The Fuzzy (PI+D)2 Sliding Mode Scheme to Motor Vibration Control

Rahbar, Saman; Piltan, Farzin; Pouladi, Ehsan; Davarpanah, Hossein; Jowkar, Somayeh

doi:10.14257/ijunesst.2015.8.1.33

Cited by 4 publications

(5 citation statements)

References 14 publications

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“…Several different methods are available to compute system dynamic equations. These methods include the Newton-Euler (N-E) methodology, the Lagrange-Euler (L-E) method, and Kane's methodology [16][17][18][19][20]. The Newton-Euler methodology is based on Newton's second law and several different researchers are signifying to develop this method.…”

Section: Spherical Motormentioning

confidence: 99%

“…This equation can be described the behavior of a spherical motor joint-by-joint from first to final, called forward recursion and transfer the essential information from end to base frame, called backward recursive. The literature on Euler-Lagrange's is vast but a good starting point to learn about it is in [16][17][18][19][20]. Calculate the dynamic equation spherical motor using E-L method is easier because this equation is derivation of nonlinear coupled and quadratic differential equations.…”

Section: Spherical Motormentioning

confidence: 99%

“…Calculate the dynamic equation spherical motor using E-L method is easier because this equation is derivation of nonlinear coupled and quadratic differential equations. The Kane's method was introduced in 1961 by Professor Thomas Kane [18]. This method used to calculate the dynamic equation of motion without any differentiation between kinetic and potential energy functions.…”

Section: Spherical Motormentioning

confidence: 99%

“…Conversely in this theory good trajectory following is based on fast switching, fast switching is caused to have system instability and chattering phenomenon. Fine tuning the sliding surface slope is based on nonlinear equivalent part [18]. However, this controller is used in many applications but, pure sliding mode controller has two most important challenges: chattering phenomenon and nonlinear equivalent dynamic formulation in uncertain parameters [19].…”

Section: Sliding Mode Controllermentioning

confidence: 99%

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Intelligent Trajectory Tracking Control of Robot-Assisted Surgery

Avazpour¹,

Piltan²,

Ghiasi³

et al. 2015

IJHIT

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Robotic surgery, computer-assisted surgery, and robotically-assisted surgery are terms for technological developments that use robotic systems to aid in surgical procedures. Robotically-assisted surgery was developed to overcome the limitations of minimallyinvasive surgery and to enhance the capabilities of surgeons performing open surgery. In the case of robotically-assisted minimally-invasive surgery, instead of directly moving the instruments, the surgeon uses one of two methods to control the instruments; either a direct telemanipulator or through computer control. A telemanipulator is a remote manipulator that allows the surgeon to perform the normal movements associated with the surgery whilst the robotic arms carry out those movements using end-effectors and manipulators to perform the actual surgery on the patient. In computer-controlled systems the surgeon uses a computer to control the robotic arms and its end-effectors, though these systems can also still use telemanipulators for their input. One advantage of using the computerised method is that the surgeon does not have to be present, but can be anywhere in the world, leading to the possibility for remote surgery. The multi degrees of freedom actuator is an important joint, which has attracted worldwide developing interests for its medical, industry and aerospace applications. This paper addresses the problem of trajectory tracking of three dimensions joint in the presence of model uncertainties and external disturbances. An adaptive fuzzy sliding mode controller (AFLSMC) is proposed to steer a three dimension joint along a desired trajectory precisely. First, the dynamics model of a three dimension joint is formulated and the trajectory tracking problem is described. Second, a sliding mode controller (SMC) is designed to track a time-varying trajectory. The fuzzy logic system (FLS) is employed to approximate the uncertain model of the three dimension joint, with the tracking error and its derivatives and the commanded trajectory and its derivatives as FLS inputs and the approximation of the uncertain model as FLS output. And a fuzzy logic system is also adopted to attenuate the chattering results from the SMC. The control gains are tuned synchronously with the sliding surface according to fuzzy rules, with switching sliding surface as fuzzy logic inputs and control gains as fuzzy logic outputs. The stability and convergence of the closed-loop controller is proven using the Lyapunov stability theorem. Finally, the effectiveness and robustness of the proposed controller are demonstrated via simulation results. Contrasting simulation results indicate that the AFLSMC attenuates the chattering effectively and has better performance against the SMC.

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Section: Spherical Motormentioning

confidence: 99%

Section: Spherical Motormentioning

confidence: 99%

Section: Spherical Motormentioning

confidence: 99%

Section: Sliding Mode Controllermentioning

confidence: 99%

See 2 more Smart Citations

Intelligent Trajectory Tracking Control of Robot-Assisted Surgery

Avazpour¹,

Piltan²,

Ghiasi³

et al. 2015

IJHIT

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“…Conventional nonlinear control theories are highly sensitive to system's behavior and work based on cancelling decoupling and nonlinear terms of dynamic parameters of each likes in robot manipulators. Computed Torque Control (CTC) and Sliding Mode Control (SMC) are two nonlinear conventional controller which introduced by many researchers to control of OCTARM continuum robot manipulator [17].…”

Section: Introductionmentioning

confidence: 99%

Design Second Order Vibration Reduction

Kazeminasab¹,

Esmaeili²,

Salehi³

et al. 2015

IJHIT

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Design of a robust controller for multi input-multi output (MIMO) nonlinear uncertain dynamical system can be a challenging work. This paper focuses on the design and analysis of a high performance adaptive baseline sliding mode control for second order nonlinear uncertain system, in presence of uncertainties to reduce the vibration. In this research, sliding mode controller is a robust and stable nonlinear controller which selected to control of robot manipulator. The proposed approach effectively combines of design methods from switching sliding mode controller, adaptive model-free baseline controller and linear Proportional-Derivative (PD) control to improve the performance, stability and robustness of the sliding mode controller. Sliding mode controller has two important subparts, switching and equivalent. Switching part (discontinuous part) is very important in uncertain condition but it causes chattering phenomenon. To solve the chattering, the most common method used is linear boundary layer saturation method, but this method lost the stability. To reduce the chattering with respect to stability and robustness; linear controller is added to the switching part of the sliding mode controller. The linear controller is to reduce the role of sliding surface slope and switching (sign) function. The nonlinearity term of the sliding mode controller is used to eliminate the decoupling and nonlinear term of link's dynamic parameters. However nonlinearity term of sliding mode controller is very essential to reliability but in uncertain condition or highly nonlinear dynamic systems it can cause some problems. To solve this challenge the baseline controller is used as online tune or adaptive controller. This controller improves the stability and robustness, reduces the chattering as well and reduces the level of energy due to the torque performance as well.

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