Adaptive Predictive Control System Research Articles

Comparison of an auto-tuned PID regulator and an adaptive predictive control system on an industrial bleach plant

A PID-based scheme and an adaptive-predictive controller are compared on the same industrial bleach plant. The PID scheme is auto-tuned and uses a linearizing block and gain scheduling to compensate for sensor nonlinearities and production rate changes, while APCS treats those as unknown perturbations. Both schemes behave well. It shows that, when nonlinearities are known and changes in process dynamics are predictable, gain scheduling combined with a well-tuned PID regulator behave remarkably well. At the same time, it shows that adaptive control can handle the same situation with less tailoring.

ADAPTIVE CONTROL OF A BATCH REACTOR

A globally stable adaptive predictive control system (APCS) is evaluated by application to a simulated PVC batch reactor. The reactor is run under APCS control with the objective of either temperature setpoint tracking or constant reaction rate. The batch nature of this system makes it possible to learn about the physical problem from successive runs. This knowledge is incorporated into the control strategy to improve the performance of the reactor. The problem of excessive manipulation of the control variable has been recognized and resolved by using control weighting. Performance of the adaptive technique is compared with previous results using self-tuning and PID control of the same reactor. APCS provides good, robust control despite the nonlinear dynamics of the system.

Industrial Comparison of an Auto-Tuned PID Regulator and an Adaptive Predictive Control System (APCS)

A novel technique for automatically tuning PID controllers and an adaptive-predictive control scheme (APCS) have been applied to the same loop in an industrial bleach plant. The PID uses a linearizing block and gain scheduling to compensate for sensor non-linearities and production rate changes while APCS treated the latter as an unknown perturbation. Both schemes gave excellent performance and proved very reliable. This shows that, when non-linearities are known and changes in process dynamics are predictable, then gain scheduling combined with a well-tuned PID regulator behave remarkably well. At the same time, the results have also shown that adaptive control can easily handle this situation with less tailoring.

Multivariable adaptive predictive control of a binary distillation column

This paper presents a tutorial review of an adaptive predictive control system (APCS). Special emphasis is given to the key issues involved in the practical application of APCS to real processes. These practical issues are illustrated by actual application of SISO and MIMO control of a pilot scale binary distillation column. The experimental evaluation of this method reveals the simplicity of the adaptive algorithm and its excellent performance in an industrial type environment. The experimental results easily outperformed well-tuned classical PID controllers. A brief review of other applications of adaptive control to chemical processes is also included in this paper.

A globally stable APCS in the presence of bounded noises and disturbances

This paper presents a globally stable adaptive predictive control system (APCS) for discrete linear processes with pure time delays in the presence of bounded measurement and process noise plus unmeasured disturbances. The main contribution is the proof of stability in the context of discrete adaptive control schemes. This proof is based on the minimization of the a posteriori estimation error and the convergence of the estimated parameters.

A stable adaptive predictive control system

An adaptive, predictive control system (APCS) is described and a formal proof of global stability is presented for the case of multi variable, stable – in verse, time – invariant processes in the presence of bounded, unmeasured disturbances plus process and measurement noise. Global stability is proven in the sense that the process input / output vector remains bounded and the control error is minimized. The proof of stability is based on certain properties of the aposteriori estimation error and convergence properties of the estimated parameters. Reference is also made to successful experimental and simulation applications of APCS.

An adaptive predictive control system for random signals

A predictive system was described by Chestnut, et al. [4]. The purpose of this work is to increase the types of plants which can be controlled by such systems by making the fast time model adaptive, and to experimentally compare the random signal behavior of the resultant system with various other types of controllers. These controllers include a relay controller adjusted to switch when the error is zero, and a linear controller. According to the criteria of the minimum integral of the absolute error, the adaptive predictive controller delivered the most satisfactory-responses for second- and third-order systems.