Intelligent Decoupling PID Control of a Class of Complex Industrial Processes

For complex industrial processes with strong couplings, high nonlinearities and uncertainties, conventional proportional-integral-differential (PID) control alone in distributed control systems (DCS) cannot achieve satisfactory performances. To deal with such problems, a nonlinear intelligent decoupling PID control strategy is developed, which can be easily implemented in DCS. The control system is based on the integration of conventional PID controllers, a decoupling compensator and a neural feedforward compensator for the unmodeled dynamics. The parameters of such controller are determined by multivariable generalized minimum variance (GMV) decoupling control law. Multi-layer neural networks (MNNs) are adopted to estimate and compensate the unmodeled dynamics adaptively. All the signals in the closed loop are guaranteed to be globally bounded and the tracking error is convergent. Theoretical analysis, simulation results of the system with abrupt variations, and simulations of the ball mill coal-pulverizing system show the effectiveness and strong robustness of the proposed controller