A prescribed performance control scheme based on the three-inflection-point hyperbolic function and predefined time performance function is proposed to solve the trajectory tracking problem of the forward-tilting morphing aerospace vehicle with time-varying actuator faults. To accurately estimate the loss degree of actuator faults, an immersion and invariance observer based on the predefined time dynamic scale factor is designed to estimate and compensate it. A composite dynamic sliding mode surface is designed using a three-inflection-point hyperbolic function, and a novel three-inflection-point sliding mode control framework is proposed. The convergent domain of the sliding manifold is adjusted by parameters, and the system error convergence is controllable. A transfer function is designed to eliminate the sensitivity of the three-inflection-point hyperbolic sliding mode to the unknown initial state, and combined with the barrier Lyapunov function, and the performance constraint of the system is realized. The global asymptotic stability of the system is demonstrated using a strict mathematical proof. The effectiveness and superiority of the proposed control scheme are proven by simulation experiments.

Dynamic Reactive Power Optimization (DRPO) is a large-scale, multi-period, and strongly coupled nonlinear mixed-integer programming problem that is difficult to solve directly. First, to handle discrete variables and switching operation constraints, DRPO is formulated as a nonlinear constrained two-objective optimization problem in this paper. The first objective is to minimize the real power loss and the Total Voltage Deviations (TVDs), and the second objective is to minimize incremental system loss. Then a Filter Collaborative State Transition Algorithm (FCSTA) is presented for solving DRPO problems. Two populations corresponding to two different objectives are employed. Moreover, the filter technique is utilized to deal with constraints. Finally, the effectiveness of the proposed method is demonstrated through the results obtained for a 24-hour test on Ward & Hale 6 bus, IEEE 14 bus, and IEEE 30 bus test power systems. To substantiate the effectiveness of the proposed algorithms, the obtained results are compared with different approaches in the literature.

A Projection Pursuit Dynamic Cluster (PPDC) model optimized by Memetic Algorithm (MA) was proposed to solve the practical problems of nonlinearity and high dimensions of sample data, which appear in the context of evaluation or prediction in complex systems. Projection pursuit theory was used to determine the optimal projection direction; then dynamic clusters and minimal total distance within clusters (min TDc) were used to build a PPDC model. 17 agronomic traits of 19 tomato varieties were evaluated by a PPDC model. The projection direction was optimized by Simulated Annealing (SA) algorithm, Particle Swarm Optimization (PSO), and MA. A PPDC model, based on an MA, avoids the problem of parameter calibration in Projection Pursuit Cluster (PPC) models. Its final results can be output directly, making the cluster results objective and definite. The calculation results show that a PPDC model based on an MA can solve the practical difficulties of nonlinearity and high dimensionality of sample data.