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This paper presents an efficient multi-objective optimization method, focusing on aerodynamic optimization of a diverterless supersonic inlet (DSI) in transonic and supersonic flight conditions. The DSI inlet, through scrutinizing the Bump shape has potential to attain greater aerodynamic performance on exit plane of inlet. However, the high cost of computational fluid dynamic (CFD) simulations raises a significant challenge in the DSI optimization process. In order to obtain solution set in few numbers of objective function calls, a meta-model multi-objective particle swarm optimization (MOPSO) method is proposed based on a self-adaptive Kriging surrogate model, and applied to solve this kind of costly black-box optimization problem. The Kriging model is updated by using a dynamic expected hyper-volume improvement (EHVI) sample metric, which is developed by analyzing disadvantages of the original sample criterion. With the help of the dynamic sample metric, simulation results show that the surrogate-based MOPSO algorithm can obtain plenty enough non-dominated solutions and achieve high precision in the approximation of the Pareto front. In terms of DSI inlet optimization, the bump shape is parameterized by free form deformation (FFD) method, and the total pressure distortions of inlet exit plane are treated as two minimization objectives under transonic and supersonic flight conditions. A well distributed non-dominated solution set is generated by the proposed algorithm within the context of a small call number of cost evaluations, and optimized inlet configurated by the selected solution has better aerodynamic characteristics compared with the initial inlet.

This paper proposes an active fault-tolerant control strategy for a quadrotor helicopter against actuator faults and model uncertainties while explicitly considering fault estimation errors based on adaptive sliding mode control and recurrent neural networks. Firstly, a novel adaptive sliding mode control is proposed. In virtue of the proposed adaptive schemes, the system tracking performance can be guaranteed in the presence of model uncertainties without stimulating control chattering. Then, due to the fact that model-based fault estimation schemes may fail to correctly estimate fault magnitudes in the presence of model uncertainties, a fault estimation scheme is proposed by designing a parallel bank of recurrent neural networks. With the trained networks, the severity of actuator faults can be precisely estimated. Finally, by synthesizing the proposed fault estimation scheme with the developed adaptive sliding mode control, an active fault-tolerant control mechanism is established. Moreover, the issue of actuator fault estimation error is explicitly considered and compensated by the proposed adaptive sliding mode control. The effectiveness of the proposed active fault-tolerant control strategy is validated through real experiments based on a quadrotor helicopter subject to actuator faults and model uncertainties. Its advantages are demonstrated in comparison with a model-based fault estimator and a conventional adaptive sliding mode control.

This paper presents a dual adaptive fault-tolerant control strategy for a quadrotor helicopter based on adaptive sliding mode control and adaptive boundary layer. Within the proposed adaptive control strategy, both model uncertainties and actuator faults can be compensated without the knowledge of the uncertainty bounds and fault information. By virtue of the proposed adaptive control scheme, the minimum discontinuous control gain is adopted, which significantly reduces the control chattering effect. As compared to the existing adaptive sliding mode control schemes in the literature, larger actuator faults can be tolerated by employing the proposed control scheme while suppressing control chattering. Moreover, boundary layer is used to smoothen control discontinuity and further eliminate control chattering. Nevertheless, the choice of boundary layer thickness is a trade-off between system stability and tracking accuracy. By explicitly considering this fact, an adaptive boundary layer is developed and synthesized with the proposed adaptive control framework to ensure stability and tracking accuracy of the considered system. When the control parameter tends to be overestimated, the thickness of boundary layer can be appropriately adjusted to avoid control parameter overestimation. Simulation and experimental tests of a quadrotor helicopter are both conducted to validate the effectiveness of the proposed control scheme. Its advantages are demonstrated in comparison with a conventional adaptive sliding mode control scheme.