a new fault tolerant controller (ftc) has been presented in this research by integrating a fault detection and diagnosis (fdd) mechanism in a nonlinear model predictive controller framework. the proposed fdd utilizes a multi-sensor data fusion (msdf) methodology to enhance its reliability and estimation accuracy. an augmented state-vector model is developed to incorporate the occurred sensor fa...

A new Fault Tolerant Controller (FTC) has been presented in this research by integrating a Fault Detection and Diagnosis (FDD) mechanism in a nonlinear model predictive controller framework. The proposed FDD utilizes a Multi-Sensor Data Fusion (MSDF) methodology to enhance its reliability and estimation accuracy. An augmented state-vector model is developed to incorporate the occurred senso...

A nonlinear predictive controller (NMPC) is developed to control a unicycle-like mobile robot for trajectory tracking. Dynamic model of PIONNER 3-DX mobile robot is used, where external forces and wheels sliding have been considered. Restrictions on control actions and system states are also considered. Simulations results at both tracking and regulation (positioning) are shown, these results s...

This paper presents a new dual mode nonlinear model predictive controller (NMPC) that is based on the combination of the finite horizon NMPC with the infinite horizon predictive controller (IHMPC). The resulting nonlinear controller is shown to be stable when the IHMPC is globally stabilizing. The main advantage of the proposed controller in comparison to the IHMPC is a better performance as th...

A plant-wide control strategy based on integrating linear model predictive control (LMPC) and nonlinear model predictive control (NMPC) is proposed. The hybrid method is applicable to plants that can be decomposed into approximately linear subsystems and highly nonlinear subsystems that interact via mass and energy ̄ows. LMPC is applied to the linear subsystems and NMPC is applied to the nonlin...

an adaptive version of growing and pruning rbf neural network has been used to predict the system output and implement linear model-based predictive controller (lmpc) and non-linear model-based predictive controller (nmpc) strategies. a radial-basis neural network with growing and pruning capabilities is introduced to carry out on-line model identification.an unscented kalman filter (ukf) algor...

Nonlinear Model Predictive Control (NMPC), generally based on nonlinear state space models, needs knowledge of the full state for feedback. However, in practice knowledge of the full state is usually not available. Therefore, an asymptotically stabilizing MPC scheme for a class of nonlinear discrete-time systems is proposed, which only requires knowledge of the output of the system for feedback...

Over the recent years many advances in the area of nonlinear model predictive control have been made. However, with respect to the output feedback problem and predictive control only a limited number of results are available. Most of the existing approaches do only guarantee local stability. In this note we propose the use of a high gain observer in combination with a sampled nonlinear predicti...

Widespread application of dynamic optimization with fast optimization solvers leads to increased consideration of first-principles models for nonlinear model predictive control (NMPC). However, significant barriers to this optimization-based control strategy are feedback delays and consequent loss of performance and stability due to on-line computation. To overcome these barriers, recently prop...

An analytical solution to the nonlinear model predictive control (NMPC) optimization problem is derived for single–input single–output (SISO) systems modeled by second–order Volterra–Laguerre models. All input moves except the current move (m > 1 in the NMPC framework) are approximated by solving an unconstrained linear MPC problem which utilizes a locally accurate linear model of the process. ...