Limits of control performance: a model-based approach

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Basic control has matured to the point where many advanced control algorithms are now standard products in most DCS systems. For many critical loops it is desired to obtain the highest level of control performance given a specific type of control strategy, e.g. Proportional, Integral and Derivative (PID) or Model-based Predictive Control (MPC). Such fundamental limits to control performance are due to specific process, actuator and sensor characteristics and can be expressed in terms of the bounds due to Bode's integral theorem on the sensitivity and complementary sensitivity functions. An understanding of these types of factors and their impact on the performance that can be attained with a 'well designed' controller are important factors and can most easily be described if the process model is known.Therefore the specific focus of this research proposal will be to explore the limits of control performance from a model-based perspective, unlike the historical data-based perspective. The data-based performance monitoring approach is now well established and its focus has been on computing performance metrics from routine process data and comparing them to a benchmark, for example the minimum variance benchmark or other used-specified benchmarks.  The topic of the best achievable control given some knowledge of the process dynamics and controller type, for example a PI(D) controller, will be the main focus of this research proposal.Over 90% of the controllers used in industry are (PID) type and yet the issue of the best achievable PI(D) control for a given class of first order plus time-delay or a second order plus time-delay process is an open problem. This proposal is to explore these fundamental limitations of PI(D) controllers when applied to the most common industrial processes.