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Aiming at the defect that traditional PPC usually leads to control singularity when dealing with the input constrained problem, a new non-fragile prescribed performance control (PPC) methodology is proposed based on neural approximation for waverider vehicle (WV). To deal with the saturation of velocity control input and altitude control input, compensated systems are devised. Furthermore, the states of compensated systems are used to construct adaptive re-adjusting terms that are further applied to improve the constraint envelopes of traditional PPC. Besides, neural networks are introduced to approximate the WV normalized unknown terms, which guarantees the robust performance. The advantage of the proposed method is that it overcomes the fragile defect of traditional PPC, and also reduces the control complexity and online computational load. Finally, the effectiveness and superiority of the exploited method are validated via numerical simulation.

In view of the complex structure of neural network prescribed performance control method and low control real-time performance, a low-complexity prescribed performance inversion control method is proposed for waverider vehicle(WV). Firstly, a new type of performance function with simple form and independent of initial error is designed to envelop the tracking error. Secondly, using a conversion strategy, the inequality constraint on the tracking error is equivalently converted into an equality constraint by introducing a conversion error. Then, based on the conversion error, a new type of inversion controller without any learning parameters is designed for WV, and it avoids the “differential term expansion” problem of traditional back-stepping control. Finally, the simulation results show that the proposed method is expected to guarantee good dynamic performance and steady-state accuracy of tracking error.

The approximate optimization is considered though adaptive dynamic programming (ADP) method for waverider vehicles (WVs). This method can achieve the stability, robustness, and near optimization of the tracking problem for WVs. First, baseline controllers are designed for both velocity subsystem and altitude subsystem via neural network approximation. Meantime, novel auxiliary systems are implemented to compensate the desired control law with actuator saturation. Then, transient optimal controllers are developed using single-network adaptive critic method. Moreover, the stability of closed-loop systems, and the convergence of both the tracking error and the auxiliary system are theoretically proved. Finally, the superiority of the proposed method is proved via compared simulation results. [ABSTRACT FROM AUTHOR]