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On simplified predictive control as a generalization of least-squares dynamic matrix control
G C Kember1, R Dubay, S E Mansour
1Department of Engineering Mathematics, Dalhousie University, PO Box 1000, Halifax, NS, Canada, B3J 2X4.
ISA Transactions
|August 9, 2005
Summary
Simplified predictive control (SPC) offers a generalized approach to dynamic matrix control (DMC) for single-input, single-output systems. This method effectively controls faster responses while maintaining system stability and performance.
Area of Science:
- Chemical Engineering
- Control Systems Theory
Background:
- Dynamic Matrix Control (DMC) is a widely used model predictive control strategy.
- Existing DMC formulations can be computationally intensive.
- Simplified Predictive Control (SPC) offers a computationally efficient alternative.
Purpose of the Study:
- To compare the performance of Simplified Predictive Control (SPC) against Dynamic Matrix Control (DMC) and its variants.
- To analyze the generalization capabilities of SPC for single-input, single-output (SISO) systems.
- To evaluate control performance within a two time-step control horizon.
Main Methods:
- Closed-loop, continuous analysis of control algorithms.
- Discrete-time formulation and comparison of SPC and DMC.
- Evaluation of system conditioning and response speed.
Main Results:
- The discrete form of SPC was shown to generalize discrete DMC algorithms.
- SPC demonstrated effective control for responses faster than one-half the process response time.
- The SPC approach maintained well-conditioned system dynamics.
Conclusions:
- Simplified Predictive Control (SPC) provides a robust and generalized framework for model predictive control.
- SPC is particularly advantageous for achieving faster closed-loop responses in SISO systems.
- This study validates SPC as an efficient and effective control strategy compared to traditional DMC.