  A novel self-organizing fuzzy proportional-integral-derivative (SOF-PID)
control system is proposed in this paper. The proposed system consists of a
pair of control and reference models, both of which are implemented by a
first-order autonomous learning multiple model (ALMMo) neuro-fuzzy system. The
SOF-PID controller self-organizes and self-updates the structures and
meta-parameters of both the control and reference models during the control
process "on the fly". This gives the SOF-PID control system the capability of
quickly adapting to entirely new operating environments without a full
re-training. Moreover, the SOF-PID control system is free from user- and
problem-specific parameters, and the uniform stability of the SOF-PID control
system is theoretically guaranteed. Simulations and real-world experiments with
mobile robots demonstrate the effectiveness and validity of the proposed
SOF-PID control system.
