No abstract

In this paper, a novel non-linear system identi cation methodology is developed employing the features of the arti cial immune system. A simpli ed 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 eVectiveness of the proposed algorithm, a simulation example on a two-link robot was studied. This algorithm can achieve robustness and eYciency in identifying complex non-linear systems. The simulation results show that the identi ed immune models are robust to noise and various uncertainties in the robot dynamics.

In this paper, a novel approach to model-based fault detection for non-linear systems is presented. An immune model of the system is used for the generation of residual. The orthogonal least-squares method is implemented to select the significant receptor vectors of the immune model. After the model identification, the filtered residual scheme and the fault alarm concentration are applied for the fault detection. To verify and demonstrate the performance of the proposed methodology, a simulation example on a two-link robot was studied. The results show the effectiveness and robustness in both system identification and fault detection.