A systematic synthesis of optimal process control with neural networks

Infinite-time optimal controllers have been designed for a dispersion-type tubular reactor model within the framework of adaptive-critic-based neuro-controller design. For the reactor control problem, which is governed by two coupled nonlinear partial differential equations, an optimal controller synthesis scheme is presented using two sets of neural networks. One set of neural networks captures the relationship between the states and the control, whereas the other set of networks captures the relationship between the states and the co-states. This innovative approach solves the optimal controller in a feedback form. This methodology can be viewed as a practical computational tool in designing optimal controllers for distributed parameter systems in general