This paper explores a novel fuzzy fault-tolerant control scheme for hypersonic flight vehicles (HFVs) that accounts for parameter perturbations and system disturbances, thereby achieving flexible prescribed properties and designated-time convergence. In contrast to existing studies, this framework incorporates the more detrimental elevator stuck fault within the context of prescribed performance control (PPC). To mitigate the fragility issues associated with PPC in fault-tolerant control, we propose an innovative approach that integrates a fault sensing system along with a readjustment prescribed envelope. This is accomplished by transforming the original HFVs model into an imprecise pure feedback model, allowing for the design of low-complexity fuzzy controllers with minimal model dependence through the utilization of higher-order error functions and fuzzy logic systems. Finally, numerical simulations validate both the effectiveness and superiority of the proposed method.

This article is devoted to the exploration of a novel design process for synthesizing fuzzy prescribed performance control (PPC), with a specific emphasis on achieving preselected behaviors in discrete-time. To overcome the limitations of existing studies that solely rely on a fixed and inflexible sliding mode control framework, we put forward an innovative indirect stabilization method that offers a practical and readily implementable design process in discrete-time. Different from current approaches, our method ensures enhanced prescribed performance by guaranteeing finite-time convergence and avoiding high frequency chattering. To achieve this, we directly develop discrete-time PPC protocols using a low-computational fuzzy approximation strategy based on completely unknown system dynamics, thus eliminating the need for complex model equivalent reconstruction as demanded by previous studies. Finally, we verify the effectiveness and superiority of our proposed design.

This study focuses on a low-computational neural discrete-time attitude controller for the seeker stabilized platform, with the aim of ensuring fixed-time prescribed behaviors in tracking errors through an event-triggered approach. In contrast to existing reaching-law- based discrete-time prescribed performance control (DPPC) methodologies that rely on fully/partially accurate model plants, our approach establishes discrete-time protocols that accommodate unknown system dynamics while guaranteeing fixed convergence time and minimizing steady-state error to its utmost extent. This is achieved through a newly constructed design framework that completely deviates from current DPPC practices. Our framework incorporates fixed-time performance functions, the back-stepping procedure, and innovative accumulation terms, all of which synergistically enhance steady-state accuracy while obviating the need to calculate virtual controller differences. Furthermore, we utilize neural networks to approximate unknown system dynamics and devise an event-triggered mechanism instead of a time-based one for intermittently updating neural approximators, thereby effectively reducing computational costs. Finally, comparative validation results demonstrate that the proposed control precision has been enhanced by 27% to one or more orders of magnitude and the simulation run time has been reduced by more than 26% under identical conditions compared with existing strategies.

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.

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.

Waverider vehicles (WVs) have the prominent advantage of long-distance and rapid delivery over traditional vehicles, but meanwhile, it also brings new challenges in control system design. Consequently, it is crucial to develop a reliable controller with good transient performance and steady-state accuracy for WVs. This article introduces a non-fragile prescribed performance control (NPPC) scheme that incorporates a precise flexible term for WVs subject to actuator constraints. The proposed NPPC scheme addresses the fragility issue of traditional PPC through two approaches: firstly, by designing a finite-time anti-saturation compensation system (FACS) with adjustable convergence time to accurately compensate for tracking errors; secondly, by utilizing the FACS as an error sensor and developing a precisely tunable prescribed performance flexible term. Moreover, uncertainty-estimation-free controllers are designed for WVs under actuator constraints based on the NPPC scheme and backstepping design approach. Additionally, the stability of the proposed controllers is proven through Lyapunov analysis while their advantages are further demonstrated and validated through comparative simulations.

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 upward and the lower envelope downward 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. In addition, 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.