Add to ORKGThe paper presents an over-parametrization free certainty equivalence state feedback backstepping adaptive control design method for systems of any relative degree with unmatched uncertainties and unknown virtual control coefficients. It uses a fast prediction model to estimate the unknown parameters, which is independent of the control design. It is shown that the system's input and output tracking errors can be systematically decreased by the proper choice of the design parameters. The benefits of the approach are demonstrated in numerical simulations
In this paper, the adaptive H∞ control problem based on the neural network technique is studied for ...
When the nonlinear mechanical system has mismatched uncertainties, it is difficult to design control...
We construct bounded globally asymptotically stabilizing output feedbacks for a family of nonlinear ...
The paper presents a certainty equivalence output feedback backstepping adaptive control design meth...
This paper deals with the tracking control problem for a class of unknown pure feedback system with ...
Although many of today’s nonlinear control design algorithms assume the system dynamics to be affine...
Although many of todays nonlinear control design algorithms assume the system dynamics to be affine ...
The paper highlights the main steps of adaptive output feedback control for non-affine uncertain sys...
Most existing adaptive control designs for nonlinear pure-feedback systems have been derived based o...
Grant 62022031 Grant 61773135 Grant U20A20188Backstepping method is a successful approach to deal wi...
This paper presents a normalization based modified reference model adaptive control method for multi...
AbstractA novel scheme is proposed for the design of backstepping control for a class of state-feedb...
The design of closed-loop adaptive control systems based on nonparametric identification was address...
The paper presents a modified model reference adaptive control (M-MRAC) for multi-input multi-output...
Abstract. The paper proposes a unitary approach of adaptive output feedback control for non-affine u...
In this paper, the adaptive H∞ control problem based on the neural network technique is studied for ...
When the nonlinear mechanical system has mismatched uncertainties, it is difficult to design control...
We construct bounded globally asymptotically stabilizing output feedbacks for a family of nonlinear ...
The paper presents a certainty equivalence output feedback backstepping adaptive control design meth...
This paper deals with the tracking control problem for a class of unknown pure feedback system with ...
Although many of today’s nonlinear control design algorithms assume the system dynamics to be affine...
Although many of todays nonlinear control design algorithms assume the system dynamics to be affine ...
The paper highlights the main steps of adaptive output feedback control for non-affine uncertain sys...
Most existing adaptive control designs for nonlinear pure-feedback systems have been derived based o...
Grant 62022031 Grant 61773135 Grant U20A20188Backstepping method is a successful approach to deal wi...
This paper presents a normalization based modified reference model adaptive control method for multi...
AbstractA novel scheme is proposed for the design of backstepping control for a class of state-feedb...
The design of closed-loop adaptive control systems based on nonparametric identification was address...
The paper presents a modified model reference adaptive control (M-MRAC) for multi-input multi-output...
Abstract. The paper proposes a unitary approach of adaptive output feedback control for non-affine u...
In this paper, the adaptive H∞ control problem based on the neural network technique is studied for ...
When the nonlinear mechanical system has mismatched uncertainties, it is difficult to design control...
We construct bounded globally asymptotically stabilizing output feedbacks for a family of nonlinear ...