Biomedical subjects

Yash P Gupta

Publications and source records attributed to Yash P Gupta.

3 recordsLinked to original sources

PubMed2006

Robust and nominal stability conditions for a simplified model predictive controller.

A new condition is derived that guarantees robust stability for a set of stable, linear time-invariant plants controlled by using a simplified model predictive control algorithm (SMPC). Discrete single-input-single-output control systems are considered in this paper. Uncertainty is treated in the time domain by considering the stabilization of a set of pulse response functions. The method presented is suitable for stabilizing a set of plants that are not necessarily related. Central to this method is a bounding function, which is a function of the model and controller parameters. The bounding function is designed to have a larger magnitude than all of the pulse response functions in the set of plants to be stabilized. Using this method, it was found that the bounding function is monotonically decreasing when a first-order plus dead-time model is used to design the controller. This allows the coincidence point used in SMPC to be employed directly as a tuning "knob" for robustness, and also simplifies the analysis for dead-time uncertainty. In addition, a comparison of two nominal stability conditions is provided.

Journal Article↗

PubMed2005

A simplified predictive control algorithm for disturbance rejection.

Model predictive control (MPC) offers several advantages for control of chemical processes. However, the standard MPC may do a poor job in suppressing the effects of certain disturbances. This shortcoming is mainly due to the assumption that disturbances remain constant over the prediction horizon. In this paper, a simple disturbance predictor (SDP) is developed to provide predictions of the unmodeled deterministic disturbances for a simplified MPC algorithm. The prediction is developed by curve fitting of the past information. A tuning parameter is employed to handle a variety of disturbance dynamics and a procedure is presented to find an optimum value of the tuning parameter online. A comparison is made with the commonly used disturbance prediction on three example problems. The results show that an improved regulatory performance and zero offset can be achieved under both regular and ramp output disturbances by using the proposed disturbance predictor.

Journal Article↗

PubMed2004

Solution of low-dimensional constrained model predictive control problems.

Large benefits are possible by utilizing the solution of the constrained optimization problem involved in model predictive control. For a special case of these problems, the solution can be obtained relatively easily from its relationship with the unconstrained optimum. In this paper, a visualization of the relationship between the constrained and unconstrained optimum is presented. Based upon this relationship, a method for finding the constrained optimum is proposed that is suitable for low-dimensional control systems. A comparison with a linear programming formulation on 2 x 2 and 3 x 3 problems shows that the computational effort can be 10-35 times lower. For such processes, the proposed approach may allow one to avail the benefits of optimization by using the small process control systems already present in many plants.

Journal Article↗