PSO Optimized Active Disturbance Rejection Control for Aircraft Anti-Skid Braking System

Abstract

A high-quality and secure touchdown run for an aircraft is essential for economic, operational, and strategic reasons. The shortest viable touchdown run without any skidding requires variable braking pressure to manage the friction between the road surface and braking tire at all times. Therefore, the manipulation and regulation of the anti-skid braking system (ABS) should be able to handle steady nonlinearity and undetectable disturbances and to regulate the wheel slip ratio to make sure that the braking system operates securely. This work proposes an active disturbance rejection control technique for the anti-skid braking system. The control law ensures action that is bounded and manageable, and the manipulating algorithm can ensure that the closed-loop machine works around the height factor of the secure area of the friction curve, thereby improving overall braking performance and safety. The stability of the proposed algorithm is proven primarily by means of Lyapunov-based strategies, and its effectiveness is assessed by means of simulations on a semi-physical aircraft brake simulation platform.

Observer Sliding Mode Control Design for lower Exoskeleton system: Rehabilitation Case

“…The optimization algorithm based on PSO [32][33][34][35][36][37][38][39][40] results in optimal design parameters for SMC and finding the optimal values of (𝑐 = 0.1), (𝐾 = 40) for ( 15) and (𝐾 1 = 6.93), (𝐾 2 = 600.59) for (17). The values of (𝐾 𝑝 ), (𝐾 𝑑 ), ( 𝛽 1 ), ( 𝛽 2 ) and (𝛽 3 ) are calculated according to (7) and (14) when choosing (𝑤 𝑐 = 24.5 𝑟𝑎𝑑/𝑠𝑒𝑐) and (𝑤 𝑜 = 4𝑤 𝑐 ).…”

Active Disturbance Rejection Control Design with Sensitivity Constraint for Drum Water Level

“…Suppose that f is bounded, and r is independent of time. Based on the controlled plant in Equation (20) and the equivalent structure in Equations ( 28) and (29), the sufficient condition of the bounded tracking error y r − is that all roots of ( ) ( )…”

“…With the strong ability of ESO to estimate and compensate for the total disturbance, the ADRC has strong robustness to handle system uncertainties [23]. With comprehensive theoretical analysis and rich parameter tuning methods [24], the excellent control performance has been verified in different applications, such as applications in robotic systems [25], main steam pressure systems [26], superheater temperature systems [27], particleboard glue systems [28], aircraft anti-skid braking systems [29] and compression liquid chiller systems [30].…”

Laser Cleaning Surface Roughness Estimation Using Enhanced GLCM Feature and IPSO-SVR

“…Then, the Tamura coarseness feature [27], Alexnet [28] abstract depth feature, single blind/referenceless image spatial quality evaluator (BRISQUE) [29,30] feature, and a novel enhanced gray level co-occurrence matrix (EGLCM) feature are extracted. Finally, support vector regression (SVR) [31,32] is used to fit the surface roughness, and the improved particle swarm optimization (IPSO) algorithm is developed to optimize the training process parameters of SVR [33,34]. This method has a high degree of computational accuracy through our experiments.…”