The perturbations in model parameters of hypersonic flight vehicles (HFVs) are highly likely to induce fluctuations in control error, which can potentially render the existing prescribed performance control (PPC) singular and pose a threat to flight safety. Therefore, our objective is to propose an adaptive fuzzy safety control protocol for HFVs that aims to achieve adaptable prescribed behaviors in the presence of parameter perturbations. To accomplish this, we initially develop a novel error-sensing system for timely detection and forecasting of error fluctuations. Building upon this foundation, we further define an adjustment mechanism that appropriately adjusts the upper envelope upwards and the lower envelope downwards at regular intervals. In contrast to existing fixed PPC approaches, the proposed sensing and adjustment mechanism enables both velocity and altitude tracking errors to satisfy a new type of adaptable prescribed qualities, thereby ensuring safe flight control of HFVs. Additionally, we explore low-computational-burden fuzzy approximation techniques that minimize the required online adaptive parameters while guaranteeing excellent real-time control performance. Finally, comparative simulations are conducted to validate the proposed method.

In order to deal with the new threat of low altitude slow small (LSS) targets in air defense operations and provide support for LSS target interception decision, we propose a simple and reliable LSS target threat assessment method. Based on the detection capability of LSS targets and their threat characteristics, this paper proposes a threat evaluation factor and threat degree quantization function in line with the characteristics of LSS targets. LSS targets not only have the same threat characteristics as traditional air targets but also have the unique characteristics of flexible mobility and dynamic mission planning. Therefore, we use analytic hierarchy process (AHP) and information entropy to determine the subjective and objective threat factor weights of LSS targets and use the optimization model to combine them to obtain more reliable evaluation weights. Finally, the effectiveness and credibility of the proposed method are verified by experimental simulation.

This article is dedicated to safety flight control of hypersonic vehicles that serve as long-range strategic transport aircraft, with the aim of alleviating the inherent fragility defect in existing prescribed performance control (PPC) schemes. In pursuit of this goal, we first establish a safety boundary that offers early warnings for fluctuations in hypersonic tracking errors and serves as a crucial mechanism for envelope adjustment. Building on this groundwork, we further develop a universal sensing-adjustment system with dynamically activated states triggered by the defined safety boundary, enabling active readjustment of the prescribed envelope boundaries. This results in a new PPC scheme capable of promptly detecting error fluctuations and smoothly readjusting prescribed envelopes, effectively addressing the fragility defect associated with existing protocols while ensuring hypersonic flight safety. Moreover, our proposed hypersonic controller does not necessitate approximators or learning parameters required for current fuzzy/neural control approaches, showcasing a low-computational design framework. Finally, we assess the efficiency of our approach by conducting comparative simulations.

We address the problem of tracking control for uncertain discrete-time systems with unknown and unavailable plant dynamics, aiming to achieve prescribed performance within a pre-set convergence time for tracking errors. Our proposed control protocol is independent of the knowledge of system dynamics or the utilization of approximators/estimators. Instead, we employ transformed errors to develop novel nonlinear functions for control feedback. Consequently, we establish a new estimation/approximation-free indirect stabilization framework that serves as a standard paradigm for discrete-time prescribed performance control synthesis. Finally, simulation results applied to the missile seeker stabilized platform demonstrate the effectiveness of our approach.

The control performance of permanent magnet brush (PMB) DC motors, which are essential components in electric vehicles, is crucial for ensuring the seamless and secure operation of these vehicles. This study investigates discrete-time fuzzy-neural intelligent control with fixed-time prescribed performance for the electromechanical dynamics of PMB DC motors. Unlike existing prescribed performance control (PPC) schemes designed for continuous-time systems, our approach introduces a more practical discrete-time version of PPC that guarantees system outputs with fixed-time preselected behaviors. This advancement addresses the technical limitations faced by current discrete-time PPC methods when managing dynamic systems with time-varying sampling intervals. To achieve this objective, we integrate an enhanced fuzzy-neural approximation technique with the back-stepping design to develop a low-computational discrete-time control protocol. Our method minimizes online learning parameters while circumventing the issue known as “explosion of terms”. Finally, we compare our proposed controller against several existing alternatives to demonstrate its effectiveness and superiority.