Asymptotic Tracking for Uncertain Dynamic Systems Via a Multilayer Neural Network Feedforward and RISE Feedback Control Structure

Patre, Parag; MacKunis, William; Kaiser, K.; Dixon, Warren E.

doi:10.1109/tac.2008.930200

Cited by 222 publications

(152 citation statements)

References 17 publications

Supporting

Mentioning

151

Contrasting

Order By: Relevance

“…This implicit learning law is the key element that allows the controller to obtain an exponential stability result despite the additive nonvanishing exogenous disturbance. Other results in literature also have used the implicit learning structure [26][27][28][29].…”

Section: Closed-loop Error Systemmentioning

confidence: 99%

Lyapunov-Based Exponential Tracking Control of a Hypersonic Aircraft with Aerothermoelastic Effects

Wilcox

MacKunis

Bhat

et al. 2010

Journal of Guidance, Control, and Dynamics

137

View full text Add to dashboard Cite

Hypersonic flight conditions produce temperature variations that can alter both the structural dynamics and flight dynamics. These aerothermoelastic effects are modeled by a nonlinear, temperature-dependent, parameter-varying state-space representation. The model includes an uncertain parameter-varying state matrix, an uncertain parameter-varying nonsquare (column-deficient) input matrix, and a nonlinear additive bounded disturbance. A Lyapunov-based continuous robust controller is developed that yields exponential tracking of a reference model, despite the presence of bounded nonvanishing disturbances. Simulation results for a hypersonic aircraft are provided to demonstrate the robustness and efficacy of the proposed controller.

show abstract

Section: Closed-loop Error Systemmentioning

confidence: 99%

Lyapunov-Based Exponential Tracking Control of a Hypersonic Aircraft with Aerothermoelastic Effects

Wilcox

MacKunis

Bhat

et al. 2010

Journal of Guidance, Control, and Dynamics

137

View full text Add to dashboard Cite

show abstract

“…The estimates for NN weights, and can be bounded using the projection algorithm as in [27] and [28].…”

Section: ) Propertymentioning

confidence: 99%

“…Since the openloop error expression for the mass in (13) does not have an actual control input, a virtual control input, is introduced by adding and subtracting to (13) as (16) To facilitate the subsequent backstepping-based design, the virtual control input to the unactuated mass-spring-damper system is designed as (17) In (17), is a constant positive control gain, and and are the estimates of the ideal weights, which are updated based on the subsequent stability analysis as (18) where , are constant, positive definite, symmetric gain matrices, and denotes a projection algorithm 1 utilized to guarantee that the weight estimates and remain bounded [27], [28]. The estimates for the NN weights in (18) are generated online (there is no offline learning phase).…”

Section: Closed-loop Error Systemmentioning

confidence: 99%

“…The closed-loop error system for the mass can be developed by substituting (17) into (16) as (19) Adding and subtracting to (19), and then using the Taylor series approximation in (12), the following expression for the closed-loop mass error system can be obtained (20) where the notation and is introduced in (12), and is defined as 1 The initial weights are selected in some compact set, then new estimates are generated based on the weight update laws in (18). If the weights on the boundary of the set are found to be directed outside the compact set, they are projected back into the set using a smooth projection algorithm (see [27], [28] for details). …”

Section: Closed-loop Error Systemmentioning

confidence: 99%

See 1 more Smart Citation

Neural Network Control of a Robot Interacting With an Uncertain Hunt-Crossley Viscoelastic Environment

Bhasin

Dupree

Patre

et al. 2008

ASME 2008 Dynamic Systems and Control Conference, Parts a and B

View full text Add to dashboard Cite

Abstract-A continuous controller is developed for a robot that moves in free space, undergoes a collision with a viscoelastic environment, and then regulates the new coupled dynamic system to a desired setpoint. Since the model for the viscoelastic surface contains uncertainties that do not satisfy the linear-in-the-parameters assumption, the model is approximated by a neural network feedforward term, which is combined with a continuous feedback term to guarantee uniformly ultimately bounded regulation of the system despite parametric uncertainties in the robot and the viscoelastic environment. Experimental results of a two-link robot interacting with a human tissue phantom demonstrate the performance of the proposed controller.

show abstract

“…The unknown disturbance represents unsteady nonlinear aerodynamic effects. A NN is used as a feedforward control term to compensate for the unknown nonlinear disturbance and a RISE feedback term [16][17][18] ensures asymptotic tracking of a desired state trajectory. A RISE-like controller was also recently developed in Ref.…”

Section: Introductionmentioning

confidence: 99%

Tracking Control of Limit Cycle Oscillations in an Aero-Elastic System

Bialy

Pasiliao

Dinh

et al. 2014

Journal of Dynamic Systems, Measurement, and Control

View full text Add to dashboard Cite

Limit cycle oscillations (LCOs) affect current fighter aircraft and are expected to be present on next generation fighter aircraft. Current efforts in control systems designed to suppress LCO behavior have either used a linear model, restricting the flight regime, require exact knowledge of the system dynamics, or require uncertainties in the system dynamics to be linear-in-theparameters and only present in the torsional stiffness. Furthermore, the aerodynamic model used in prior research efforts neglects nonlinear effects. This paper presents the development of a controller consisting of a continuous robust integral of the sign of the error (RISE) feedback term with a neural network (NN) feedforward term to achieve asymptotic tracking of uncertainties that do not satisfy the linear-in-the-parameters assumption. Simulation results are presented to validate the performance of the developed controller.

show abstract

Asymptotic Tracking for Uncertain Dynamic Systems Via a Multilayer Neural Network Feedforward and RISE Feedback Control Structure

Cited by 222 publications

References 17 publications

Lyapunov-Based Exponential Tracking Control of a Hypersonic Aircraft with Aerothermoelastic Effects

Lyapunov-Based Exponential Tracking Control of a Hypersonic Aircraft with Aerothermoelastic Effects

Neural Network Control of a Robot Interacting With an Uncertain Hunt-Crossley Viscoelastic Environment

Tracking Control of Limit Cycle Oscillations in an Aero-Elastic System

Contact Info

Product

Resources

About