Self-tuning run-time reconfigurable PID controller

Journal title

Archives of Control Sciences




No 2

Publication authors

Divisions of PAS

Nauki Techniczne


Archives of Control Sciences welcomes for consideration papers on topics of significance in broadly understood control science and related areas, including: basic control theory, optimal control, optimization methods, control of complex systems, mathematical modeling of dynamic and control systems, expert and decision support systems and diverse methods of knowledge modelling and representing uncertainty (by stochastic, set-valued, fuzzy or rough set methods, etc.), robotics and flexible manufacturing systems. Related areas that are covered include information technology, parallel and distributed computations, neural networks and mathematical biomedicine, mathematical economics, applied game theory, financial engineering, business informatics and other similar fields.

Aims and Scope: Archives of Control Sciences publishes papers in the broadly understood field of control science and related areas while promoting the closer integration of the Polish, as well as other Central and East European scientific communities with the international world of science.


Self-tuning run-time reconfigurable PID controller Digital PID control algorithm is one of the most commonly used algorithms in the control systems area. This algorithm is very well known, it is simple, easily implementable in the computer control systems and most of all its operation is very predictable. Thus PID control has got well known impact on the control system behavior. However, in its simple form the controller have no reconfiguration support. In a case of the controlled system substantial changes (or the whole control environment, in the wider aspect, for example if the disturbances characteristics would change) it is not possible to make the PID controller robust enough. In this paper a new structure of digital PID controller is proposed, where the policy-based computing is used to equip the controller with the ability to adjust it's behavior according to the environmental changes. Application to the electro-oil evaporator which is a part of distillation installation is used to show the new controller structure in operation.


Committee of Automatic Control and Robotics PAS




ISSN 1230-2384


R. Anthony: (n.d.) <a target="_blank" href=''></a> ; Anthony R. (2006), A policy-definition language and prototype implementation library for policy-based autonomic systems, null, 265. ; Anthony R. (2010), Context-aware reconfiguration of autonomic managers in real-time control applicaitons, null, 73. ; Anthony R. (2008), Flexible and robust run-time configuration for self-managing systems, null, 491. ; Byrski W. (1993), Observers and their applications in adaptive control systems, Scientific Bulletins of The University of Mining and Metallurgy, 1551, 65. ; Byrski W. (1984), Optimal finite parameter observer. An application To synthesis of stabilizing feedback for a linear system, Control and Cybernetics, 13, 1. ; Byrski W. (2005), Modelling and simulation of state observers in the computer control systems, null. ; Byrski W. (2006), Continuous and discrete integral state observers in online control systems, null. ; Chia-Ju W. (1999), Genetic tuning of PID controllers using a neural network model: A seesaw example, J. Intell. Robotics Syst, 25, 43, ; Corcau J. (2008), An adaptive pid fuzzy controller for synchronous generator. ; Fujinaka T. (2000), Stabilization of double inverted pendulum with self-tuning neuro-PID, IEEE Computer Society. ; Jie G. (2009), Application of parameter self-tuning fuzzy PID controller in guidance loop of unmanned aircraft, IEEE Computer Society. ; Karakasal O. (2005), Implementation of a new self-tuning fuzzy pid controller on PLC, Turkish J. of Electrical Engineering, 13, 2, 277. ; Kuan-Yu C. (2009), A self-tuning fuzzy PID-type controller design for unbalance compensation in an active magnetic bearing, Expert Syst. Appl, 36, 8560, ; Lima J. (2000), Neuro-genetic pid autotuning: time invariant case, Math. Comput. Simul, 51, 287, ; Lin F. (2000), Self-tuning of PID controllers by adaptive interaction, null, 3676. ; Pelc M. (2009), Policy supervised exact state reconstruction in real-time embedded control systems, null. ; Pelc M. (2010), Context-aware real-time systems with autonomic controllers, null. ; Shiuh-Jer H. (2009), Metal chamber temperature control by using fuzzy pid gain auto-tuning strategy, WSEAS Trans. Sys. Ctrl, 4, 1. ; Ward P. (2008), Embedding dynamic behavior into a self-configuring software system, null, 373. ; Yau-Tarng J. (2008), Design of fuzzy PID controllers using modified triangular membership functions, Inf. Sci, 178, 1325, ; Yazdizadeh A. (2009), Adaptive neuro-PID controller design with application to nonlinear water level in NEKA power plant, J. of Applied Sciences. ; Zaheer-Uddin M. (2004), Neuro-PID tracking control of a discharge air temperature system, Energy Conversion and Management.