Optimal Sliding Mode Cascade Control for Stabilization of Underactuated Nonlinear Systems

This paper presents an optimal sliding mode cascade control for stabilization of a class of underactuated nonlinear mechanical systems. A discrete-time, nonlinear model predictive control structure is used to optimally select and update the parameters of the sliding mode control surfaces at specified intervals in order to achieve a desired performance objective. The determination of these surface parameters is subject to constraints that arise from the stability conditions imposed by the sliding mode control law and the physical limits on the system such as input saturation. Nominal stability of the optimal cascade control structure is demonstrated and its robust performance is illustrated using an experimental rotary inverted pendulum system

Parameter Identification and On-Line Estimation of a Reduced Kinetic Model

In this work, we present the estimation techniques used to update the model parameters in a reduced kinetic model describing the oxidation-reduction re- actions in a hydrothermal oxidation reactor. The model is used in a nonlinear model-based controller that minimizes the total aqueous nitrogen in the reac- tor effluent. Model reduction is accomplished by com- bining similar reacting compounds into one of four component groups and considering the global reac- tion pathways for each of these groups. The reduced kinetic model developed for thk reaction system pro- vides a means to characterize the complex chemical reaction system without considering each chemicaJ species present and the reaction kinetics of every pos- sible reaction pathway. For the reaction system under study, model reduction is essential in order to reduce the computational requirement so that on-line imple- mentation of the nonlinear model-based controller is possible and also to reduce the amount of a priori information required for the model

Recursive Estimation for the Tracking of Radioactive Sources

This paper describes a recursive estimation algorithm used for tracking the physical location of radioactive sources in real-time as they are moved around in a facility. The al- gorithm is a nonlinear least squares estimation that mini- mizes the change in, the source location and the deviation between measurements and model predictions simultane- ously. The measurements used to estimate position consist of four count rates reported by four different gamma ray de tectors. There is an uncertainty in the source location due to the variance of the detected count rate. This work repre- sents part of a suite of tools which will partially automate security and safety assessments, allow some assessments to be done remotely, and provide additional sensor modalities with which to make assessments

Recursive estimation for the tracking of radioactive sources

This paper describes a recursive estimation algorithm used for tracking the physical location of radioactive sources in real-time as they are moved around in a facility. The algorithm is related to a nonlinear least squares estimation that minimizes the change in the source location and the deviation between measurements and model predictions simultaneously. The measurements used to estimate position consist of four count rates reported by four different gamma ray detectors. There is an uncertainty in the source location due to the large variance of the detected count rate. This work represents part of a suite of tools which will partially automate security and safety assessments, allow some assessments to be done remotely, and provide additional sensor modalities with which to make assessments