Gain margins and phase margins for control systems with adjustable parameters

Han, Kuang-Wei; Chang, Chia‐Hao

doi:10.2514/3.25351

Cited by 81 publications

(33 citation statements)

References 3 publications

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“…Furthermore, the locus in the a vs. b plane is a boundary of the constant gain margin if K is assumed to be equal to another constant and y ¼ 0 : However, the locus is a boundary of the constant phase margin if K ¼ 0 dB and y is assumed to be equal to another constant. Additionally, when the third parameter g is considered, the three aforementioned boundaries can also be found in parameter space for each specific value of g [10].…”

Section: Parameter Plane Analysismentioning

confidence: 99%

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Limit cycle analysis of nonlinear sampled-data systems by gain–phase margin approach

Wu¹,

Perng²,

Chin³

2005

Journal of the Franklin Institute

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Section: Parameter Plane Analysismentioning

confidence: 99%

“…Methods of analyzing the gain-phase margin in a linear continuous-data system with adjustable parameters have recently been developed [10]. This approach has been extended to analyze a nuclear reactor system with various transport lags [11] and a proportional navigation sampled-data control system [12].…”

Section: Introductionmentioning

confidence: 99%

Limit cycle analysis of nonlinear sampled-data systems by gain–phase margin approach

Wu¹,

Perng²,

Chin³

2005

Journal of the Franklin Institute

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“…2, the method proposed in Ref. 17 can be employed to determine a combination of the parameters K a , W I , and K r , such that the required gain margin (GM) and phase margin (PM) can be satis ed. For the requirementthat the GM and PM in the four ight conditionsshown in table 1 are GM D 10 dB and PM D 60 deg, the combinationsof the gains K a , W I , and K r for the four ight conditions are listed in Table 1.…”

Section: Missile Dynamics and Conventional Control Designsmentioning

confidence: 99%

Improving Conventional Longitudinal Missile Autopilot Using Cerebellar Model Articulation Controller Neural Networks

Lin¹,

Chen

2003

Journal of Guidance, Control, and Dynamics

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A way to integrate the cerebellar model articulation controller (CMAC) neural network with the missile longitudinal conventional feedback controller (CFC) to compensate for nonlinearities, unmodeled dynamics, parameter variations, etc., is proposed. The inner loop of the CFC will essentially be left unchanged to improve the stability of the missile. The outer loop of CFC, in addition to playing its traditional role, would work with the CMAC to learn quickly to approximate the dynamic inversion from angle of attack to control de ection, to achieve better tracking in normal acceleration. In this arrangement, the well-known CFC acts as a safety net, whereas additional performance is brought about through CMAC learning. Nomenclatureaerodynamic coef cients d = reference diameter, cm I y = pitch moment of inertia, kg ¢ m 2 M m = Mach number Q = dynamic pressure, kg/(m.sec 2 / q = pitch rate, deg/s S = reference area, m 2 U = U C C U O U C = output of cerebellar model articulation controller neural network, deg U I = command generated by the inner loop of conventional feedback controller, deg U O = command generated by the outer loop of conventional feedback controller, deg V m = missile speed, m/s W = missile weight, kg ® = angle of attack, deg ± = tail-n de ection, deg µ = pitch angle, deg

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“…This methodology originated from the fact that controller and process can be matched and reduced to that of pure time delay and integral action (Rad and Lo 1995). In Chang and Han (1990) as well as Tan and Atherton (2006), an analytical GPM tester was added to find the boundaries of constant GPMs, and the complete information about the effects of adjustable parameters was obtained. However, it is hard to plot the feasible boundary consisting of variable margin pairs for a fixed-order controller when the frequency sweep approach is employed.…”

Section: Introductionmentioning

confidence: 99%

Stability margin-based PD attitude control tuning for unstable flight vehicle

Sun¹,

Yang²,

Wang³

et al. 2013

International Journal of Systems Science

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Proportional-derivative (PD) attitude control is widely used for the flight vehicles, especially in boost phase. Some of the flight dynamics are open-loop unstable, which often limits the achievable closed-loop performance. Based on the intrinsic characteristics of the linear model obtained from the small perturbation theory, simple numerical analytical tuning formulae of PD attitude control are derived to meet the gain and phase margin specifications. According to Routh stability criterion, the decreasing gain margin is obtained by using the approximation of delay amid low frequency with the established tuning rule. Some numerical polynomial solving approaches are employed to seek the feasible stability margin region, which is explicitly plotted in the 2-D plane. Taking engineering practice into account, the maximum gain constraint is also imposed. Finally, several numerical examples are presented to validate the analysis result.

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Gain margins and phase margins for control systems with adjustable parameters

Cited by 81 publications

References 3 publications

Limit cycle analysis of nonlinear sampled-data systems by gain–phase margin approach

Limit cycle analysis of nonlinear sampled-data systems by gain–phase margin approach

Improving Conventional Longitudinal Missile Autopilot Using Cerebellar Model Articulation Controller Neural Networks

Stability margin-based PD attitude control tuning for unstable flight vehicle

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