Nonconventional control of the flexible pole-cart balancing problem: Experimental results

Emerging techniques of intelligent or learning control seem attractive for applications in manufacturing and robotics. It is however important to understand the capabilities of such control systems. In the past the inverted pendulum has been used as a test case, however, this problem is not sufficiently testing. This research therefore concentrates on the control of the inverted pendulum with additional degrees of freedom as a testing demonstrator problem for learning control system experimentation. A flexible pole is used in place of a rigid one. The transverse displacement of the flexible pole has distributed elasticity and therefore infinite degrees of freedom. The dynamics of this new system are more complex as the system needs additional parameters to be defined due to the pole's elastic deflection. This problem also has many of the significant features associated with flexible robots with lightweight links as applied in manufacturing. Novel neural network and fuzzy control systems are presented that control such a system in real time in one of its modes of vibration. A fuzzy-genetic approach is also demonstrated that allows the creation of fuzzy control systems without the use of extensive knowledge.