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kalman filter for motor control

Posted on December 6th, 2020

This function determines the optimal steady-state filter gain M based on the process noise covariance Q and the sensor noise covariance R. First specify the plant + noise model. Schematic diagram of the EKF loop, Figure 4. Now, the problem of interest is to estimate the state x(k) of the DC motor based on output measurements. induction motor drive methods using flux observers including Kalman filters. Control of the field-oriented induction motor model, 4. The proposed flatness-based control scheme with the use of Extended Kalman Filtering for estimation of the non-measurable parameters of the motor's state vector is depicted in Fig. To overcome the EKF flaws, the Unscented Kalman Filter can be also considered. 2006] Akin, B., Orguner, U., Ersak, A. Electric Power Systems Research, Implementation of Robust Wavelet-Neural-Network Sliding-Mode Control for Induction Servo Motor Drive, Backstepping Wavelet Neural Network Control for Indirect Field-Oriented Induction Motor Drive, An adaptive high-gain observer for nonlinear systems, High-gain observer based state and parameter estimation in nonlinear systems. Here, a new method that replaces the standard regression with a regression using the bivariate rank statistics is described. In this work a sensorless technique for controlling the stepper motor using a Kalman filter has been developed. The DC motor of Eq. 2003) and (Akin et al. 2009] Hilairet, M., Augerb, F., Berthelot, E. (, Julier et al. 5. Kalman Filtering can be applied to discrete-time state models of the form, where the state x(k) is a m -vector, w(k) is a m -element process noise vector and Φ is a m×m real matrix. 2010) the Unscented Kalman Filter is applied to state estimation for fault diagnosis of induction motors. in dynamic positioning of ships where the Kalman Filter estimates the position and the speed of the vessel and also environmental forces. In the control of robotic manipulators, which is actually control of the DC motors that rotate the robot's joints, the angle of each joint is usually measured with the use of an optical encoder. The inputs to EKF are computed based on the measured data as well as the disturbance (an external mechanical load). Kalman Filter-based control for DC and induction motors can have several applications for the design of industrial and robotic systems of improved performance. In this case, my partner and I used it for a class project for our Autonomous Robots class. Taking into account that several variables of the induction motor state vector (e.g. This site uses cookies. Since all state variables of the circuits describing the induction motor dynamics can be expressed as functions of y=(θ,ρ) and its derivatives it can be concluded that the induction motor is a differentially flat system. The Unscented Kalman Filter can be used for state estimation of nonlinear electric motors, such as the induction motor analyzed in Sections 3 and 4. The aforementioned system of Eq. strategies, the motor position and speed is estimated and used as a feedback signal for closed-loop speed control. In Section 7 the efficiency of the above mentioned Kalman Filter-based control schemes, for both the DC and induction motor models, is tested through simulation experiments. Numerical experiment indicates that convergence rate or the estimation accuracy of parameter estimates is much more improved compared with standard RPLR method.As an application of the proposed method to real data processing, the modeling on a day evolution of some medical time-series data is dealt. Schematic diagram of the UKF loop. Transformation to the dq reference frame is again performed, however this time there is no assumption about decoupling between the rotor speed dynamics and the magnetic flux dynamics. A model of the feedback system using DC motor consisting of rotational speed and armature current as states is used in the state-space form. Abstract: This work deals with the tuning of an Extended Kalman Filter for sensorless control of induction motors for electrical traction in automotive. The extended Kalman filter is employed to identify the speed of an induction motor and rotor flux based on the measured quantities such as stator currents and DC link voltage. 11 the sensorless controller succeeded asympotic elimination of the tracking error despite abrupt changes in the reference trajectory, or the existence of process and measurement noises. M.Tech, Signal Processing, Reva Institute of Technology, Scientist (Retd. state variable increments are normally computed from the observation increments by linear regression using the prior bivariate ensemble of the state and observation variable. If you have access to a journal via a society or association membership, please browse to your society journal, select an article to view, and follow the instructions in this box. The Kalman filter is an algorithm which operates recursively on streams of noisy input data to produce a statistically optimal estimate of the ... two wheel drive vehicle, odometer, motor control, sonar, integer matrix algebra, vectors, PID control and kalman filter. Kalman Filter. The paper has studied sensorless control, for DC and induction motors, using Kalman Filtering techniques. This paper presents a detailed analysis for the Lp-stability of tracking errors when the Kalman filter is used for tracking unknown time-varying parameters. In the outer loop, control of the magnetic flux is performed enabling decoupling between the motor's speed dynamics and the flux dynamics. (, Akin et al. INTRODUCTION The indirect field oriented control method is widely used for in- duction motor drives. ResearchGate has not been able to resolve any references for this publication. Following a linearization procedure, φ is expanded into Taylor series about x^: where Jφ(x) is the m×m Jacobian of φ calculated at x^(k): where x^−(k) is the estimation of the state vector x(k) before measurement at the k -th instant to be received and x^(k) is the updated estimation of the state vector after measurement at the k -th instant has been received. The state distribution is represented again by a Gaussian Random Variable but is now specified using a minimal set of deterministically chosen weighted sample points. The Extended Kalman Filter can give estimates of the non-measured state vector elements, i.e. Thus the convergence rate of the estimator must be acclerated to obtain the more accurate estimates. The Kalman filter is a special kind of observer which provides optimal estimation of the system states based on least-square techniques. The quantity w denotes the system variable while w(i), i=1,2,⋯,q are its derivatives (these can be for instance the elements of the system's state vector). (41) one can apply state feedback control. The papers establishing the mathematical foundations of Kalman type filters were published between 1959 and 1961. The email address and/or password entered does not match our records, please check and try again. A common problem in linear regression is that largely aberrant values can strongly influence the results. 17 to Fig. Parameter x2 of the state vector of the DC motor in state estimation with use of the Kalman Filter (a) when tracking a see-saw set-point (b) when tracking a sinusoidal setpoint, Figure 8. (. (54) is also applicable to the nonlinear DC motor model of Eq. The sampling period was taken to be Ts=0.01sec. The last two implementations include calculation of the kalman gain. master. The Unscented Kalman Filter can be also used in place of the Extended Kalman Filter and in the latter case there will be no need to compute Jacobian matrices. The camera capture the image of line laser reflected in front of the wheelchair to detect any existing obstacle on the wheelchair’s pathway based on the line shape of reflected line laser. AC motor circuit, with the a−b stator reference frame and the d−q rotor reference frame, The classical method for induction motors control is based on a transformation of the stator's currents (isα and isb) and of the magnetic fluxes of the rotor (ψrα and ψrb) to the reference frame d−q which rotates together with the rotor (Fig. ISA Transactions, Particle Filtering for State Estimation in Nonlinear Industrial Systems, Particle and Kalman filtering for fault diagnosis in DC motors, Sigma-point Kalman Filters and Particle Filters for integrated navigation of unmanned aerial vehicles, On Unscented Kalman Filtering for state estimation of continuous-time nonlinear systems, Flatness-based vehicle steering control strategy with SDRE feedback gains tuned via a sensitivity approach, Intelligent control of induction servo motor drive via wavelet neural network. 2004) the Unscented Kalman Filter (UKF) and Extended Kalman Filter (EKF) are analyzed and compared both experimentally and theoretically in the problem of non-linear state estimation for field-oriented sensorless control of AC drives. Next the nonlinear model of a field-oriented induction motor is examined and the motor's angular velocity is estimated by an Extended Kalman Filter which processes measurements of the rotor's angle. If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. International Journal of Systems Science, Particle Filtering for state estimation in industrial robotic systems, Adaptive fuzzy control of DC motors using state and output feedback. Kalman Filter T on y Lacey. (4) in its linearized form of Eq. The 1×2 Jacobian Jγ(x) is. (63). In fact, the very first use of Kalman filters involved nonlinear Kalman filters in NASA's space program in the 1960s. In Unscented Kalman Filter-based control a set of suitably chosen weighted sample points (sigma points) were propagated through the nonlinear system and used to approximate the true value of the system's state vector and of the state vector's covariance matrix. You can use the function KALMAN to design a steady-state Kalman filter. The real state variable is denoted by the dashed blue line, the estimated state variable is denoted by the dashed green line, while the associated reference setpoint is denoted by the continuous red line. Introduction There is increasing demand for dynamical systems to become more realizable and more cost-effective. (62), while the time update of the EKF is given by Eq. Finally, The paper studies sensorless control for DC and induction motors, using Kalman Filtering techniques. Some basic operations performed in the UKF algorithm (Unscented Transform) are summarized as follows: 1) Denoting the state vector mean as x^, a set of 2n+1 sigma points is taken from the columns of the n×n matrix (n+λ)Pxx as follows: Matrix Pxx is the covariance matrix of the state x and index i denotes its i -th column. Flatness-based control of the induction motor with the use of Extended Kalman Filtering in case of tracking a constant setpoint (a) stator's current isd (b) stator's current isq, The approach on flatness-based control of the induction motor that was presented in Section 4 needs knowledge of the electric motor's state vector x=[θ,ω,ψsd,isd,isq,ρ]. Obstacle’s distance is estimated using Linier Regression. This research aim to propose a new approach to detect obstacles and to estimate the distance of the obstacle which is in this case applied to smart wheelchair equipped with camera and line laser.

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