Anti-Windup Designs for Multivariable Controllers

This paper addresses two important aspects of anti-windup (AW) designs, namely the parametrization of linear AW compensators, and the role of artificial nonlinearity (AN) in the design of AW compensators for multivariable systems. For the first issue, a simple parametrization is given using the classical feedback structure in the framework of constrained unity-feed-back multivariable control systems. For the second issue, two existing AN designs for coordinating plant inputs whenever one plant input enters saturation are reviewed. A comparative simulation study illustrates that the conditioning technique, enhanced by optimal AN design, gives the best tracking performance among different existing methods.     

Discrete Adaptive Sliding Mode Control of a State-Space System with a Bounded Disturbance

This paper presents the discrete adaptive sliding mode control of a state-space system in the presence of a bounded disturbance. The delta form of the discrete state-space model is used as it closely resembles that of the continuous model. The control law takes into account of the effect of the disturbance by using its approximate value. The system behavior in the vicinity of the sliding surface is studied. It is shown that the adaptive controller leads to a stable closed-loop system. Also, simulation results are presented to illustrate the features of the proposed adaptive control strategy.     

Multivariable process identification for mpc: the asymptotic method and its applications

In this work we will introduce the asymptotic method (ASYM) of identification and provide two case studies. The ASYM was developed for multivariable process identification for model based control. The method calculates time domain parametric models using frequency domain criterion. Fundamental problems, such as test signal design for control, model order/structure selection, parameter estimation and model error quantification, are solved in a systematic manner. The method can supply not only input/output model and unmeasured disturbance model which are asymptotic maximum likelihood estimates, but also the upper bound matrix for the model errors that can be used for model validation and robustness analysis. To demonstrate the use of the method for model predictive control (MPC), the identification of a Shell benchmark process (a simulated distillation column) and an industrial application to a crude unit atmospheric tower will be presented.     

A Multivariable Two-Degree-Of-Freedom Control Methodology

This paper presents a two-degree-of-freedom design methodology based on a decoupling scheme. The proposed scheme is derived from the Youla parametrization and leads to a two-step design procedure. In the first step, a model-matching approach is proposed in order to set the desired nominal tracking objectives, while in the second step, μ-synthesis techniques are used in order to deal with the robust performance objectives. Special attention is paid to the robust tracking problem. For unstructured uncertainty, the most significant feature of the proposed design is that robust tracking requirements can be achieved, in a satisfactory way, by a simple H_∞ optimization. We carry out the design example on an ill-conditioned plant.     

A multivariate statistical controller for on-line quality improvement

Producing good quality products is an important process control objective. However, achieving this objective can be very difficult in a continuous process, especially when quality measurements are not available on-line or they have long time delays. In this paper, a control approach using multivariate statistical models is presented to achieve this objective. The goal of the control approach is to decrease variations in product quality without real time quality measurements. A PCA model which incorporates time lagged variables is used, and the control objective is expressed in the score space of this PCA model. A controller is designed in the model predictive control (MPC) framework, and it is used to control the equivalent score space representation of the process. The score predictive model for the MPC algorithm is built using partial least squares (PLS). The proposed controller can be developed from and implemented on top of existing PID control systems, and it is demonstrated in two case studies, which involve a binary distillation column and the Tennessee Eastman process.