Development of novel hybrid metaheuristic algorithms for identification and control in the delta domain

Abstract

The thesis develops five novel hybrid metaheuristic algorithms as per Talbi s taxonomy of hybrid metaheuristics. Firefly algorithm is integrated with bacterial foraging, flower pollination, pattern search and grey wolf optimizer respectively to develop high performance computing algorithms. Two types of test functions, namely unimodal and multi-modal of relatively high dimension are considered to validate each of the hybrid propositions. The results obtained are compared with the parent as well as some standard and latest heuristics reported in the literature. The results show great promise for the hybrid algorithms developed in terms of convergence speed and accuracy. These firefly-based hybrid algorithms has further been applied for identification of linear dynamic systems with static nonlinearities in the delta domain unifying continuous and discrete time analyses at high sampling rate. A test system with different polynomial nonlinearities has been considered for hammerstein and wiener system identification in continuous, discrete and delta domain. Delta operator modelling provides a unified approach for system identification matching continuous and discrete-delta results at high sampling frequency. Pseudo random binary sequences (PRBS), contaminated with white noise of fixed signal-to-noise (SNR), have been taken up as the input signal to estimate the unknown model parameters as well as static nonlinear coefficients. The hybrid algorithms not only outperform the parent heuristics of which they are constituted but also prove better as compared to some of the standard and latest heuristic techniques available in the literature. Delta operator parameterization presents a unified framework in analysis and design of discrete-time systems, in which the resultant model converges to its continuous-time counterpart at high sampling limit.