Machine Learning
Reinforcement learning for quasi-passive dynamic walking of an unstable biped robot
Robotics and Autonomous Systems
Nearly optimal neural network stabilization of bipedal standing using genetic algorithm
Engineering Applications of Artificial Intelligence
Journal of Intelligent and Robotic Systems
Boundary condition relaxation method for stepwise pedipulation planning of biped robots
IEEE Transactions on Robotics - Special issue on rehabilitation robotics
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Engineering Applications of Artificial Intelligence
A biped static balance control and torque pattern learning under unknown periodic external forces
Engineering Applications of Artificial Intelligence
Fault tolerance in the framework of support vector machines based model predictive control
Engineering Applications of Artificial Intelligence
Support vector regression based modeling of pier scour using field data
Engineering Applications of Artificial Intelligence
Sensory reflex control for humanoid walking
IEEE Transactions on Robotics
IEEE Transactions on Robotics
Modifiable Walking Pattern of a Humanoid Robot by Using Allowable ZMP Variation
IEEE Transactions on Robotics
Energy-Efficient and High-Speed Dynamic Biped Locomotion Based on Principle of Parametric Excitation
IEEE Transactions on Robotics
A Type-2 Fuzzy Switching Control System for Biped Robots
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
IEEE Transactions on Neural Networks
SVR Versus Neural-Fuzzy Network Controllers for the Sagittal Balance of a Biped Robot
IEEE Transactions on Neural Networks
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
CPG-Inspired Workspace Trajectory Generation and Adaptive Locomotion Control for Quadruped Robots
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A high speed railway control system based on the fuzzy control method
Expert Systems with Applications: An International Journal
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To learn biped walking dynamics accurately, and then compensate time-varying external disturbances timely, a time-sequence-based fuzzy SVM (TSF-SVM) learning control system considering time properties of biped walking samples is proposed. For the first time, time-sequence-based triangular and Gaussian fuzzy membership functions have been proposed for the single support phase (SSP) and the double support phase (DSP), respectively, according to time properties of different biped phases, which provides an effective way to formulate time properties of biped walking samples in the context of time-varying external disturbances. In addition, a time-sequence-based moving learning window (TS-MLW) is proposed for online training of the proposed TSF-SVM. The performance of the proposed TSF-SVM is compared with other typical intelligent methods; simulation results demonstrate that the proposed method is more sensitive to occasional external disturbances, which increases the stability margin and prevents the robot from falling down.