Robust adaptive control of nonlinearly parametrized multivariable systems with unmatched disturbances

Abstract

SummaryA new robust adaptive control scheme is developed for nonlinearly parametrized multivariable systems in the presence of parameter uncertainties and unmatched disturbances. The developed control scheme employs a new integrated framework of a functional bounding technique for handling nonlinearly parametrized system dynamics, an adaptive parameter estimation algorithm for dealing with parameter uncertainties, a nonlinear feedback controller structure for stabilization of interconnected system states, and a robust adaptive control design for accommodating unmatched disturbances. It is proved that such a new robust adaptive control scheme is capable of ensuring the global boundedness and mean convergence of all closed‐loop system signals. A complete simulation study on an air vehicle system with nonlinear parametrization in the presence of an unmatched wind disturbance is conducted, and its results verify the effectiveness of the proposed robust adaptive control scheme.

Cooperative fault estimation for a class of heterogeneous multi‐agents with stochastic nonlinearities based on finite impulse response filter

“…. Now, it is clear to see that the fault estimation problem for system (1) has been transferred to finding the optimal filter gain p * i (q) for the FIR in (20) such that the estimator error r i (q) converges to 0.…”

“…For the fault estimator (20), the main result for system ( 1) is ready to be presented based on the Proposition 2.…”

“…S4: Obtain the optimal FIR filter gain p * i (q) based on Theorem 1; S5: Construct the FIR filter based estimator as (20)and derive the corresponding fault estimation results for each selected agent;…”

“…It should be noted that many practical systems can be described by the discrete time-varying stochastic nonlinear state space model (1), such as mobile robots, cooperating UAV team operations (surveillance and reconnaissance), formation flying of UAV's and satellites, vehicle platoons. For these systems, we can calculate the optimal filter gain p * i (q) and construct the FIR filter based estimator as (20) to derive the corresponding fault estimation following the Algorithm 1 above. Considering the difference between practical application and theoretical research, if necessary, the optimal filter gain may need to be slightly adjusted to obtain better performance.…”

Filtering‐based recursive least squares estimation approaches for multivariate equation‐error systems by using the multiinnovation theory

“…[12][13][14][15][16][17][18] However, most industrial processes are essentially multivariate systems with complex structures and diverse noises. [19][20][21][22][23] Hence in order to meet the requirements of practical engineering applications, some identification methods for multivariate systems have been proposed by researchers in recent years, which made the theories of system identification more comprehensive. 24,25 For instance, Anderson et al considered the problem of identifiability of the parameters of a high frequency multivariate autoregressive model from mixed frequency time series data, and the identifiability for generic parameter values using the population second moments of the observations was demonstrated.…”