Adaptive Control and the NASA X-15-3 Flight Revisited

doi:10.1109/mcs.2010.936292

Cited by 132 publications

(20 citation statements)

References 36 publications

(42 reference statements)

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“…Thus, the output feedback stabilization problem given by Eqs. (4) and 5is converted into a full-state feedback control design problem by equivalently considering Eqs. (14) and (15).…”

Section: Nonminimal State-space Realization Formulationmentioning

confidence: 99%

“…Assumption 2: The system given by Eqs. (4) and 5is square (i.e., m l), and B is nonsingular. It is important to note that Assumption 2 implies the existence of K q ∈ R ln×m and K…”

Section: Adaptive Control For the Nonminimal State-space Modelmentioning

confidence: 99%

“…This is especially important in high-performance safety-critical aircraft systems, since system faults, structural damage, or sudden changes in the control surface effectiveness can result in large changes in the system parameters. However, high learning rates can result in adaptive control signals with high-frequency content and, for practical application, can result in violation of actuator rate saturation constraints [3], excite unmodeled dynamics [4,5], and cause system instability [6]. With the notable exceptions of [7][8][9], the design tradeoff between system stability and adaptation learning rate (i.e., adaptation gain) has not been considered when designing adaptive controllers.…”

Section: Introductionmentioning

confidence: 99%

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Output Feedback Adaptive Control with Low-Frequency Learning and Fast Adaptation

Rajpurohit

Haddad

Yucelen

2016

Journal of Guidance, Control, and Dynamics

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Although adaptive control has been used in numerous applications to achieve system performance without excessive reliance on system models, the necessity of high-gain learning rates for achieving fast adaptation can be a serious limitation of adaptive controllers. Specifically, in safety-critical systems involving large system uncertainties and abrupt changes in system dynamics, fast adaptation is required to achieve stringent tracking performance specifications. However, fast adaptation using high-gain learning rates can cause high-frequency oscillations in the control response, resulting in system instability. This paper develops an output feedback adaptive control framework for continuous-time minimum-phase multivariable dynamical systems for output stabilization and command following to address the problem of achieving fast adaptation using high-gain learning rates for systems with partial state information. The proposed framework uses a controller architecture involving a modification term in the update law that filters out the high-frequency content in the control response while preserving uniform boundedness of the system error dynamics. The approach is based on a nonminimal state-space realization that generates an expanded set of states using the filtered inputs and filtered outputs, as well as their derivatives, of the original system, and requires knowledge of only the open-loop system's relative degree and a bound on the system's order.

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“…Thus, the output feedback stabilization problem given by Eqs. (4) and 5is converted into a full-state feedback control design problem by equivalently considering Eqs. (14) and (15).…”

Section: Nonminimal State-space Realization Formulationmentioning

confidence: 99%

“…Assumption 2: The system given by Eqs. (4) and 5is square (i.e., m l), and B is nonsingular. It is important to note that Assumption 2 implies the existence of K q ∈ R ln×m and K…”

Section: Adaptive Control For the Nonminimal State-space Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Output Feedback Adaptive Control with Low-Frequency Learning and Fast Adaptation

Rajpurohit

Haddad

Yucelen

2016

Journal of Guidance, Control, and Dynamics

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“…For conventional aircraft operating under emergency flight conditions, as well as for unconventional aircraft, recent research has focused on adaptive control techniques [8][9][10][11][12][13]. The failure of the Honeywell MH-96 self-adaptive controller used on the X-15-3 was analyzed in [12], and L 1 adaptive control with an uncertain flight envelope has been flight tested on the NASA AirStar scaled aircraft [13].…”

Section: Introductionmentioning

confidence: 99%

“…With the advent of optimal control methods, state-space-based control techniques have also been successful [4,5]. Although classical optimal control does not account for model uncertainty, aircraft flight control has benefited from advances in robust control [6,7].For conventional aircraft operating under emergency flight conditions, as well as for unconventional aircraft, recent research has focused on adaptive control techniques [8][9][10][11][12][13]. The failure of the Honeywell MH-96 self-adaptive controller used on the X-15-3 was analyzed in [12], and L 1 adaptive control with an uncertain flight envelope has been flight tested on the NASA AirStar scaled aircraft [13].The goal of the present paper is to investigate the performance of an alternative technique for adaptive flight control, specifically, retrospective cost adaptive control (RCAC).…”

mentioning

confidence: 99%

Retrospective Cost Adaptive Control of Generic Transport Model Under Uncertainty and Failure

Ansari

Bernstein

2017

Journal of Aerospace Information Systems

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η = ground velocity alignment error with a ⇀ Θ, Φ, Ψ = pitch, roll, yaw angles θ = controller coefficients τ = turn rate ϕ = feedback vector I. IntroductionA LONG with aerodynamics, lightweight structures, and propulsion, feedback control is one of the key technologies that enable aviation.Feedback control uses inertial and noninertial sensors to provide measurements of position, velocity, acceleration, attitude, and angular rates to specify thrust and aerodynamic surfaces to apply corrective forces and moments. Closed-loop control enables the operation of autopilots, which can either assist the pilot or assume complete control of aircraft operation.The standard approach to feedback control of aircraft is based on classical control techniques [1][2][3]. With the advent of optimal control methods, state-space-based control techniques have also been successful [4,5]. Although classical optimal control does not account for model uncertainty, aircraft flight control has benefited from advances in robust control [6,7].For conventional aircraft operating under emergency flight conditions, as well as for unconventional aircraft, recent research has focused on adaptive control techniques [8][9][10][11][12][13]. The failure of the Honeywell MH-96 self-adaptive controller used on the X-15-3 was analyzed in [12], and L 1 adaptive control with an uncertain flight envelope has been flight tested on the NASA AirStar scaled aircraft [13].The goal of the present paper is to investigate the performance of an alternative technique for adaptive flight control, specifically, retrospective cost adaptive control (RCAC). RCAC is a direct discrete-time adaptive control technique for stabilization, command following, and disturbance rejection [14]. As a discrete-time approach, RCAC is motivated by the desire to implement control algorithms that operate at the sensor sample rate without the need for controller discretization. This also means that the required modeling information can be estimated based on data sampled at the same rate as the control update.RCAC was originally motivated by the notion of retrospectively optimized control, where past controller coefficients used to generate past control inputs are reoptimized in the sense that if the reoptimized coefficients had been used over a previous window of operation, then the performance would have been better. Unlike signal processing applications such as estimation and identification, however, it is impossible to change past control inputs, and thus the reoptimized controller coefficients are used only to generate the next control input.RCAC was originally developed within the context of active noise control experiments [15]. The algorithm used in [15] is gradient based, where the gradient direction and step size are based on different cost functions. In subsequent work [16], the gradient algorithm was replaced by batch least-squares optimization. In both [15,16], the modeling information is given by Markov parameters (impulse response components) of the openloop transfer function ...

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Model Reference Adaptive Control

Yucelen

2019

Wiley Encyclopedia of Electrical and Electronics Engineering

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Adaptive control methods present effective system‐theoretical tools in order to achieve closed‐loop system stability and performance in the presence of exogenous disturbances and system uncertainties, where they are generally classified as either direct or indirect. A well‐known class of direct adaptive control methods is model reference adaptive control architectures. In particular, these architectures employ two major components – a reference model and a parameter adjustment mechanism. A desired closed‐loop dynamical system response is captured by the reference model for which its response is compared with the response of the uncertain dynamical system. The system error signal resulting from this comparison drives the parameter adjustment mechanism. This mechanism then adjusts the controller parameters in a real‐time (i.e., online) manner for driving the trajectories of the uncertain dynamical system to the trajectories of the reference model. This article discusses these components in a basic state feedback setting in order to provide an introduction to the model reference adaptive control design procedure. The article also makes connections to several other, relatively advanced model reference adaptive control methods for interested readers.

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Adaptive Control and the NASA X-15-3 Flight Revisited

Cited by 132 publications

References 36 publications

Output Feedback Adaptive Control with Low-Frequency Learning and Fast Adaptation

Output Feedback Adaptive Control with Low-Frequency Learning and Fast Adaptation

Retrospective Cost Adaptive Control of Generic Transport Model Under Uncertainty and Failure

Model Reference Adaptive Control

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