Strategies for Multivariable Control

Control Strategies for Multivariable Control If a conventional multiloop control strategy performs poorly due to control loop interactions, a number of solutions are available  

Choose different controlled or manipulated variables (or their pairings). 

Use a multivariable control scheme (e.g., model predictive control). 

Detuning a controller (e.g., using a smaller controller gain or a larger reset time) tends to reduce control loop interactions by sacrificing the performance for the detuned loops. This approach may be acceptable if some of the controlled variables are faster or less important than others. 

Decoupling Control Systems Decoupling control systems provide an alternative approach for reducing control loop interactions. The basic idea is to use additional controllers called decouplers to compensate for undesirable process interactions. 

and U2 affects only w. Thus, the control loop interactions have been eliminated. Similarly, for the pH neutrahzation process in Fig. 8-39h, the control loop interactions would be greatly reduced if pH were controlled hy U = Wa (wa + Wg) and liquid level h were controlled by 

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Process control books and journal articles tend to emphasize problems with a single controlled variable. In contrast, many processes require multivariable control with many process variables to be controlled. In fact, for virtually any important industrial process, at least two variables must be controlled product quality and throughput. In this section, strategies for multivariable control are considered. 

SVA, etc.) for multivariable control problems. In the absence of process models, one must resort to heuristic (rule-of-thumb) approaches. Although these approaches generally are based on prior experience, they also incorporate an understanding of the fundamental physics and chemistry that apply to all plants. In this chapter, several case studies are used to introduce important plantwide concepts. In the final chapter (Appendix H), we present a general strategy for designing plantwide control systems. 

Dougherty, D., and Cooper, D., A practical multiple model adaptive strategy for multivariable model predictive control, Control Eng. Practice, 2003, 11, 649. 

Shridhar, R., and Cooper, D. J., A Novel Tuning Strategy for Multivariable Model Predictive Control. ISA Transaction. 1998, 36, 273. 

Even after linearization, the state-space model often contains too many dependent variables for controller design or for implementation as part of the actual control system. Low-order models are thus required for on-line implementation of multivariable control strategies. In this section, we study the reduction in size, or order, of the linearized model. 

The challenges in SMB control are not only the complex nonlinear process dynamics, but also the long delays of the effect of disturbances. The required control strategy has to be able to handle multivariable dynamics with time-delays and hard constraints. Model Predictive Control (MPC) has been proven to be the most effective control strategy for this type of problems [1,2]. Only recently, a few scientific publications have addressed the automatic... 

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