Control Of Puma-560 Robot Using Feedback Linearization Control Method And Kalman Filter Estimator For Regulation And Tracking Purpose

Zakeri, Ehsan; Moezi, Seyed Alireza; Zare, Mehdi; Rad, Mostafa Parnian

doi:10.22436/jmcs.011.04.02

Cited by 4 publications

(5 citation statements)

References 11 publications

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“…So, the kinematic and potential energy of the system must be computed to use in Lagrangian equation. The kinematic and potential energy of the system is obtained as [16]:…”

Section: The Governing Scara Dynamical Equationsmentioning

confidence: 99%

“…In feedback linearization method by equaling the nonlinear system in to a stable linear system, the control law can be derived as nonlinear system as below [1,16,26]. The stable linear system has been considered as below:…”

Section: Controller Designmentioning

confidence: 99%

“…In control theory, the Linear-Quadratic-Estimator (LQE) control problem is one of the most fundamental optimal control problems [20]. It concerns uncertain linear systems disturbed by additive white Gaussian noise, having incomplete state information and undergoing control subject to quadratic costs.…”

Section: Lqe Controllermentioning

confidence: 99%

“…There are various applicable controller methods that can be divided in to different branches such as model-base or none modelbase [11][12][13][14][15][16][17][18][19][20][21]; (Figure 1 & 2), adaptive [22][23][24][25][26][27][28], and intelligence [29][30][31][32][33][34][35][36] methods. Tuning the parameters of a controller method which contains a high number of tunable parameters, is very crucial and can be very cumbersome.…”

Section: Introductionmentioning

confidence: 99%

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Control of a SCARA Robot Using Feedback Linearization and Kalman Filter Observer

Nazari

2019

EME

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In this paper an efficient controller is presented for controlling SCARA robot in present of undesirable noises in variable states. To do so, feedback linearization is considered as the main controller and kalman filter estimator is utilized to estimate the state variables from the noisy output signals. In order to evaluate the performance of the purposed method, a simulation test is performed to apply this controller on the SCARA robot. The simulation is done for both noisy and not noisy signals. The results show that the kalman filter observer has accomplished a good state estimation and feedback lineation controller has tracked the desired signals perfectly as well.

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“…So, the kinematic and potential energy of the system must be computed to use in Lagrangian equation. The kinematic and potential energy of the system is obtained as [16]:…”

Section: The Governing Scara Dynamical Equationsmentioning

confidence: 99%

Section: Controller Designmentioning

confidence: 99%

Section: Lqe Controllermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Control of a SCARA Robot Using Feedback Linearization and Kalman Filter Observer

Nazari

2019

EME

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“…Selecting a suitable controller is of utmost importance, due to the remote control nature of unmanned vehicles Zare et al, 2014) or robots (Zakeri et al, 2012;Zakeri et al, 2013;Zakeri et al 2014;. So far, researchers have presented different control methods for UUVs, including, control of a high-speed underwater robot using H ∞ controller (Zhang et al, 2015) or control of an underwater robot using dynamic sliding mode controller (Xu et al, 2015).…”

Section: Latin American Journal Of Solids and Structures 13 (2016) 10mentioning

confidence: 99%

Path Planning for Unmanned Underwater Vehicle in 3D Space with Obstacles Using Spline-Imperialist Competitive Algorithm and Optimal Interval Type-2 Fuzzy Logic Controller

Zakeri

Farahat

Moezi

et al. 2016

Lat. Am. j. solids struct.

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In this research, generation of a short and smooth path in three-dimensional space with obstacles for guiding an Unmanned Underwater Vehicle (UUV) without collision is investigated. This is done by utilizing spline technique, in which the spline control points positions are determined by Imperialist Competitive Algorithm (ICA) in three-dimensional space such that the shortest possible path from the starting point to the target point without colliding with obstacles is achieved. Furthermore, for guiding the UUV in the generated path, an Interval Type-2 Fuzzy Logic Controller (IT2FLC), the coefficients of which are optimized by considering an objective function that includes quadratic terms of the input forces and state error of the system, is used. Selecting such objective function reduces the control error and also the force applied to the UUV, which consequently leads to reduction of energy consumption. Therefore, by using a special method, desired signals of UUV state are obtained from generated three-dimensional optimal path such that tracking these signals by the controller leads to the tracking of this path by UUV. In this paper, the dynamical model of the UUV, entitled as "mUUV-WJ-1", is derived and its hydrodynamic coefficients are calculated by CFD in order to be used in the simulations. For simulation by the method presented in this study, three environments with different obstacles are intended in order to check the performance of the IT2FLC controller in generating optimal paths for guiding the UUV. In this article, in addition to ICA, Particle Swarm Optimization (PSO) and Artificial Bee Colony (ABC) are also used for generation of the paths and the results are compared with each other. The results show the appropriate performance of ICA rather than ABC and PSO. Moreover, to evaluate the performance of the IT2FLC, optimal Type-1 Fuzzy Logic Controller (T1FLC) and Proportional Integrator Differentiator (PID) controller are designed and applied to the UUV and compared with each other. The simulation results show the superiority of the IT2FLC than the other two controllers.

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Tracking Control of Ball on Sphere System Using Tuned Fuzzy Sliding Mode Controller Based on Artificial Bee Colony Algorithm

Zakeri

Moezi

Eghtesad

2017

Int. J. Fuzzy Syst.

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Control Of Puma-560 Robot Using Feedback Linearization Control Method And Kalman Filter Estimator For Regulation And Tracking Purpose

Cited by 4 publications

References 11 publications

Control of a SCARA Robot Using Feedback Linearization and Kalman Filter Observer

Control of a SCARA Robot Using Feedback Linearization and Kalman Filter Observer

Path Planning for Unmanned Underwater Vehicle in 3D Space with Obstacles Using Spline-Imperialist Competitive Algorithm and Optimal Interval Type-2 Fuzzy Logic Controller

Tracking Control of Ball on Sphere System Using Tuned Fuzzy Sliding Mode Controller Based on Artificial Bee Colony Algorithm

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