Design Model Free Switching Gain Scheduling Baseline Controller with Application to Automotive Engine

Piltan, Farzin; Akbari, Mohammad; Piran, Mojdeh; Bazregar, Mansour

doi:10.5815/ijitcs.2013.01.07

Cited by 35 publications

(49 citation statements)

References 12 publications

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“…Since the actual position is the measured signal. Figure 3 shows linear PID methodology, applied to continuum robot manipulator [21][22][23][24][25][26][27][28][29][30][31][32][33][34]. The model-free control strategy is based on the assumption that the joints of the manipulators are all independent and the system can be decoupled into a group of single-axis control systems [18][19][20][21][22][23].…”

Section: Dynamic Formulation Of Continuum Robotmentioning

confidence: 99%

Design New Intelligent PID like Fuzzy Backstepping Controller

Khajeh¹,

Piltan²,

Rashidian³

et al. 2014

IJMECS

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Abstract-The minimum rule base Proportional Integral Derivative (PID) Fuzzy backstepping Controller is presented in this research. The popularity of PID Fuzzy backstepping controller can be attributed to their robust performance in a wide range of operating conditions and partly to their functional simplicity. The process of setting of PID Fuzzy backstepping controller can be determined as an optimization task. Over the years, use of intelligent strategies for tuning of these controllers has been growing. PID methodology has three inputs and if any input is described with seven linguistic values, and any rule has three conditions we will need 7 × 7 × 7 = 343 rules. It is too much work to write 343 rules. In this research the PID-like fuzzy controller can be constructed as a parallel structure of a PD-like fuzzy controller and a PI-like controller to have the minimum rule base. However backstepping controller is work based on cancelling decoupling and nonlinear terms of dynamic parameters of each link, this controller is work based on manipulator dynamic model and this technique is highly sensitive to the knowledge of all parameters of nonlinear robot manipulator"s dynamic equation. This research is used to reduce or eliminate the backstepping controller problem based on minimum rule base fuzzy logic theory to control of flexible robot manipulator system and testing of the quality of process control in the simulation environment of MATLAB/SIMULINK Simulator.Index Terms-PID like fuzzy control, backstepping controller, PD like fuzzy control, PI like control, flexible robot manipulator.

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Section: Dynamic Formulation Of Continuum Robotmentioning

confidence: 99%

Design New Intelligent PID like Fuzzy Backstepping Controller

Khajeh¹,

Piltan²,

Rashidian³

et al. 2014

IJMECS

Self Cite

View full text Add to dashboard Cite

show abstract

“…Since the actual position is the measured signal. Figure 2 shows linear PID methodology, applied to continuum robot manipulator [21][22][23][24][25][26][27][28][29][30][31][32][33][34]. The model-free control strategy is based on the assumption that the joints of the manipulators are all independent and the system can be decoupled into a group of single-axis control systems [18][19][20][21][22][23].…”

Section: Dynamic Formulation Of Continuum Robotmentioning

confidence: 99%

“…Feedback control system development is the most important thing in many different fields of safety engineering. The main targets in design control systems are safety stability, good disturbance rejection to reach the best safety, and small tracking error [21][22][23][24][25][26][27][28][29][30][31][32][33]. At present, in some applications robot arms are used in unknown and unstructured environment, therefore strong mathematical tools used in new control methodologies to design nonlinear robust controller with an acceptable safety performance (e.g., minimum error, good trajectory, disturbance rejection).…”

Section: Introductionmentioning

confidence: 99%

Design Intelligent Model base Online Tuning Methodology for Nonlinear System

Roshanzamir¹,

Piltan²,

Mozafari³

et al. 2014

IJMECS

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Abstract-In various dynamic parameters systems that need to be training on-line adaptive control methodology is used. In this paper fuzzy model-base adaptive methodology is used to tune the linear Proportional Integral Derivative (PID) controller. The main objectives in any systems are; stability, robust and reliability. However PID controller is used in many applications but it has many challenges to control of continuum robot. To solve these problems nonlinear adaptive methodology based on model base fuzzy logic is used. This research is used to reduce or eliminate the PID controller problems based on model reference fuzzy logic theory to control of flexible robot manipulator system and testing of the quality of process control in the simulation environment of MATLAB/SIMULINK Simulator.Index Terms-PID Controller, Fuzzy logic methodology, Adaptive Techniques, Model reference fuzzy tuning.

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“…According to Ogata, to do the first significant wo rk in three -term or PID controllers wh ich Nicholas Minorsky worked on it by automatic controllers in 1922. In 1934, Stefen Black was invention of the [25][26][27][28][29][30][31][32][33][34][35][36][37][38][39][40]. Negative feedback invited communicat ions engineer Harold Black in 1928 and it occurs when the output is subtracted from the input.…”

Section: Introductionmentioning

confidence: 99%

Design New Online Tuning Intelligent Chattering Free Fuzzy Compensator

Khalilian¹,

Piltan²,

Avatefipour³

et al. 2014

IJISA

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Abstract− This research is focused on proposed adaptive fuzzy sliding mode algorithms with the adaptation laws derived in the Lyapunov sense. The stability of the closed-loop system is proved mathematically based on the Lyapunov method. Adaptive M IMO fuzzy compensate fuzzy sliding mode method design a M IM O fuzzy system to compensate for the model uncertainties of the system, and chattering also solved by new adaption method. Since there is no tuning method to adjust the premise part of fuzzy rules so we presented a scheme to online tune consequence part of fuzzy rules. Classical sliding mode control is robust to control model uncertainties and external disturbances. A sliding mode method with a switching control low guarantees the stability of the certain and/or uncertain system, but the addition of the switching control low introduces chattering into the system. One of the main targets in this research to reduce or eliminate chattering is to insert online tuning method. Classical sliding mode control method has difficulty in handling unstructured model uncertainties. One can overcome this problem by combining a sliding mode controller and artificial intelligence (e.g. fuzzy logic). To approximate a time-varying nonlinear dynamic system, a fuzzy system requires a large amount of fuzzy rule base. This large number of fuzzy rules will cause a high computation load. The addition of an adaptive law to a fuzzy sliding mode controller to online tune the parameters of the fuzzy rules in use will ensure a moderate computational load. The adaptive laws in this algorithm are designed based on the Lyapunov stability theorem. Asymptotic stability of the closed loop system is also proved in the sense of Lyapunov. This method is applied to continuum robot manipulator to have the best performance.

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Design Model Free Switching Gain Scheduling Baseline Controller with Application to Automotive Engine

Cited by 35 publications

References 12 publications

Design New Intelligent PID like Fuzzy Backstepping Controller

Design New Intelligent PID like Fuzzy Backstepping Controller

Design Intelligent Model base Online Tuning Methodology for Nonlinear System

Design New Online Tuning Intelligent Chattering Free Fuzzy Compensator

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