Fuzzy based Gains Tuning of PD controller for joint position control of AIT Leg Exoskeleton-I (ALEX-I)

Aphiratsakun, Narong; Parnichkun, Manukid

doi:10.1109/robio.2009.4913112

Cited by 5 publications

(5 citation statements)

References 6 publications

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“…Body COM of the ALEX is shifted to the supported foot, before the right swing is generated. At this point, authors noticed that constraints on the actual designed joint angles at the ankle have to be exceeded [8]. However, the gaits data could be achieved in the simulation module.…”

Section: Simulation Resultsmentioning

confidence: 99%

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ZMP Based Gait Generation of AIT Leg Exoskeleton-A Further Gaits Generation

Aphiratsakun¹,

Chairungsarpsook

Parnichkun

2012

AMR

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This paper is the extension of gait pattern generation of the Asian Institute of Technology’s Leg EXoskeleton (ALEX) and its wearer can walk safety with the passing criteria of Zero Moment Point (ZMP) theorem for static and dynamic considerations respectively. ALEX has 12 DOF (6 DOF for each leg: 3 at the Hip, 1 at the knee and 2 at the ankle), controlled by 12 DC motors. The CAD drawing and assembly of ALEX are exported directly to MATLAB’s Simulink-SimMechanics to obtain accurate position of center of gravity (CG) and moment of inertia of each link. The gait pattern is visually observed using 3D VRML interpreter while ZMP trajectory is monitored using MATLAB’s 2D graphics representation. The further gaits generations in this paper include walking from non-zero initial condition, climbing up the stairs and turning left motions. From previous work, the gait data from simulation has confirmed it effectiveness with the actual hardware. With this further gait generation data, ALEX can be tested to confirm its balance gait motions prior to the real implementation to avoid any undesired motions that could damages to the robot system.

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Section: Simulation Resultsmentioning

confidence: 99%

“…ALEX has been developed in 2006 by Asian Institute of Technology (AIT), Thailand. The mechanical details have been discussed in [7,8]. In general, ALEX has 12 DOF (6 DOF on each leg: 3 at the hip, 1 at the knee and 2 at the ankle), controlled by 12 DC motors.…”

Section: Further Gaits Generationmentioning

confidence: 99%

ZMP Based Gait Generation of AIT Leg Exoskeleton-A Further Gaits Generation

Aphiratsakun¹,

Chairungsarpsook

Parnichkun

2012

AMR

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“…Recently, because intelligent algorithms, such as neural network and fuzzy theory, do not require precise mathematical models with a great adaptability to the inputs from outside, a few scientists adopt these intelligent algorithms to develop controllers for exoskeletons. 14,17,18 As motioned earlier, intelligent algorithms need a large amount of calculation during the control process, which require high speed computers. To make the control system more adaptive and keep a small amount of calculation, it is easy to think about developing an algorithm which combines intelligent algorithms with traditional algorithms.…”

Section: The Control Methodology Of Exoskeletons a Brief Survey On Comentioning

confidence: 99%

“…10 Researchers have to face these difficulties above. Traditional control algorithms [12][13][14] in exoskeleton systems are designed for a special exoskeleton, operator or motion, which need great efforts and have weak universality. With great adaptability to imprecise models, intelligent algorithms, such as fuzzy theory, find wide applications in the field of robots, 15 which also attract the interest of exoskeleton researchers in recent years.…”

Section: Introductionmentioning

confidence: 99%

Bio-inspired control research of a novel lower extremity exoskeleton with a series-parallel mechanism

Pan

Gao

Miao

et al. 2014

Proceedings of the Institution of Mechanical Engineers, Part C:

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This paper proposes a switch-type and parameter self-tuning fuzzy-PID/PID controller for a novel lower extremity exoskeleton with a series-parallel mechanism according to the features of human level walking gait. The novel exoskeleton mechanism was described, the biological characteristics of human legs during the walking cycle were analyzed and a mapping from the positions of human lower extremity joints to the exoskeleton joints was established. Then the schematic of the exoskeleton control strategy was discussed and illustrated. The co-simulation method of ADAMS and MATLAB/SIMULINK was adopted and the fuzzy-PID/PID controller was designed. A 1-DOF rotation control was simulated by both the fuzzy-PID/PID controller and a conventional PID controller for comparison. Finally, the exoskeleton level walking gait was co-simulated by using the bio-inspired control strategy. The results show that the controller can provide a fast, stable and precise control for the exoskeleton system.

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“…When the above control algorithms are implemented in real applications, a long and tedious tuning of control gains is often required to achieve desired system performances. Despite the large number of gain optimization procedures for classical dynamical systems (see, e.g., [14], [15]) gain optimization techniques for floating base systems, and in particular in the field of humanoid robots, still needs more investigations. Preliminary results in this direction consist in applying classical LQR approaches to the linearized humanoid robot dynamics [16], [17].…”

Section: Introductionmentioning

confidence: 99%

Automatic gain tuning of a momentum based balancing controller for humanoid robots

Pucci

Nava

Nori

2016

2016 IEEE-RAS 16th International Conference on Humanoid Robots (Humanoids)

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This paper proposes a technique for automatic gain tuning of a momentum based balancing controller for humanoid robots. The controller ensures the stabilization of the centroidal dynamics and the associated zero dynamics. Then, the closed-loop, constrained joint space dynamics is linearized and the controller's gains are chosen so as to obtain desired properties of the linearized system. Symmetry and positive definiteness constraints of gain matrices are enforced by proposing a tracker for symmetric positive definite matrices. Simulation results are carried out on the humanoid robot iCub.

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Fuzzy based Gains Tuning of PD controller for joint position control of AIT Leg Exoskeleton-I (ALEX-I)

Cited by 5 publications

References 6 publications

ZMP Based Gait Generation of AIT Leg Exoskeleton-A Further Gaits Generation

ZMP Based Gait Generation of AIT Leg Exoskeleton-A Further Gaits Generation

Bio-inspired control research of a novel lower extremity exoskeleton with a series-parallel mechanism

Automatic gain tuning of a momentum based balancing controller for humanoid robots

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