Nonlinear H∞ control and the Hamilton-Jacobi-Isaacs equation

Ferreira, Henrique C.; Rocha, Paulo Henrique da; Sales, Roberto Moura

doi:10.3182/20080706-5-kr-1001.00032

Cited by 9 publications

(10 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…[35] for H-2 and Ref. [36] for nonlinear H-infinity), offers a tractable online solution, although not optimal, by a pertinent shaping of characteristic closed loop functions. Figure 18 (right) sketches the solution conceptualization, where the new output y has been added to the controlled outputs, as in mixed sensitivity approach.…”

Section: 32mentioning

confidence: 99%

“…This assumption leads to the application of a mixed sensitivity concept, shaped by dynamic weighting matrices W 1 and W 3 , as shown if Figure 18 (right). This originally linear tool, which has been extended to a particular nonlinear control system in [36] for example, could also be applied in the present case to the nonlinear model predictive controller.…”

Section: 32mentioning

confidence: 99%

See 1 more Smart Citation

Online artificial neural network model‐based nonlinear model predictive controller for the meridian UAS

Garcia

Keshmiri

2013

Intl J Robust & Nonlinear

View full text Add to dashboard Cite

A worldwide accident survey of manned and unmanned aircraft shows in-flight loss of control remains a major contributor to aircraft accidents. Operation outside the normal fight envelope is usually subject to failure of components, inappropriate crew response, and environmental conditions. The aerodynamic model of aircraft associated with hazardous weather and abnormal conditions is inherently nonlinear and unsteady. A novel adaptive nonlinear model predictive controller is proposed and conceptually proven to ensure safe control of the Meridian unmanned aerial system in off-nominal conditions. Nonlinear model predictive controllers are capable of handling input and output constraints that directly satisfy safety and performance requirements in off-nominal conditions. The performance of a nonlinear model predictive controller relies heavily on the physics-based model. Controller performance is improved by updating the physicsbased model by using real-time nonlinear estimation of aerodynamic forces. Real-time nonlinear estimation of aerodynamic forces is obtained utilizing artificial neural networks with time-varying adaptive parameters. The adaptive nonlinear model predictive controller, coupled with real-time parameter identification, is shown to exhibit robust characteristics and to successfully mitigate the impact of nonlinear and unsteady aerodynamics while preventing loss of control.

show abstract

Section: 32mentioning

confidence: 99%

Section: 32mentioning

confidence: 99%

Online artificial neural network model‐based nonlinear model predictive controller for the meridian UAS

Garcia

Keshmiri

2013

Intl J Robust & Nonlinear

View full text Add to dashboard Cite

show abstract

“…Therefore, the nominal performance is a target performance to achieve 3. The research presented in this paper is an extension of our preliminary versions of this work have been shown in[12] and[13].…”

mentioning

confidence: 92%

“…Fortunately, if the controller for a nominal plant is designed to be globally (uniformly) asymptotically stable, additional robustness of the whole system can be achieved by applying the NRIC framework. 3 In the framework, the actual performance is split into nominal performance and deviation from it (see Fig. 1).…”

Section: Introductionmentioning

confidence: 99%

Bringing Nonlinear <inline-formula><tex-math notation="LaTeX">$\mathcal {H}_\infty$</tex-math> </inline-formula> Optimality to Robot Controllers

2015

View full text Add to dashboard Cite

This paper proposes a framework called nonlinear robust internal-loop compensator that enables us to bring nonlinear H ∞ optimality to robot controllers in a unified and simple way. Using the framework, a controller designed for the nominal plant can achieve additional robustness by simply adding PID-type auxiliary input to the original control law. Robust performance is guaranteed by the nonlinear H ∞ optimality and robust stability is guaranteed by proving the extended disturbance input-to-state stability. Moreover, the framework preserves the passivity property of the original controller. Finally, the performance bound can be predicted and leads to the gain tuning rules. By virtue of the tuning rules, the performance can be tuned using only a single variable. The proposed method was validated through the simulations and experiments.1 Strictly speaking, L 2 -gain is less than γ. However, γ will be expressed as L 2 -gain throughout the paper for simplicity of writing. 1552-3098

show abstract

“…Os resultados obtidos nesta tese foram bem aceitos na comunidade científica nacional e internacional, conforme atestam os artigos [28], [37], [38], [39], [40], [41].…”

Section: Conclusãounclassified

Controle H-infinito não-linear aplicado em sistema de levitação magnética: projeto e implementação em DSP de ponto-fixo.

Rocha¹

View full text Add to dashboard Cite

Electromagnetic suspension systems are inherently nonlinear and often face hardware limitation when digitally controlled. The goal of this thesis is to present theoretical and practical aspects during the nonlinear H ∞ control applied on an electromagnetic suspension system. The first contribution is the design of a nonlinear H ∞ controller, including dynamic weighting functions, obtained from a linear H ∞ controller. Just as in the linear case, this dynamic weighting functions provide the disturbance and noise sensor rejection, robustness improvement, among other specifications. The second contribution is to present a procedure able to translate a floating-point algorithm into a fixed-point algorithm by using l ∞ norm minimization due to conversion error, which was then implemented into a 32-bit fixed-point DSP. Experimental results are also presented, in which the performance of the nonlinear controller is evaluated specifically in the initial suspension phase.

show abstract

Nonlinear H∞ control and the Hamilton-Jacobi-Isaacs equation

Cited by 9 publications

References 13 publications

Online artificial neural network model‐based nonlinear model predictive controller for the meridian UAS

Online artificial neural network model‐based nonlinear model predictive controller for the meridian UAS

Bringing Nonlinear <inline-formula><tex-math notation="LaTeX">$\mathcal {H}_\infty$</tex-math> </inline-formula> Optimality to Robot Controllers

Controle H-infinito não-linear aplicado em sistema de levitação magnética: projeto e implementação em DSP de ponto-fixo.

Contact Info

Product

Resources

About