Simulated annealing and Boltzmann machines: a stochastic approach to combinatorial optimization and neural computing
Visual Attention and Learning of a Cognitive Robot
ICANN '97 Proceedings of the 7th International Conference on Artificial Neural Networks
Velocity and position control of a wheeled inverted pendulum by partial feedback linearization
IEEE Transactions on Robotics
Linear Hopfield networks and constrained optimization
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Human-thinking simulated control
International Journal of Systems, Control and Communications
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This paper presents a dynamic control model for a flexible two-wheeled self-balancing robot based on Lagrange's equation and the theory of dynamics and mechanics. One new aspect, introduced in this paper, is the modelling of the human lumbar a spring is added to the robot to imitate lumbar flexibility and curvature. The validity of the system modelling and controller design is verified through simulation, experimental results and the implementation of the robot. Two methods were used to control the robot's posture: a Discrete Hopfield Neural Network (DHNN) and a Boltzmann machine; the results are compared and then an improved Boltzmann machine is described.