Abstract In this paper, a real-time predictive filter is derived for nonlinear systems. The major advantage of this new filter over conventional filters is that it provides a method of determining optimal state estimates in the presence of significant error in the assumed (nominal) model. The new real-time nonlinear filter determines (“predicts”) the optimal model error trajectory so that the m...

Most of the nonlinear ultrasonic studies to date have been experimental, but few theoretical predictive studies exist, especially for Lamb wave ultrasonic. Compared with nonlinear bulk waves and Rayleigh waves, nonlinear Lamb waves for structural health monitoring become more challenging due to their multi-mode dispersive features. In this paper, predictive study of nonlinear Lamb waves is done...

Nonlinear Predictive Control of Spacecraft John L. Crassidis F. Landis Markley Tobin C. Anthony Stephen F. Andrews Abstract A new approach for the control of a spacecraft with large angle maneuvers is presented. This new approach is based on a nonlinear predictive control scheme which determines the required torque input so that the predicted responses match the desired trajectories. This is ac...

Concept of model based predictive control (MBPC) has been heralded as one of the most significant control developments in recent ten years. Wide range of choice of model structures, prediction horizon, and optimization criteria allows the designer to easily tailor MBPC to its application in industry. The main idea of Model Predictive Control (MPC) is the prediction of the output signal at each ...

Non linear predictive control may require signiicant oo-line and on-line computations. This paper gives a nonlinear predictive control algorithm based on successive oo-equilibrium linearisation which requires less computation whilst accounting for nonlinear system behaviour. A nonlinear example (the pendulum and cart system) is used to show that the method provides a practical tradeoo between c...

In this paper statistical and time series models are used for determining the random drift of a dynamically Tuned Gyroscope (DTG). This drift is compensated with optimal predictive transfer function. Also nonlinear neural-network and fuzzy-neural models are investigated for prediction and compensation of the random drift. Finally the different models are compared together and their advantages a...

This article deals with the issues associated with developing a new design methodology for the nonlinear model-predictive control (MPC) of a chemical plant. A combination of multiple neural networks is selected and used to model a nonlinear multi-input multi-output (MIMO) process with time delays.  An optimization procedure for a neural MPC algorithm based on this model is then developed. T...

Laguerre function has many advantages such as good approximation capability for different systems, low computational complexity and the facility of on-line parameter identification. Therefore, it is widely adopted for complex industrial process control. In this work, Laguerre function based adaptive model predictive control algorithm (AMPC) was implemented to control continuous stirred tank rea...

A new nonlinear predictive control law for a class of multivariable nonlinear systems is presented in this paper. It is shown that the closed-loop dynamics under this nonlinear predictive controller explicitly depend on design parameters (prediction time and control order). The main features of this result are that an explicitly analytical form of the optimal predictive controller is given, on-...