Improving disturbance rejection of PID controllers by means of the magnitude optimum method.

The magnitude optimum (MO) method provides a relatively fast and non-oscillatory closed-loop tracking response for a large class of process models frequently encountered in the process and chemical industries. However, the deficiency of the method is poor disturbance rejection performance of some processes. In this paper, disturbance rejection performance of the PID controller is improved by applying the "disturbance rejection magnitude optimum" (DRMO) optimisation method, while the tracking performance has been improved by a set-point weighting and set-point filtering PID controller structure. The DRMO tuning method requires numerical optimisation for the calculation of PID controller parameters. The method was applied to two different 2-degrees-of-freedom PID controllers and has been tested on several different representatives of process models and one laboratory set-up. A comparison with some other tuning methods has shown that the proposed tuning method, with a set-point filtering PID controller, is quite efficient in improving disturbance rejection performance, while retaining tracking performance comparable with the original MO method.

Comparative study of decay ratios of disturbance-rejection magnitude optimum method for PI controllers.

One of the key time-domain closed-loop performance requirements is the closed-loop response decay ratio. In this paper, the decay ratios of the disturbance-rejection magnitude optimum (DRMO) tuning method [Vrancic D, Strmcnik S, Kocijan J. Improving disturbance rejection of PI controllers by means of the magnitude optimum method. ISA Trans 2004; 43: 73-84; Vrancic D, Strmcnik S. Achieving optimal disturbance rejection by using the magnitude optimum method. In: Pre-prints of the CSCC'99 conference. 1999. p. 3401-6] are analyzed and compared to decay ratios of two other modern tuning methods, i.e. the Kappa-Tau tuning method (based on time-domain step-response characteristics) [Aström KJ, Högglund T. PID controllers: Theory, design, and tuning. 2nd ed. Instrument Society of America; 1995] and the non-convex optimization tuning method (based on frequency response) [Panagopoulos H, Aström KJ, Hägglund T. Design of PI controllers based on non-convex optimization. Automatica 1998; 34: 585-601; Panagopoulos H, Aström KJ, Hägglund T. Design of PID controllers based on constrained optimisation. IEE Proc Control Theory Appl 2002; 149 (1): 32-40]. It is shown that the DRMO method results in such a closed-loop response that the decay ratio is within a relatively narrow interval when compared to the other two methods.

Pattern recognition-based supervision of indirect adaptation for better disturbance handling.

An advanced pattern recognition-based supervision algorithm for an indirect adaptive controller is proposed. The aim is to improve performance under certain conditions that are common in the industrial environment, in which indirect adaptive controllers with simple supervision are known to perform poorly or unreliably. Specifically, the problem of large invasive unmeasured disturbances of short or longer duration is addressed. The supervisor is designed to recognize such events as quickly as possible by analysis of recent control signals, without additional measurements. It applies appropriate strategies to prevent model degradation by learning from misleading data and to maintain acceptable performance under unfavorable conditions. As an illustration, it has been applied to the control of a model of a semi-cleanroom HVAC installation subsystem.

Improving disturbance rejection of PI controllers by means of the magnitude optimum method.

The magnitude optimum (MO) method provides a relatively fast and nonoscillatory closed-loop tracking response for a large class of process models frequently encountered in the process and chemical industries. However, the deficiency of the method is poor disturbance rejection when controlling low-order processes. In this paper, the MO criterion is modified in order to optimize disturbance rejection performance, while the tracking performance has been improved by an integral set-point filtering PI controller structure. The new tuning rules, referred to as the disturbance rejection magnitude optimum (DRMO) method, were applied to several different two-degrees-of-freedom PI controllers. The DRMO method has also been tested on several different representatives of process models. The results of experiments have shown that the proposed tuning method with the integral set-point filtering PI controller is quite efficient in improving disturbance rejection performance, while retaining tracking performance comparable to the original MO method.

A new approach to PLC software design.

This paper presents a model-based approach to PLC software development. The essence of this approach is the introduction of a new procedural modeling language called ProcGraph. In contrast to commonly used methods, ProcGraph deals with the procedural aspect of the control system and allows software specification at a higher level of abstraction. The modeling language has been supported with the development of a software tool which facilitates graphical model design and automatic code generation. The specification notation has been tested in the development of software for industrial applications. The supporting tool has been tested in a laboratory environment.