  A data-driven computational heuristic is proposed to control MIMO systems
without prior knowledge of their dynamics. The heuristic is illustrated on a
two-input two-output balance system. It integrates a self-adjusting nonlinear
threshold accepting heuristic with a neural network to compromise between the
desired transient and steady state characteristics of the system while
optimizing a dynamic cost function. The heuristic decides on the control gains
of multiple interacting PID control loops. The neural network is trained upon
optimizing a weighted-derivative like objective cost function. The performance
of the developed mechanism is compared with another controller that employs a
combined PID-Riccati approach. One of the salient features of the proposed
control schemes is that they do not require prior knowledge of the system
dynamics. However, they depend on a known region of stability for the control
gains to be used as a search space by the optimization algorithm. The control
mechanism is validated using different optimization criteria which address
different design requirements.
