In this paper, a novel non-linear system identification methodology is developed employing the features of the artificial immune system. A simplified incremental approach integrated with the maximum entropy principle and an instantaneous feedback mechanism is proposed to reorganize the system's parameters simultaneously. To verify and demonstrate the effectiveness of the proposed algorithm, a simulation example on a two-link robot was studied. This algorithm can achieve robustness and efficiency in identifying complex non-linear systems. The simulation results show that the identified immune models are robust to noise and various uncertainties in the robot dynamics.
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