Robust Adaptive Control for Nonlinear Aircraft System with Uncertainties

Abstract

Model reference adaptive control (MRAC) schemes are known as an effective method to deal with system uncertainties. High adaptive gains are usually needed in order to achieve fast adaptation. However, this leads to high-frequency oscillation in the control signal and may even make the system unstable. A robust adaptive control architecture was designed in this paper for nonlinear aircraft dynamics facing the challenges of input uncertainty, matched uncertainty, and unmatched uncertainty. By introducing a robust compensator to the MRAC framework, the high-frequency components in the control response were eliminated. The proposed control method was applied to the longitudinal-direction motion control of a nonlinear aircraft system. Flight simulation results demonstrated that the proposed robust adaptive method was able to achieve fast adaptation without high-frequency oscillations, and guaranteed transient performance.

Impacts of Discretization and Numerical Propagation on the Ability to Follow Challenging Square Wave Commands

“…The momentum continued, and the nonlinear output regulation has been further explored by numerous authors including Cheng, Tarn, and Spurgeon [37], Khalil [38], and Wang and Huang [39] across autonomous and nonautonomous systems. The lineage emphasized in this manuscript stems from a heritage in vehicle guidance and control techniques [8][9][10][11][12][13][14][15] extended to apply to motor controllers [17][18][19][20][21][22][23][24][25][26][27][28][29][30][31][32][33][34][35] that generate vehicle motion. Vehicle maneuvering is controlled by the actuator fins displayed in figure 3b generating navigation as displayed in figure 3a.…”

Evaluation of Adaptive and Learning in Unmanned Systems

“…Section 3 results display the results of comparative analysis of computational rate (via step size) and makes recommendations based on multi-variate figures of merit: target tracking error mean and standard deviations. [31]. Notice the square waves are rounded to reduce the deleterious challenge of discontinuity; (b) self-tuning regulators tracking square wave commands from [37].…”

“…The momentum continued, and the nonlinear output regulation has been further explored by numerous authors including Cheng, Tarn, and Spurgeon [39], Khalil [40], and Wang and Huang [41] across autonomous and nonautonomous systems. The lineage emphasized in this manuscript stems from a heritage in vehicle guidance and control techniques [8][9][10][11][12][13][14][15][16] extended to apply to motor controllers [18][19][20][21][22][23][24][25][26][27][28][29][30][31][32][33][34][35][36][37] This manuscript proposes a preferred instantiation of adaptive and learning systems [28,29] by evaluating the efficacy of motor control techniques based on iterated computational rates and system discretization. The materials and methods in section 2 first describe model discretization and then introduces the two compared: one adaptive and one learning each with interconnected lineage of research in the literature.…”

“…Figure 5. (a) Comparison of model reference adaptive control and robust adaptive control tracking square waves from[31]. Notice the square waves are rounded to reduce the deleterious challenge of discontinuity; (b) self-tuning regulators tracking square wave commands from[37].…”

L 1 Adaptive Fractional Control Optimized by Genetic Algorithms with Application to Polyarticulated Robotic Systems

“…Conventional adaptive controllers such as the Model Reference Adaptive Controller [1] (MRAC) are lacking robustness. In fact, among the drawbacks of these controllers is the choice of the adaptation gains.…”