Intelligent Optimal Control of Single-Link Flexible Robot Arm

Wai, Rong‐Jong; Lee, Meng-Chang

doi:10.1109/tie.2003.821895

Cited by 61 publications

(24 citation statements)

References 21 publications

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“…Therefore, a precisely mathematic model cannot always be defined for its behavior and common techniques cannot also deal with the control systems of above-mentioned instruments. The main problems bring to attention, while controlling smart robots can sufficiently be considered [9,10]. Lack of timely performance, parameters with non-uniform distributions, non-minimal fuzzy-based behavior as well as hard connections and combinations are among the problems discussed.…”

Section: Yesmentioning

confidence: 99%

On hybrid intelligence-based control approach with its application to flexible robot system

Khoobjou

Mazinan

2017

Hum. Cent. Comput. Inf. Sci.

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Flexible robot system is in general taken into real consideration as most important process in a number of academic and industrial environments. Due to the fact that the aforementioned system is so applicable in real domains, the novel ideas with respect to state-of-the-art in outperforming its performance are always valuable. With this purpose, a number of the soft computing techniques can be preferred with reference to the traditional ones to predict and optimize the overall performance of the above-captioned process. The approach proposed here is in fact organized in line with the integration of the fuzzy-based approach in association with the neural networks, in order to enable the process under control to be capable of learning and adapting to be matched, in a number of real environments. It can be shown that the outcomes tolerate the imprecise circumstances, as one of advantages regarding the fuzzy-based approach. In the present investigation, a new hybrid approach is proposed to deal with the arm of flexible robot system through the neural networks, the fuzzy-based approach and also the particle swarm optimization. It should be noted that the objective of the proposed research is to control the claw of robot system including two-degree-of-freedom movable arms. The results indicate that the mean-square error and the root-mean-square error are accurately outperformed with reference to the traditional ones, tangibly.

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Section: Yesmentioning

confidence: 99%

On hybrid intelligence-based control approach with its application to flexible robot system

Khoobjou

Mazinan

2017

Hum. Cent. Comput. Inf. Sci.

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“…BLDC motors are now available from milliwatt to kilowatt for operating robotic arms in the automotive, aerospace, transport, and medical fields, as well as in many other industrial automation applications [1]- [3]. Industrial automation involves operations in different environments with different payloads at a uniform speed in the four quadrants with bidirectional speed controls and regenerative braking capabilities [4]- [7]. The conventional feedback proportional-integral-derivative (PID) controller is based on the accuracy of the mathematical model of the system, and its expected performance is likely to be affected by load disturbances, whereas the latest fuzzy logic is a dependable technique and achieves better dynamic performance with least chances of errors.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Position Control of a Vertical Rotating Single Arm Robot Using BLDC Servo Drive

Manikandan¹,

Arulmozhiyal²

2016

Journal of Power Electronics

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The manufacturing sector resorts to automation to increase production and homogeneity of products during mass production, without increasing scarce, expensive, and unreliable manpower. Automation in the form of multiple robotic arms that handle materials in all directions in different stages of the process is proven to be the best way to increase production. This paper thoroughly investigates robotic single-arm movements, that is, 360° vertical rotation, with the help of a brushless DC motor, controlled by a fuzzy proportional-integral-derivative (PID) controller. This paper also deals with the design and performance of the fuzzy-based PID controller used to control vertical movement against the limited scope of conventional PID feedback controller and how the torque of the arm is affected by the fuzzy PID controller in the four quadrants to ensure constant speed and accident-free operation despite the influence of gravitational force. The design was simulated through MATLAB/SIMULINK and integrated with dSPACE DS1104-based hardware to verify the dynamic behaviors of the arm.

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“…Fuzzy logic provides a method which is able to model a controller for nonlinear plant with a set of IF-THEN ru les, or it can identify the control actions and describe them by using fuzzy rules. The applicat ions of artificial intelligence, neural networks and fuzzy logic, on nonlinear systemcontrol have reported in [24][25][26]. Wai et al [24][25]have proposed a fuzzy neural network (FNN) optimal control system to learn a nonlinear function in the optimal control law.…”

Section: Introductionmentioning

confidence: 99%

“…The applicat ions of artificial intelligence, neural networks and fuzzy logic, on nonlinear systemcontrol have reported in [24][25][26]. Wai et al [24][25]have proposed a fuzzy neural network (FNN) optimal control system to learn a nonlinear function in the optimal control law. This controller is divided into three main groups: arterial intelligence controller (fu zzy neural network) wh ich it is used to compensate the system's nonlinearity and imp roves by adaptive method, robust controller to reduce the error and optimal controller which is the main part of this controller.…”

Section: Introductionmentioning

confidence: 99%

Evaluation Performance of IC Engine: Linear Tunable Gain Computed Torque Controller vs. Sliding Mode Controller

Haghighi¹,

Soltani²,

Piltan³

et al. 2013

IJISA

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I. IntroductionThe internal co mbustion (IC) engine is designed to produce power fro m the energy that is contained in its fuel. More specifically, its fuel contains chemical energy and together with air, this mixtu re is burned to output mechanical power. There are various types of fuels which can be used in IC engines namely; petroleum, d iesel, bio-fuels, and hydrogen [1]. Modeling of an entire IC engine is a very impo rtant and complicated process because engines are nonlinear, multi inputs-multi outputs (MIMO) and time variant.Controller design is the main parts in this paper as well as the majo r objectives in the controller design are stability and robustness. One of the significant challenges in control algorith ms is design a linear controller fo r nonlinear systems. When system works with various parameters and hard nonlinearit ies this technique is very useful in order to be implemented easily but it has some limitations such as working near the system operating point [9]. So me of IC engines which work in industrial processes are controlled by linear controllers, but linear controller design for IC engines is extremely difficu lt [1][2][3][4][5][6]. Co mputed torque controller (CTC) is a powerfu l nonlinear controller which it widely used in control of IC engine. It is based on feedback linearization and computes the required arm torques using the nonlinear feedback control law. This controller works very well when all dynamic and physical parameters are known but when the IC engine has variation in dynamic parameters, in this situation the controller has no acceptable performance [7][8][9][10][11][12][13][14]. In practice, most of physical systems (e.g., IC engine) parameters are unknown o r t ime variant, therefore, online tuneable gain co mputed torque controller used to compensate dynamic equation of IC engine [1,6]. Sliding mode controller (SMC) is one of the influential nonlinear controllers in certain and uncertain systems which are used to solved stability and robustness [10]. The main reason for this popularity is the attractive properties which SMCs have, such as good control performance for nonlinear systems, applicability to MIMO systems and well-established design criteria for discrete-time systems. SMC may emp loy unnecessarily large control signals to overcome the parametric uncertainties and difficulty in the calculat ion of what is known as the equivalent control [11][12][13][14][15][16][17]. In various dynamic parameters systems that need to be training online adaptive control methodology is used. Adaptive control methodology can be classified into two main groups, namely, t raditional adaptive method and fuzzy adaptive method [18][19][20][21][22]. Fu zzy adaptive method is used in systems which want to train ing parameters by expert knowledge. Trad itional adaptive method is used in systems which some dynamic parameters are known. In this research in order to solve disturbance rejection and uncertainty dynamic parameter, adaptive method are applied to slid ing mode controller and c...

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Intelligent Optimal Control of Single-Link Flexible Robot Arm

Cited by 61 publications

References 21 publications

On hybrid intelligence-based control approach with its application to flexible robot system

On hybrid intelligence-based control approach with its application to flexible robot system

Intelligent Position Control of a Vertical Rotating Single Arm Robot Using BLDC Servo Drive

Evaluation Performance of IC Engine: Linear Tunable Gain Computed Torque Controller vs. Sliding Mode Controller

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