![]() ![]() ![]() The above study focuses more on the PSC of turbofan engines and less on multivariable propulsion systems the initial value sensitivity and the local convergence problems of the traditional algorithm will be more prominent due to the increased number of control variables and the harsh constraints of aero-propulsion modeling. Other methods, such as the direct method, the Beetle Antennae Search algorithm, and IA are also proposed. PSO provides an ideal optimization effect under maximum thrust optimization condition, but it depends on multiple-parameter tweaking, which cannot adapt to the multiple operating states. Several academics have used GA and EA to tackle optimization issues. With the rapid growth of artificial intelligence, intelligence algorithms have been gradually applied to the PSC, overcoming the initial value dependence of traditional methods. According to Chen HY, the FSQP could obtain the optimal operating conditions of the double bypass variable cycle engine in different conditions. Later, numerous academics engaged in the research and expansion of LP, including SLP, FSQP, and SQCQP. In terms of the numerical simulation study, the linear programming (LP) approach is employed as the performance optimization algorithm in the NASA report, and Orme, J.S used LP in the airborne adaptive model. Under supersonic conditions, thrust can be enhanced by 9%, fuel consumption can be lowered by 8%, and turbine temperature can be reduced by 48K. NASA Dryden Flying Test Center’s Orme J.S conducted the PSC supersonic flight test. S applied the full envelope PSC to PW1128 powered F-15 aircraft. Scholars have long researched and experimented with PSC, attempting to apply it to the propulsion system. Therefore, research on comprehensive performance-seeking control algorithms for supersonic propulsion systems is required, and the fusion and conversion of performance-seeking controls with sensor-based controllers necessitates concentrated effort. performance-seeking control (PSC) is an essential mode of intelligent engine control, which is implemented by using optimization algorithms to find the maximum performance potential (maximum thrust, minimum fuel consumption, or minimum turbine temperature) of the model while satisfying various operating limitations. Propulsion systems are characterized by complex structures and wide operating envelopes that cannot be fully exploited by conventional controllers, necessitating the use of fusion optimization approaches. The present control system mostly uses sensor-based control design principles to handle non-measurable parameters, including thrust, temperature, and safety margins indirectly through measurable parameters. With the rapid development of supersonic aero technology, the propulsion system controller requires increased reliability and performance potential, and the control system object increasingly transfers from engine to comprehensive propulsion system. This study presents a theoretical foundation and engineering applications for the design of supersonic propulsion system controllers. Maximum installed thrust mode capable of achieving no thrust loss and a maximum fluctuation rate within 2000 N/s, with the largest variation in thrust during the conversion being less than 0.9% under the minimum turbine temperature mode and the minimum specific fuel consumption mode. ![]() The intelligent fusion controller is able to achieve a smooth transition of performance modes to ensure that the engine is provided with a stable thrust during operation under supersonic conditions, and the potential for performance optimization is maintained at a reasonable level. The analysis reveals that the NSDE-GWO hybrid algorithm, which takes advantages of the two algorithms, significantly improves the computing efficiency and optimization accuracy, achieving better optimization solutions in three different modes. To investigate the performance potential of the aero-propulsion system and the problem of control mode conversion, this paper takes the inlet/engine integrated component-level model as the research object, and a performance-seeking control (PSC) scheme based on the neighborhood-based speciation differential evolution–grey wolf optimization (NSDE-GWO) algorithm is designed and combined with an active disturbance rejection control (ADRC) to establish a multivariable fusion closed-loop control system. ![]()
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