Robust Fuzzy PD Method with Parallel Computed Fuel Ratio Estimation Applied to Automotive Engine

Piltan, Farzin; ShahryarZadeh, Fatemeh; Mansoorzadeh, Mohammad; kamgari, Marzieh; Zare, Saeed

doi:10.5815/ijisa.2013.08.10

Cited by 25 publications

(28 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It has as sumed that the desired motion trajectory for the manipulator ( ) , as determined, by a path planner. Defines the tracking error as [23][24][25][26][27][28][29][30][31][32][33][34][35][36][37]:…”

Section: (4)mentioning

confidence: 99%

“…Automatic control has played an important role in advance science and engineering and its extreme importance in many industrial applications, i.e., aerospace, mechanical engineering and joint control. The first significant work in automatic control was James Watt"s centrifugal governor for the speed control in motor engine in eighteenth century [29][30][31][32][33][34][35][36][37][38][39][40]. There are several methods for controlling a spherical motor, wh ich all of them fo llo w two co mmon goals, namely, hardware/software imp lementation and acceptable performance.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Design Intelligent System Compensator to Computed Torque Control of Spherical Motor

Rahmani¹,

Piltan²,

Matin³

et al. 2014

IJISA

Self Cite

View full text Add to dashboard Cite

Abstract-Spherical three Degree-of-Freedom (DOF) is controlled by model-base fuzzy computed torque controller. The spherical motor has three revolute joints allowing the corresponding parts to move horizontally and vertically. When developing a controller using conventional control methodology (e.g., feedback linearization methodology), a design scheme has to be produced, usually based on a system"s dynamic model. The work outline in this research utilizes soft computing applied to new conventional controller to address these methodology issues. Computed torque controller (CTC) is influential nonlinear controllers to certain systems which this method is based on compute the required arm torque using nonlinear feedback control law. When all dynamic and physical parameters are known, CTC works superbly; practically a large amount of systems have uncertainties and fuzzy feedback Inference Engine (FIS) is used to reduce this kind of limitation. Fuzzy logic provides functional capability without the use of a system dynamic model and has the characteristics suitable for capturing the approximate, varying values found in a M ATLAB based area. Based on this research model-base fuzzy computed torque controller applied to spherical motor is presented to have a stable and robust nonlinear controller and have a good result compared with conventional and pure fuzzy logic controllers.

show abstract

Section: (4)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Design Intelligent System Compensator to Computed Torque Control of Spherical Motor

Rahmani¹,

Piltan²,

Matin³

et al. 2014

IJISA

Self Cite

View full text Add to dashboard Cite

show abstract

“…Th is control problem is classified into two main groups. Firstly, transformation the desired motion ( ) to joint variable ( ) by inverse kinemat ics of robot manipulators [34][35][36][37][38][39][40][41][42][43][44][45][46][47][48][49][50]. This control include simple PD control, PID control, inverse dynamic control, Lyapunov-based control, and passivity based control that explained them in the following section.…”

Section: Introductionmentioning

confidence: 99%

“…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%

“…It should be mentioned that application of fu zzy logic is not limited to a system that"s difficult for modeling, but it can be used in clear systems that have complicated mathemat ics models because most of the time it can be shortened in design but there is no high quality design just sometimes we can find design with high quality. Besides using fuzzy logic in the main controller of a control loop, it can be used to design adaptive control, tuning parameters, working in a parallel with the classical and non classical control method [32][33][34][35][36][37][38][39]. The applications of artificial intelligence such as neural networks and fuzzy logic in modelling and control are significantly growing especially in recent years.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Design New Online Tuning Intelligent Chattering Free Fuzzy Compensator

Khalilian¹,

Piltan²,

Avatefipour³

et al. 2014

IJISA

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Design of a fuzzy controller for delayed and high-order systems using particle swarm optimization

Rezaei

Bakhoda

Menhaj

2016

2016 4th International Conference on Control, Instrumentation, and Automation (ICCIA)

View full text Add to dashboard Cite

Robust Fuzzy PD Method with Parallel Computed Fuel Ratio Estimation Applied to Automotive Engine

Cited by 25 publications

References 16 publications

Design Intelligent System Compensator to Computed Torque Control of Spherical Motor

Design Intelligent System Compensator to Computed Torque Control of Spherical Motor

Design New Online Tuning Intelligent Chattering Free Fuzzy Compensator

Design of a fuzzy controller for delayed and high-order systems using particle swarm optimization

Contact Info

Product

Resources

About