Yash P. Gupta

Control of nonlinear processes by using linear model predictive control algorithms

Most chemical processes are inherently nonlinear. However, because of their simplicity, linear control algorithms have been used for the control of nonlinear processes. In this study, the use of the dynamic matrix control algorithm and a simplified model predictive control algorithm for control of a bench-scale pH neutralization process is investigated. The nonlinearity is handled by dividing the operating region into sub-regions and by switching the controller model as the process moves from one sub-region to another. A simple modification for model predictive control algorithms is presented to handle the switching. The simulation and experimental results show that the modification can provide a significant improvement in the control of nonlinear processes.

A closed-loop cross-correlation method for detecting model mismatch in MIMO model-based controllers

Correlation between a dithering signal and the prediction error has been used for detecting model mismatch in univariate model based control systems. This paper extends that approach to MIMO control systems. A closed-loop cross-correlation method is presented to detect which specific input-output pairings of a model-based controller are mismatched. This method may be used in screening the complete set of models and in selecting candidate models for re-identification. The method first finds the rows and columns of the transfer function matrix that contain mismatch, and then the candidates are found by the intersection of the said rows and columns. Placing the system under partial control, whereby one or more of the manipulated variables are held constant, can be used to further reduce the set of candidate models.

Control of integrating processes using dynamic matrix control

Dynamic Matrix Control (DMC) has been popular for the control of chemical and petroleum processes. These processes commonly include integrating process units, which produce a ramp change in the output for a step change in input. When DMC is used for control of integrating process units, a steady-state offset occurs for sustained load changes. This offset is not acceptable for certain applications. A simple modification in the DMC algorithm that eliminates this offset is presented in this paper. The performance of the proposed algorithm is presented on SISO and MIMO example problems through simulations and experimental results.

A Power-Based Time Domain Passivity Control for Haptic Interfaces

In this paper, a power based time domain passivity control is presented. Compared to the energy based time domain passivity control, integration is not needed in the calculation and the energy estimation error is avoided. The new passivity observer (PO) distributes the activation of the passivity controller (PC) along the time index and helps to smooth the output of the PC. Applications of the approach to haptic interfaces are simulated. Comparisons with the two existing energy based approaches are made. Simulation results show the computational simplicity and effectiveness of the approach.

Time domain passivity control of teleoperation systems with random asymmetric time delays

In this paper, the power and energy behavior of bilateral communication with time delay is analyzed. Time delay may result in activeness and in turn generate energy. In a bilateral system, the energy generated by time delay communication can destabilize the whole system. Based on the power based time domain passivity control previously proposed by the authors, a time domain passivity control approach is derived for bilateral communication. After the compensation of time domain passivity control, the bilateral communication is kept passive at every time instant. Then the approach is applied to a bilateral teleoperation system with random asymmetric time delays. Simulation results verify the effectiveness of the approach.

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.

Constrained multivariable control of fluidized catalytic cracking process using linear programming

Model Predictive Control has been used for control of the fluidized catalytic cracking process. The constrained optimization problem involved in the control has generally been solved in a piece-meal fashion in practice. For improved control, the optimization problem needs to be solved directly, that is, without decomposition. In a previous paper, the results of a direct solution using an LP formulation were presented on a simple mathematical model of the FCC process. In this paper, the results of the direct solution are presented on a comprehensive mathematical model of the FCC process. The results show that improved control performance can be obtained by including the constraints within the optimization problem.

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.

Predictive Control of Nonlinear Processes Using Interpolated Models

Chemical processes are nonlinear and have been controlled using linear models. However, controllers based on linear models do not perform well for highly nonlinear situations. Several methods have been proposed to deal with the nonlinearity. Most of these methods are based on fundamental models, in the form of differential equations, that are difficult to obtain for industrial processes. In this paper, a procedure for handling the nonlinearity of industrial processes is presented which is based upon step response models that are easier to obtain. The step response models are obtained for a few sub-regions of the operating region experimentally and the models for other sub-regions are determined through interpolation. The approach is tested on example problems from the literature through simulations. The results show that a significant improvement in the control performance can be achieved in this manner.

Solution of some model predictive control problems using slopes between unconstrained and constrained optimums

At times, the number of controlled variables equals the number of manipulated variables and the objective of the control system is to minimize the difference in the desired and predicted output trajectories subject only to constraints on the manipulated variables. If a simplified model predictive control algorithm is used for such applications, then solution to the optimization problem can be obtained by using the slopes between the unconstrained and constrained optimums. The solution procedure is described for a two-input–two-output case. A comparison with a linear programming (LP) formulation showed that the computational time for the proposed solution was about 35 times less than the time for the LP solution. Copyright