Ze-Wen Wang, Jin-Hua She, Guang-Jun Wang. Adaptive Equivalent-input-disturbance Approach to Improving Disturbance-rejection Performance[J]. Machine Intelligence Research, 2020, 17(5): 701-712. DOI: 10.1007/s11633-020-1230-7
Citation: Ze-Wen Wang, Jin-Hua She, Guang-Jun Wang. Adaptive Equivalent-input-disturbance Approach to Improving Disturbance-rejection Performance[J]. Machine Intelligence Research, 2020, 17(5): 701-712. DOI: 10.1007/s11633-020-1230-7

Adaptive Equivalent-input-disturbance Approach to Improving Disturbance-rejection Performance

  • This paper presents an adaptive equivalent-input-disturbance (AEID) approach that contains a new adjustable gain to improve disturbance-rejection performance. A linear matrix inequality is derived to design the parameters of a control system. An adaptive law for the adjustable gain is presented based on the combination of the root locus method and Lyapunov stability theory to guarantee the stability of the AEID-based system. The adjustable gain is limited in an allowable range and the information for adjusting is obtained from the state of the system. Simulation results show that the method is effective and robust. A comparison with the conventional EID approach demonstrates the validity and superiority of the method.
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