Robust Control of Nonlinear Systems Using Model-Error Control Synthesis

Robust Control of Nonlinear Systems Using Model-Error Control Synthesis

A new approach for the robust control of nonlinear systems is presented. This approach employs an optimal real-time nonlinear estimator to determine model error corrections to the control input using a one time-step ahead technique. Control compensation is achieved by using the estimated model error as a signal synthesis adaptive correction to the nominal control input so that maximum performan...

Robust Aircraft Longitudinal Control Using Model-Error Control Synthesis

In this paper a robust control for aircraft longitudinal motion is presented. The ModelError Control Synthesis (MECS) is developed for this application, which consists of a nominal controller with a model-error predictive filter. The control input is updated directly using the estimated model-error from a predictive filter to cancel the unmodeled dynamics or disturbance inputs. The predictive f...

Robust Model Predictive Control for a Class of Discrete Nonlinear systems

This paper presents a robust model predictive control scheme for a class of discrete-time nonlinear systems subject to state and input constraints. Each subsystem is composed of a nominal LTI part and an additive uncertain non-linear time-varying function which satisfies a quadratic constraint. Using the dual-mode MPC stability theory, a sufficient condition is constructed for synthesizing the ...

robust control of a high-purity disttilation column using synthesis

Dynamic Sliding Mode Control of Nonlinear Systems Using Neural Networks

Dynamic sliding mode control (DSMC) of nonlinear systems using neural networks is proposed. In DSMC the chattering is removed due to the integrator which is placed before the input control signal of the plant. However, in DSMC the augmented system is one dimension bigger than the actual system i.e. the states number of augmented system is more than the actual system and then to control of such ...