Majji, Manoranjan (2003-05). Robust control of redundantly actuated dynamical systems. Master's Thesis. Thesis uri icon

abstract

  • The eigenstructure assignment scheme for robust multivariable feedback control is extended to redundantly actuated dynamical systems. It is shown that an orthonormal set of close loop eigenvectors is always exactly assignable in the case of redundant actuation proving the inherent robustness in the control design methodology. A choice of close loop eigenvector set to minimize the feedback gain matrix is suggested. Partial Eigenstructure Assignment methodology is proposed for second order mechanical systems. A methodology for coordinated actuation of redundant actuator sets by a trained weighted minimum norm solution is presented. To apply the methodology to hyper-redundant actuator arrays, for application to smart actuator arrays, a novel adaptive discretization algorithm is proposed. The adaptive aggregation strategy, based on the physics of the system, introduces nodes, to optimize a performance index of the overall plant model. The dimensionality of the inputs thus reduces to a finite number, making it a candidate plant for control by the robust redundant control scheme. The adaptive aggregation together with robust redundant control methodology is demonstrated on a finite element model of a novel morphing wing. This schema unifies the traditionally disparate methods of modeling and controller design.
  • The eigenstructure assignment scheme for robust multivariable feedback control
    is extended to redundantly actuated dynamical systems. It is shown that an orthonormal
    set of close loop eigenvectors is always exactly assignable in the case of
    redundant actuation proving the inherent robustness in the control design methodology.
    A choice of close loop eigenvector set to minimize the feedback gain matrix
    is suggested. Partial Eigenstructure Assignment methodology is proposed for second
    order mechanical systems. A methodology for coordinated actuation of redundant
    actuator sets by a trained weighted minimum norm solution is presented. To apply
    the methodology to hyper-redundant actuator arrays, for application to smart actuator
    arrays, a novel adaptive discretization algorithm is proposed. The adaptive
    aggregation strategy, based on the physics of the system, introduces nodes, to optimize
    a performance index of the overall plant model. The dimensionality of the
    inputs thus reduces to a finite number, making it a candidate plant for control by
    the robust redundant control scheme. The adaptive aggregation together with robust
    redundant control methodology is demonstrated on a finite element model of a novel
    morphing wing. This schema unifies the traditionally disparate methods of modeling
    and controller design.

publication date

  • May 2003