters and the accuracy of the models that are used. Therefore, a method for estimating the process noise covariance matrix is proposed. Moreover, sampling methods are analysed and a low-complexity analytical solution for the continuous-time update in the Kalman filter, that does not involve oversampling, is proposed.The thesis also investigates two types of control problems. First, the norm-optimal iterative learning control (ilc) algorithm for linear systems is extended to an estimation-based norm-optimal ilc algorithm where the controlled variables are not directly available as measurements. The algorithm can also be applied to non-linear systems. The objective function in the optimisation problem is modified to incorporate not only the mean value of the estimated variable, but also information about the uncertainty of the estimate. Second, H ∞ controllers are designed and analysed on a linear four-mass flexible joint model. It is shown that the control performance can be increased, without adding new measurements, compared to previous controllers. Measuring the end-effector acceleration increases the control performance even more. A non-linear model has to be used to describe the behaviour of ...

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Extended Kalman Filtering Applied to a Two-Axis Robotic Arm with Flexible Links

Cited by 22 publications

References 27 publications

Nonlinear Control of a Multilink Aerial System and ASEKF-Based Disturbances Compensation

Nonlinear Control of a Multilink Aerial System and ASEKF-Based Disturbances Compensation

Design of a Kalman filter for rotary shape memory alloy actuators

Sensor Fusion and Control Applied to Industrial Manipulators

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