Fuzzy controlled gait synthesis for a biped walking machine

Magdalena, Luis; Monasterio, F.

doi:10.1109/fuzzy.1993.327586

Cited by 15 publications

(1 citation statement)

References 16 publications

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“…Alternatively, if they are to be used in environments where humans regularly work, biped robots will need to be smaller, lighter or more useful if they are to replace them. Many methodologies have been developed for the control of biped walking robots, from model based modern control [1,2], to fuzzy logic (FL) [3], neural networks (NN) [4,5] genetic algorithms (GA) [6] and even intuitive control [7]. However, it is dif®cult to maintain stable and smooth locomotion while the biped is dynamic walking.…”

Section: Introductionmentioning

confidence: 99%

Neuro-fuzzy gait synthesis with reinforcement learning for a biped walking robot

Zhou

2000

Soft Computing

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A reinforcement learning-based neuro-fuzzy gait synthesizer, which is based on the GARIC (Generalized Approximate Reasoning for Intelligent Control) architecture, is proposed for the problem of biped dynamic balance. We modify the GARIC architecture to enable it to generate the trunk trajectory in both sagittal and frontal plane. The proposed gait synthesizer is trained by reinforcement learning that uses a multi-valued scalar signal to evaluate the degrees of failure or success for the biped locomotion by means of the ZMP (Zero Moment Point). It can form the initial dynamic balancing gait from linguistic rules, which are obtained from human intuitive balancing knowledge and biomechanics studies, and accumulate dynamic balancing knowledge through reinforcement learning, and thus constantly improve its gait during walking. The feasibility of the proposed method is veri®ed through a 5-link biped robot simulation.

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Section: Introductionmentioning

confidence: 99%

Neuro-fuzzy gait synthesis with reinforcement learning for a biped walking robot

Zhou

2000

Soft Computing

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Control of a Bipedal Walking Robot Using a Fuzzy Precompensator

Bououden

Abdessemed

Benali

2009

Agent and Multi-Agent Systems: Technologies and Applications

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Fuzzy neural network approaches for robotic gait synthesis

Juang

2000

IEEE Trans. Syst., Man, Cybern. B

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In this paper, a learning scheme using a fuzzy controller to generate walking gaits is developed. The learning scheme uses a fuzzy controller combined with a linearized inverse biped model. The controller provides the control signals at each control time instant. The algorithm used to train the controller is "backpropagation through time". The linearized inverse biped model provides the error signals for backpropagation through the controller at control time instants. Given prespecified constraints such as the step length, crossing clearance, and walking speed, the control scheme can generate the gait that satisfies these constraints. Simulation results are reported for a five-link biped robot.

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Fuzzy controlled gait synthesis for a biped walking machine

Abstract: The control of Biped Walking Machines constitutes a really complex problem that we will focuse by using an Intelligent Controller.

Cited by 15 publications

References 16 publications

Neuro-fuzzy gait synthesis with reinforcement learning for a biped walking robot

Neuro-fuzzy gait synthesis with reinforcement learning for a biped walking robot

Control of a Bipedal Walking Robot Using a Fuzzy Precompensator

Fuzzy neural network approaches for robotic gait synthesis

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