Zikang Su

A novel robust hybrid gravitational search algorithm for reusable launch vehicle approach and landing trajectory optimization

The approach and landing (A&L) trajectory optimization is a critical problem for secure flight of reusable launch vehicle (RLV). In this paper, the A&L is divided into two sub-phases, glide phase and flare phase respectively. The flare phase is designed firstly based on the desired touchdown (TD) states. Then, the glide phase is optimized using a proposed novel robust hybrid algorithm that combines advantages of the gravitational search algorithm (GSA) and gauss pseudospectral method (GPM). In the proposed hybrid algorithm, an improved GSA (IGSA) is presented to enhance the convergence speed and the global search ability, by adopting the elite memory reservation strategy and an adaptive gravitational constant adaption with individual optimum fitness feedback. At the beginning stage of search process, an initialization generator is constructed to find an optimum solution with IGSA, due to its strong global search ability and robustness to the initial values. When the change in fitness value satisfies the predefined value, the IGSA is replaced by the GPM to accelerate the search process and to get an accurate optimum solution. Finally, the Monte Carlo simulation results are analyzed in detail, which demonstrate the proposed method is practicable. The comparison with GSA and GPM shows that the hybrid algorithm has better performance in terms of convergence speed, robustness and accuracy for solving the RLV A&L trajectory optimization problem. The reusable launch vehicle (RLV) approach and landing (A&L) trajectory is formulated into two sub-phases: glide and flare.A novel robust hybrid optimization algorithm combined advantages of gravitational search algorithm (GSA) and gauss pseudospectral method (GPM) is proposed.The GSA is improved by adding the elite memory reservation strategy and adaptive gravitational constant adaption.The hybrid algorithm is firstly applied to the trajectory optimization of RLV A&L, comparative study is presented to further demonstrate its superiority.

A hybrid backtracking search optimization algorithm for nonlinear optimal control problems with complex dynamic constraints

Nonlinear optimal control (NOC) problem with complex dynamic constraints (CDC) is difficult to compute even with direct method. In this paper, a hybrid two-stage approach integrating an improved backtracking search optimization algorithm (IBSA) with the hp-adaptive Gauss pseudo-spectral methods (hpGPM) is proposed. Firstly, BSA is improved to enhance its convergent speed and the global search ability, by adopting the harmony search strategy and an adaptive amplitude control factor with individual optimum fitness feedback. Then, at the beginning stage of the hybrid search process, an initialization generator is constructed using IBSA to find a near optimum solution. When the change in fitness function approaches to a predefined value which is small enough, the search process is replaced by hpGPM to accelerate the search process and find an accurate solution. By this way, the hybrid algorithm is able to find a global optimum more quickly and accurately. Two NOC problems with CDC are examined using the proposed algorithm, and the corresponding Monte Carlo simulations are conducted. The comparison results show the hybrid algorithm achieves better performance in convergent speed, accuracy and robustness. The improved BSA (IBSA) is proposed to enhance the convergent speed and the global search ability, by adopting the harmony search strategy and an adaptive amplitude control factor with individual optimum fitness feedback.The hp-adaptive gauss pseudospectral method (hpGPM) is adopted to overcome the drawbacks of the fixed discrete points generated during the conventional controls parameterization.A novel hybrid two-stage optimization algorithm integrating the IBSA with the hpGPM is proposed to find a global optimum more quickly and accurately.

Autonomous aerial refueling precise docking based on active disturbance rejection control

In this paper, the receiver aircraft of the AAR is modeled, and the disturbances are presented. The longitudinal and the lateral model of the receiver are correspondingly transformed into relevant three second-order systems and a third-order system. It makes the motion model more convenient for controller design, and the scale separation is avoided at the mean time. Then, the ADRC is firstly applied to the precise docking controller design to adequately reject the different complex disturbances during the docking of AAR. Comparative simulation results show that the ADRC achieves better performance in AAR docking control under complex flow interference.

Hybrid UAV path planning based on interfered fluid dynamical system and improved RRT

In this paper, a hybrid strategy based on interfered fluid dynamical system (IFDS) and improved rapidly-exploring random tree (IRRT) is proposed for the unmanned aerial vehicle (UAV) route planning problem in 3-dimensional complex environments. By imitating the phenomenon of fluid flow, the IFDS method can plan a smooth and safe path quickly, but the route may fall into the concave area produced by some overlapping obstacles. Hence the IFDS method is combined with IRRT, which introduces the target probability and heuristic evaluation function on the basis of the traditional RRT. In this hybrid method, the IRRT method can be adopted as the framework of route planning, where the expanding nodes can be computed by IFDS algorithm. The simulation results by different methods prove that this method is of good performance of space searching and obstacle avoidance in 3-dimensional path planning.

Time-optimal memetic whale optimization algorithm for hypersonic vehicle reentry trajectory optimization with no-fly zones

A novel time-optimal memetic whale optimization algorithm (WOA) integrating the Gauss pseudo-spectral methods (GPM), is proposed in this paper for the hypersonic vehicle entry trajectory optimization problem with no-fly zones. The WOA is featured with the strong global search ability and non-sensitive to the initial values, but also shows poor searching convergence speed around the global optimum. Conversely, GPM may be sensitive to the initial solution and easily trapped in a local optimum, but it also possesses more rapid convergence speed around the optimum and higher searching accuracy. Thus, a memetic optimization algorithm which contains a two-stage approach mechanism is proposed for searching the global optimum. The first searching stage, which is driven by an improved WOA (IWOA), works as an initializer of the entire searching due to its strong global search ability and non-sensitive to the initial values. The local optimum reservation and adaptive amplitude factor updating strategy are established to improve the convergent speed and the global search ability of the WOA. Once the changing of fitness value satisfies the predefined criterion, the next searching stage driven by GPM will take the place of the IWOA to expedite the search process around optimum and to obtain a precise global optimal solution. By this hybrid way, the proposed optimization algorithm may find an optimum more quickly and accurately. Simulation results show the proposed algorithm possesses faster convergence speed, higher accuracy, and stronger robustness for the hypersonic vehicle entry trajectory optimization.