Transactions of the Institute of Measurement and Control

Mid-ranging refers to control problems where there are two manipulated inputs and only one output to control. Most mid-ranging controllers in industrial use today are based on the valve position control concept. Presently, design and tuning of valve position control schemes is largely done ad hoc. As a result, much time is spent devising control strategies that may work well in one application, but not nearly so well in another. To address this problem, we set out to devise a systematic approach to the design and tuning of valve position controllers. The new method was tested in simulation, and pilot plant and paper mill examples were used to demonstrate the performance of the method in several real-life applications. Overall, the proposed design was able to reject disturbances quicker and mid-range faster, and with less oscillation than conventional schemes. This approach may be applied to virtually any mid-ranging control application and is easily implemented on any distributed control system.

Multivariate statistical representations have been widely used in the process manufacturing industries for process performance monitoring, in particular for the detection of changes in current operation and the onset of process disturbances or faults. Applications of the technology have focused to a lesser extent on manufacturing processes where drift occurs over time as part of normal process operation, e.g., due to reactor fouling, machine wear, ramping of temperatures during process operation, and changes due to set-point adjustments. In this paper, an extension to the methodology based on the statistical projection technique of principal component analysis (PCA) is proposed for the monitoring of processes where drift and set-points changes are common place, i.e., exponentially weighted PCA. The technique is illustrated through its application to a polymer film manufacturing process where the representation is required to adapt quickly to changes in the process that are part of normal operating procedures, but remain sensitive to the detection of deviations from normal operation.

Paper manufacturing consists of the sequential removal of over 90% of the water from pulp through gravity, vacuum dewatering, pressing and thermal drying. Control of moisture content is important for paper quality and energy economy. Current strategy for the control of moisture content uses a feedback sensor at the end of the process to adjust the dryers. This introduces a long dead-time and causes excessive use of the dryers, which translate to limitations in performance, robustness and inefficient energy usage. In this paper, we investigate a new control approach in which in-process moisture contents are estimated using air-flow as surrogate measurements, and the pressure settings in the vacuum dewatering boxes are adjusted according to the surrogate measurements. A pre-emptive control algorithm is developed which has the ability to decouple and eliminate the effects of the disturbances that occur upstream in the process from the downstream. Robustness analysis and simulation studies suggest that as long as the surrogate measurements are accurate, the proposed control scheme will be robust and accurate.

This paper describes a new approach towards a model-based optimization of internal combustion engine control maps. The goals of the optimization are - at the same time - minimum fuel consumption, low emissions and a good driveability. First the structure of a torque-oriented engine management system based on control maps is described. Then, an optimization environment is developed, which calculates the basic control maps for the engine settings based on the modelled emission behaviour of the engine. The underlying nonlinear models are realized by fast neural networks. Results in both simulation and measurements prove the quality of the proposed methodology.

Using a double-controller strategy and a design approach related to Dahlin’s controller, a new sampled data control scheme is presented that is suitable for handling both set-point change and load disturbances. This control scheme has two controllers, a set-point controller and a load controller, which result in the separation of the load response from the set-point response in a closed-loop system. These two controllers can be designed independently to achieve good system performance for both set-point tracking and load rejection. The control scheme is applicable to processes that can be approximated by first-order plus time delay dynamics. For set-point changes, the set-point controller is a generalized Dahlin controller that has an extra tuning parameter T_LC and has more ‘exibility and more robustness than Dahlin’s controller. For load disturbances, the load controller is also a generalized Dahlin controller and shows a significant improvement over the performance of Dahlin’s controller. The new double-controller scheme also alleviates a difficult compromise that the generalized Dahlin controller makes between the set-point tracking performance and load rejection performance. A simulation study is used to evaluate the performance of this new double-controller scheme in the presence of noise and model errors, and to compare it to Dahlin’s controller and the generalized Dahlin controller. The results show that the proposed double-controller scheme is superior to both Dahlin’s controller and the generalized Dahlin controller.