A real-time learning control technique for a general non-linear multivariable process is presented and applied to a laboratory plant. The proposed technique is a hybrid approach, which combines the ability of a recurrent neural network for modelling purposes and a linear pole placement control law to design the controller, providing a bridge between the "eld of neural networks and the well-known linear adaptive control methods.
An Elman-type recurrent neural network strategy is introduced to model the behaviour of the non-linear plant, using available input}output data (an unmeasurable state problem is assumed). Following a linearization technique a linear time-varying state-space model is obtained, which allows simultaneous estimation of parameters and states. Once the neural model is linearized, some well-established standard linear control strategies can be applied. With simultaneous online training of the neural network and controller synthesis, the resulting structure is an indirect adaptive self-tuning strategy.
The identi"cation and control performances of the proposed approach are investigated on a non-linear multivariable three-tank laboratory system. Experimental results show the e!ectiveness of the proposed hybrid structure.