Self-adaptive predictive control of slurry transportation-rate in the dredging pipeline

A new single neuron self-adaptive predictive scheme is introduced for controlling the slurry transportationrate in the dredging pipeline. To deal with such a process of high inertia, long time-delay, time-varying parameters and the difficulty in modeling, this scheme makes use of the self-learning capability of the neuron network to study the control system structure, parameters, uncertainties and nonlinear characteristics; combines with the identification-free self-adaptive control algorithm proposed by Marsik and Strejc to carry out the on-line adjustment of control variables; incorporates the Smith predictor and employs the optimization searching algorithm for on-line optimizing the time-delay parameter to enhance the robustness and adaptability of the predictive algorithm. Field experiments are also carried out to test the performance of the proposed control scheme. Results show that the control performance is satisfactory in various dredging environments, even the time-delay is significant; the control system compensates external disturbances and exhibits desirable tracking ability.