Adaptive tracking for pneumatic muscle actuators in bicep and tricep configurations

Lilly, J. H.

doi:10.1109/tnsre.2003.816870

Cited by 107 publications

(64 citation statements)

References 14 publications

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“…Consequently, such an actuator already finds many applications in the field of medical engineering, medical welfare system. For examples, the upper limb muscle actuator [1], powerassist wear [2], bio-mimetic actuators [3], rehabilitation robot [4,5], biceps and triceps actuator [6], and so on. In this paper, a novel human-friendly artificial flexible robot arm using four parallel-connected PMAs is developed.…”

Section: Introductionmentioning

confidence: 99%

An artificial flexible robot arm based on pneumatic muscle actuators

Renn

2017

MATEC Web Conf.

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Abstract. The purpose of this paper is to develop a novel human-friendly artificial flexible robot arm using four parallel-connected pneumatic muscle actuators (PMAs). The PMA is a flexible silicone rubber actuator which has some behaviors nearest to the real biological muscle including translational and rotational motions. An inverse kinematic model for the motion control is also developed. Finally, from experiment results, it is proved that not only the axial contraction control of a single PMA but also the attitude control of the whole pneumatic flexible robot arm using PID controller are satisfactory.

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Section: Introductionmentioning

confidence: 99%

An artificial flexible robot arm based on pneumatic muscle actuators

Renn

2017

MATEC Web Conf.

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“…Hysteresis can contribute to the limitations of use in the field of high precision positioning such as robotics. The nonlinear and time variable behaviour is due to compressibility of air and the viscoelastic material [8,9]. Choi et al in [10] highlight to overcome the nonlinearity several easier models have been developed, but most results are limited and valid only on simulation.…”

Section: Introductionmentioning

confidence: 99%

Elimination of the hysteresis effect of PAM actuator: modelling and experimental studies

Sárosi

2015

Teh. vjesn.

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Original scientific paper Fluidic Muscle produced by Festo Company is the most used and investigated type of pneumatic artificial muscle (PAM) actuators which can be characterized such as powerful, dynamic (even 6000 N, 50 m/s 2 ), judder-free and resistant to dirt and dust, therefore these actuators are widely used in industrial environment besides electric motors or hydraulic actuators. One of the most mentioned drawbacks of PAMs is the existence of hysteresis. In this paper two experiments related to hysteresis phenomena of PAMs are described. The goal is to present a precise static force model for the hysteresis loop in the force-contraction (force-relative displacement) curve and a LabVIEW based sliding mode controller for hysteresis independent accurate positioning. Using the approximation algorithm for the force produced by the Fluidic Muscle the correlation coefficient R of nearly 1 and 0,01 mm accuracy in positioning is reached. Keywords: hysteresis; LabVIEW; pneumatic artificial muscle; sliding mode controller; static force model Otklanjanje učinka histereze PAM aktuatora: modeliranje i analiza eksperimenataIzvorni znanstveni članak Fluidic Muscle koji je proizvela Festo Company najviše je korišteni i istraživani tip aktuatora pneumatskih umjetnih mišića (PAM -pneumatic artificial muscle) koji se može okarakterizirati kao snažan i dinamičan (čak 6000 N, 50 m/s 2 ), bez vibracija i otporan na prljavštinu i prašinu. Stoga se ti aktuatori uvelike koriste u industriji uz električne motore ili hidraulične aktuatore. Jedan od najviše spominjanih nedostataka PAMova je postojanje histereze. U ovom se radu opisuju dva eksperimenta povezana s pojavom histereze kod PAMova. Cilj je predstaviti točan model statičke sile za krivulju histereze u krivulji sila-kontrakcija (pomak u odnosu na silu) i regulator stanja klizanja za točno pozicioniranje neovisno od histereze na temelju LabVIEW. Uz primjenu algoritma aproksimacije za silu koju je ostvario Fluidic Muscle postignut je korelacijski koeficijent R od gotovo 1 i 0,01 mm točnosti u pozicioniranju.

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“…Due to their highly nonlinear nature and time-varying parameters, PAM robot arms present a challenging nonlinear model problem. Approaches to PAM control have included PID control, adaptive control (Lilly, 2003), nonlinear optimal predictive control (Reynolds et al, 2003), variable structure control (Repperger et al, 1998;Medrano-Cerda et al,1995), gain scheduling (Repperger et al,1999), and various soft computing approaches including neural network Kohonen training algorithm control (Hesselroth et al,1994), neural network + nonlinear PID controller (Ahn and Thanh, 2005), and neuro-fuzzy/genetic control (Chan et al, 2003;Lilly et al, 2003). Balasubramanian et al, (2003a) applied the fuzzy model to identify the dynamic characteristics of PAM and later applied the nonlinear fuzzy model to model and to control of the PAM system.…”

Section: Introductionmentioning

confidence: 99%

“…Balasubramanian et al, (2003a) applied the fuzzy model to identify the dynamic characteristics of PAM and later applied the nonlinear fuzzy model to model and to control of the PAM system. Lilly (2003) presented a direct continuous-time adaptive control technique and applied it to control joint angle in a single-joint arm. Tsagarakis et al (2000) developed an improved model for PAM.…”

Section: Introductionmentioning

confidence: 99%

“…Repperger et al (1999) applied a gain scheduling model-based controller to a single vertically hanging PAM. Chan et al, (2003) and Lilly et al, (2003) introduced a fuzzy P+ID controller and an evolutionary fuzzy controller, respectively, for the PAM system. The novel feature is a new method of identifying fuzzy models from experimental data using evolutionary techniques.…”

Section: Introductionmentioning

confidence: 99%

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A New Approach of the Online Tuning Gain Scheduling Nonlinear PID Controller Using Neural Network

Ánh¹,

Nam²

2011

PID Control, Implementation and Tuning

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This chapter presents the design, development and implementation of a novel proposed online-tuning Gain Scheduling Dynamic Neural PID (DNN-PID) Controller using neural network suitable for real-time manipulator control applications. The unique feature of the novel DNN-PID controller is that it has highly simple and dynamic self-organizing structure, fast online-tuning speed, good generalization and flexibility in online-updating. The proposed adaptive algorithm focuses on fast and efficiently optimizing Gain Scheduling and PID weighting parameters of Neural MLPNN model used in DNN-PID controller. This approach is employed to implement the DNN-PID controller with a view of controlling the joint angle position of the highly nonlinear pneumatic artificial muscle (PAM) manipulator in real-time through Real-Time Windows Target run in MATLAB SIMULINK ® environment. The performance of this novel proposed controller was found to be outperforming in comparison with conventional PID controller. These results can be applied to control other highly nonlinear SISO and MIMO systems.

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Adaptive tracking for pneumatic muscle actuators in bicep and tricep configurations

Cited by 107 publications

References 14 publications

An artificial flexible robot arm based on pneumatic muscle actuators

An artificial flexible robot arm based on pneumatic muscle actuators

Elimination of the hysteresis effect of PAM actuator: modelling and experimental studies

A New Approach of the Online Tuning Gain Scheduling Nonlinear PID Controller Using Neural Network

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