The balancing of an inverted pendulum by moving a cart along a horizontal track is a classic problem in the area of control. This paper describes two Neural Network controllers to swing a pendulum attached to a cart from an initial downwards position to an upright position and maintain that state. Both controllers are able to learn the demonstrated behavior which was swinging up and balancing t...

A cart with two independent inverted pendula, called a dual-inverted-pendulum system, is analyzed and compared to the single-inverted-pendulum system using classical linear methods. Using only the angles of the pendula and the position of the cart, a classical controller is designed that stabilizes the pendula in the inverted position with the cart at the center of the track. Simulations of the...

This paper investigates a new modelling and control issue: the tracking control problem of underactuated dynamic systems by using a special example – a pendulum-driven cart-pole system. A dynamic model of a pendulum-driven cart-pole system is firstly presented. Then, a six-step motion strategy of the pendulum-driven cart-pole system is proposed. After that, a desired profile of the pendulum joi...

In this paper we present a coordinating control law for a network of mechanical systems with unstable dynamics. The control law is derived using the Method of Controlled Lagrangians together with potential shaping designed to couple the mechanical systems. The coupled system is Lagrangian with symmetry, and energy methods are used to prove stability and coordinated behavior. The class of mechan...

This paper presents the modelling and simulation for optimal control design of nonlinear inverted pendulum-cart dynamic system using Proportional-Integral-Derivative (PID) controller and Linear Quadratic Regulator (LQR). LQR, an optimal control technique, and PID control method, both of which are generally used for control of the linear dynamical systems have been used in this paper to control ...

We report an empirical study on the application of machine learning to the modelling of novice controllers’ skills in balancing a pole (inverted pendulum) on top of a cart. Results are presented on the predictive power of the models, and the extent to which they were tailored to each controller. The behaviour of the participants in the study and the behaviour of an interpreter executing their m...

Learning to control dynamic systems with unknown models is a challenging research problem. However, most previous work that learns qualitative control rules does not construct qualitative states | a proper partition of continuous state variables has to be designed by human users and given to the learning programs. We design a new learning method that learns appropriate qualitative state represe...

Changes in operating conditions can alter performances of nonlinear processes controlled with a fixed PID algorithm. One way of preventing degradation is to develop a supervised control system. In this paper we present some results about fuzzy gain scheduling of a double PID controller for stabilization of an inverted pendulum on a cart. First, on the basis of the responses obtained with an uns...

Reinforcement learning has been active research area not only in machine learning but also in control engineering, operation research and robotics in recent years. It is a model free learning control method that can solve Markov decision problems. Q-learning is an incremental dynamic programming procedure that determines the optimal policy in a step-by-step manner. It is an online procedure for...