Adaptive control of a legged robot using an artificial neural network

Results are presented of a neural network strategy for the control of a dynamic, locomotive system, in particular a one-legged hopping robot. The control task is to make corrections to the motion of the robot that serve to maintain a fixed level of energy (and minimize energy losses), which yields a stable periodic limit cycle in the system's state space. The robot is controlled by the use of an artificial neural network (ANN) with a continuous learning memory. The design and simulation of an autonomous learning apparatus is investigated to devise a strategy for controlling a one-legged hopping robot. Through continuous reinforcement for past successes and failures, the control system develops a stable strategy for accomplishing the desired control objectives. The results are presented in the form of computer simulations that demonstrate the ability of the ANN to devise proper control signals that can develop a stable hopping strategy, and hence a stable limit cycle in the robot's state space, using imprecise knowledge of both the current state and mathematical model of the robotic leg.