Locomotion Control Architecture for the Pneumatic Biped Lucy consisting of a Trajectory Generator and Joint Trajectory Tracking Controller

Author(s):  
Bram Vanderborght ◽  
Bjorn Verrelst ◽  
Michael Van Damme ◽  
Ronald Van Ham ◽  
Pieter Beyl ◽  
...  
Robotica ◽  
2006 ◽  
Vol 24 (4) ◽  
pp. 401-410 ◽  
Author(s):  
Bram Vanderborght ◽  
Björn Verrelst ◽  
Ronald Van Ham ◽  
Dirk Lefeber

This paper reports on the control structure of the pneumatic biped Lucy. The robot is actuated with pleated pneumatic artificial muscles, which have interesting characteristics that can be exploited for legged locomotion. They have a high power to weight ratio, an adaptable compliance and they can absorb impact effects.The discussion of the control architecture focuses on the joint trajectory generator and the joint trajectory tracking controller. The trajectory generator calculates trajectories represented by polynomials based on objective locomotion parameters, which are average forward speed, step length, step height and intermediate foot lift. The joint trajectory tracking controller is divided in three parts: a computed torque module, a delta-p unit and a bang-bang pressure controller. The control design is formulated for the single support and double support phase, where specifically the trajectory generator and the computed torque differs for these two phases.The first results of the incorporation of this control architecture in the real biped Lucy are given. Several essential graphs, such as pressure courses, are discussed and the effectiveness of the proposed algorithm is shown by the small deviations between desired and actual attained objective locomotion parameters.


Author(s):  
Cassius Z. Resende ◽  
F. Espinosa ◽  
I. Bravo ◽  
Mario Sarcinelli-Filho ◽  
Teodiano F. Bastos-Filho

Author(s):  
Pouya Panahandeh ◽  
Khalil Alipour ◽  
Bahram Tarvirdizadeh ◽  
Alireza Hadi

Purpose Trajectory tracking is a common problem in the field of mobile robots which has attracted a lot of attention in the past two decades. Therefore, besides the search for new controllers to achieve a better performance, improvement and optimization of existing control rules are necessary. Trajectory tracking control laws usually contain constant gains which affect greatly the robot’s performance. Design/methodology/approach In this paper, a method based on neural networks is introduced to automatically upgrade the gains of a well-known trajectory tracking controller of wheeled mobile robots. The suggested method speeds up the convergence rate of the main controller. Findings Simulations and experiments are performed to assess the ability of the suggested scheme. The obtained results show the effectiveness of the proposed method. Originality/value In this paper, a method based on neural networks is introduced to automatically upgrade the gains of a well-known trajectory tracking controller of wheeled mobile robots. The suggested method speeds up the convergence rate of the main controller.


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