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In order to attain an improved controller a model predictive control (MPC) approach is designed.
The proposed control system is consisted of a robust controller, a neural controller, a model neural network of vehicle's suspension system.
In order to build and tune a controller a model is needed, which has to be able to reproduce the nonlinear behaviour.
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Global tracking convergence is illustrated with the use of a feedback linearizing PD tracker, a sliding mode controller, and a model reference adaptive controller.
The controller uses a model predictive control framework that chooses a needle twist rate such that the predicted helical trajectory minimizes the distance to the target.
The RBF neural network is used in a novel approach as a controller in a model reference adaptive control (MRAC) architecture.
To facilitate the controller design, a model transformation method is proposed to transform the LTV system into a linear time-invariant (LTI) system with norm-bounded uncertainties.
To facilitate digital implementations of the robot controller, a robot model in discrete time has been employed.
In basic systems and control theory, an effective controller must contain a model of the system it is controlling, i.e. the Process Model.
In the paper, the researchers quantified this asymmetric wing motion using a controller model, called a proportional integral controller, that's mathematically similar to those used in cruise control systems.
For DOB-based controllers, a nominal model is needed; however, obtaining the nominal model is difficult for systems with friction.
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