This paper examines the existence and global attractivity of a unique almost periodic sequence solution for a class of discrete-time Cohen-Grossberg neural networks with delays, for which some sufficient conditions are obtained. Moreover, a computer simulation is given to illustrate the correctness of our results.
This paper addressed the formation control problem of surface unmanned vessels with model uncertainty, parameter perturbation, and unknown environmental disturbances. A formation control method based on the control force saturation constraint and the extended state observer (ESO) was proposed. Compared with the control methods which only consider the disturbances from external environment, the method proposed in this paper took model uncertainties, parameter perturbation, and external environment disturbances as the compound disturbances, and the ESO was used to estimate and compensate for the disturbances, which improved the anti-disturbance performance of the controller. The formation controller was designed with the virtual leader strategy, and backstepping technique was designed with saturation constraint (SC) function to avoid the lack of force of the actuator. The stability of the closed-loop system was analyzed with the Lyapunov method, and it was proved that the whole system is uniformly and ultimately bounded. The tracking error can converge to arbitrarily small by choosing reasonable controller parameters. The comparison and analysis of simulation experiments showed that the controller designed in this paper had strong anti-disturbance and anti-saturation performance to the compound disturbances of vessels and can effectively complete the formation control.
This paper investigates parameter and order identification of a class of block-oriented nonlinear systems. By using the hierarchical identification principle, the system is divided into two subsystems, which are a linear block system and a nonlinear block system. For the purpose of solving the difficulty of estimating two sets of parameter vectors, the over-parameterization method and the key item separation technique are used, respectively. Therefore, a two-stage over-parameterization gradient-based iterative algorithm and a key term separation two-stage gradient-based iterative algorithm are derived. The simulation results indicate that the proposed algorithms are effective. Finally, the proposed method is evaluated through a battery model. The results show well agreement with the real system outputs.
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