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A robust estimation procedure, the modified maximum likelihood method (MML), can be utilized to estimate the unknown parameters a and b in such situations.
A new observer is utilized to estimate the unmeasured states.
RBF network is utilized to estimate the equivalent affine system.
Kosambi mapping function was utilized to estimate the map distances.
In addition, high-gain observers are utilized to estimate those unmeasurable states, the auxiliary system is introduced to eliminate the constraint effects of the input saturation, and the disturbance observer is adopted to cope with unknown boundary disturbance.
Fuzzy logic systems (FLSs) are utilized to approximate the unknown nonlinear functions, and the state observers are designed to estimate immeasurable states.
Therefore, fuzzy logic systems are utilized to approximate the unknown nonlinear functions, and a fuzzy state observer is established to estimate the unmeasurable states.
Fuzzy logic systems are utilized to approximate the unknown nonlinear functions, and a fuzzy state observer is designed to estimate the unmeasured states.
Fuzzy logic systems are utilized to approximate the unknown nonlinear functions, and a switched fuzzy state observer is designed to estimate the unmeasured states.
Radial basis function neural networks (RBF NNs) are utilized to approximate the unknown nonlinear functions of the controlled systems, and a state observer is designed to estimate the unmeasured states.
In the ARC algorithm, discontinuous projection based parameter adaptive law is utilized to estimate unknown system parameters, and robust control law ensures the robustness of closed-loop control system.
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