No abstract

No abstract

In this investigation, self-learning salp swarm optimization (SLSSO) based proportional- integral-derivative (PID) controllers are proposed for a Doha reverse osmosis desalination plant. Since the Doha reverse osmosis plant (DROP) is interacting with a two-input-two-output (TITO) system, a decoupler is designed to nullify the interaction dynamics. Once the decoupler is designed properly, two PID controllers are tuned for two non-interacting loops by minimizing the integral-square-error (ISE). The ISEs for two loops are obtained in terms of alpha and beta parameters to simplify the simulation. Thus designed ISEs are minimized using SLSSO algorithm. In order to show the effectiveness of the proposed algorithm, the controller tuning is also accomplished using some state-of-the-art algorithms. Further, statistical analysis is presented to prove the effectiveness of SLSSO. In addition, the time domain specifications are presented for different test cases. The step responses are also shown for fixed and variable reference inputs for two loops. The quantitative and qualitative results presented show the effectiveness of SLSSO for the DROP system.

The ability of a neural network to realize some complex nonlinear function makes them attractive for system identification. In the recent past, neural networks trained with back-propagation (BP) learning algorithm have gained attention for the identification of nonlinear dynamic systems. Slower convergence and longer training times are the disadvantages often mentioned when the standard BP algorithm are compared with other competing techniques. In addition, in the standard BP algorithm, the learning rate is fixed and that it is uniform for all weights in a layer. In this paper, we present an improvement to the standard BP algorithm based on the use of an adaptive learning rate and momentum term, where the learning rate is adjusted at each iteration to reduce the training time. Simulation results indicate a faster convergence speed and better error minimization as compared to other competing methods.

No abstract