Humanoid Robot Pitch Axis Stabilization using Linear Quadratic Regulator with Fuzzy Logic and Capture Point
This work addresses the problem of stabilizing humanoid robots for researchers and developers working on robust robot locomotion, offering an incremental improvement in handling nonlinearities.
This paper developed a control system to stabilize a position-controlled humanoid robot on synthetic grass against external disturbances. It uses a system-identified dynamic model, Kalman Filter, and an LQR controller extended with fuzzy logic to adjust gains based on angle and angular velocity, successfully maintaining stability around the pitch axis against pendulum disturbances and restraining forces.
This paper aims for a controller that can stabilize a position-controlled humanoid robot when standing still or walking on synthetic grass even when subjected to external disturbances. Two types of controllers are designed and implemented: ankle strategy and stepping strategy. The robot's joints consist of position-controlled servos which can be complicated to model analytically due to nonlinearities and non-measurable parameters, hence the dynamic model of the humanoid robot is acquired using a non-recursive least squares system identification. This model is also used to design a Kalman Filter to estimate the system states from noisy inertial measurement unit (IMU) sensor and design a linear quadratic regulator (LQR) controller. To handle the nonlinearities, the LQR controller is extended with fuzzy logic algorithm that changes the LQR gain value based on angle and angular velocity membership functions. The proposed control system can maintain the humanoid robot's stability around the pitch axis when subject to pendulum disturbances or even restraining force from a spring balance.