The finite-time control problem of the generalized discrete-time systems is a major challenging problem to be solved. In this work, the discrete-time adaptive fuzzy finite-time control scheme is presented for nonlinear systems with discrete form and uncertain dynamics. The fuzzy logic systems (FLSs) are employed to approach uncertain dynamics of systems. Then, the sufficient condition of semiglobal finite-time stability of discrete-time nonlinear systems (DTNSs) is constructed. The discrete-time filters are used to overcome the noncausal problem from the traditional backstepping controller design processes of DTNSs. With the help of the discrete finite-time theorem, discrete-time filters, backstepping techniques, and FLSs, an adaptive fuzzy discrete finite-time control scheme is proposed in a unified framework. The stability analysis can ensure the boundedness of all signals of the closed-loop systems and the achieving the desired tracking control performance in the finite-time. Finally, two examples are adopted to verify abovementioned claims.

In the field of solar photovoltaic (PV) systems, the accurate and reliable extraction of parameters from PV models is crucial for effective simulation, evaluation, and control. Although various optimization algorithms have been widely used for parameter extraction in solar PV systems, the accuracy and reliability of the parameters extracted by these methods usually fall short of the expected standards. To address these shortcomings, a novel hybrid algorithm that combines the improved marine predators algorithm (MPA) with the equilibrium optimizer (EO), named IMPAEO, is proposed. In the IMPAEO, an elite opposition-based learning strategy is introduced to expand the exploration range of the algorithm to enhance population diversity; an adaptive weight coefficient is employed to improve the updating rule of the MPA, facilitating a balance between global exploration and local exploitation; the EO operator is integrated to enhance the performance of the MPA in the local exploitation phase to improve the solution quality and convergence speed; and the linear population size reduction (LPSR) technique is introduced to dynamically reduce the population size and improve the search performance. The effectiveness of the proposed IMPAEO is validated using the CEC2017 benchmark test suite, which is also applied to extract parameters of the single diode model, the double diode model, and three different PV modules. Comparative analysis with other algorithms demonstrates that the IMPAEO exhibits significant advantages in terms of solution quality, convergence speed, and algorithm stability. Consequently, the proposed IMPAEO not only proves to be efficient and reliable in parameter extraction for solar PV models but also offers a promising alternative in this field.

This study presents a novel approach to dynamic event-triggered model predictive control (MPC) for nonlinear discrete-time cyber-physical systems (CPSs) subject to simultaneous hybrid denial of service (DoS) and deception attacks on the sensor-to-controller and controller-to-actuator communication channels. The proposed method introduces a dynamic event-triggered mechanism (DETM) with a predetermined offset to optimize communication efficiency in bandwidth-limited systems. Furthermore, a mechanism for detecting attacks has been developed to ensure the integrity of transmitted information, thereby mitigating the impact of hybrid attacks. Theoretical conditions are established to ensure the input-to-state practical stability of the closed-loop system using the proposed method. Finally, simulation cases are presented to validate the superiority of the proposed method in comparison to existing approaches in scenarios where CPSs encounter similar hybrid attacks.