Multivariable Adaptive Control Using an Observer Based on a Recurrent Neural Network

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 behavior of the nonlinear plant, using available input-output data (an unmeasurable states 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 nonlinear multivariable three tanks laboratory system. Experimental results show the e ectiveness of the proposed hybrid.