Digital H<sub>∞</sub> Robust Control of Mechanical System with Implicit Observer

Angélico, Bruno Augusto; Brugnolli, Mateus Mussi; Neves, Gabriel Pereira das

doi:10.1109/cdc40024.2019.9029321

Cited by 3 publications

(6 citation statements)

References 7 publications

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“…The implicit derivative estimator state-space is a nonminimal representation which all states are measurements of the outputs of the system (Angélico et al, 2019). The requirement is that original discrete state-space must have a set of positions and their respective velocities as the state vector, a common feature of mechanic systems.…”

Section: Implicit Derivative Estimator State-spacementioning

confidence: 99%

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A Robust Digital Control of a Gyroscope with an Implicit Derivative Estimator

Brugnolli¹,

Neves²,

Carvalho³

et al. 2020

Anais Do Congresso Brasileiro De Automática 2020

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Recently, a new state-space representation has been developed for output feedback control design. With this transformation, many mechanical systems can be represented with the system output measurements as the full state vector. Therefore, these models allow the design of output feedback controllers using state feedback gains. For this work, a set of uncertain models for a Control Moment Gyroscope is assembled and a polytope discretization is performed. The resulting set of models is transformed into the Implicit Derivative Estimator and integrators are added for the output tracking. Finally, a robust H2 digital control is designed, considering the set of uncertain models. The controller is validated through simulation and practical experiments.

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Section: Implicit Derivative Estimator State-spacementioning

confidence: 99%

“…Recently, Angélico et al (2019) timator. This method has properties of the three methods previously mentioned.…”

Section: Introductionmentioning

confidence: 99%

A Robust Digital Control of a Gyroscope with an Implicit Derivative Estimator

Brugnolli¹,

Neves²,

Carvalho³

et al. 2020

Anais Do Congresso Brasileiro De Automática 2020

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“…1 [26], [27]. The pendulum parameters are the rotating arm mass m 0 F , the pendulum mass m 1 F , the rotating arm length 2l 0 F , the pendulum length 2l 1 F , the distance from the fixed axis to the pendulum basis r F and the center of mass (CoM) of the pendulum arm d F w.r.t fixed axis [26]. The system dynamics are defined using the Euler-Lagrange formulation:…”

Section: A System Modelingmentioning

confidence: 99%

“…The potential energy P F can be described using the displacement of the CoM of the pendulum [26], such as…”

Section: A System Modelingmentioning

confidence: 99%

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Sliding Mode Control Barrier Function

Chinelato¹,

Angélico²

2020

Preprint

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This work proposes a sliding mode control barrier function to robustly deal with high relative-degree safety constraints in safety-critical control systems. Stability/tracking objectives, expressed as a nominal control law, and safety constraints, expressed as control barrier functions are unified through quadratic programming. The proposed control framework is numerically validated considering a Furuta pendulum and a magnetic levitation system. For the first system, a linear quadratic regulator is considered as a nominal control law, and a safety constraint is considered to guarantee that the pendulum angular position never exceeds a predetermined value. For the second one, a sliding mode controller is considered as a nominal control law and multiple safety constraints are considered to guarantee that the magnetic levitation system positions never exceed predetermined values. For both systems, we consider high relative-degree safety constraints robust against model uncertainties. The numerical results indicate that the stability/tracking objectives are reached and the safety constraints are respected even with model uncertainties.

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