State Space Model Based PID Controller Tuning

Advances in digital computing over the last years have resulted in new and powerful tools for obtaining process models. An example of such a tool is the dsr toolbox, which gives a state space model based on measured input/output data. Also, new control strategies based on these models have developed, usually involving optimization techniques. Despite this, the classical PID controller still has advantages and remain the most used control technique. The goal of the thesis was to compare different methods for tuning PID controllers. The advantages and disadvantages of the different methods should be explained and suggestions of how the methods could be used with state space models should be discussed. The Matlab pidtune function and the delta tuning rules should be explained and evaluated in relation to state space models. The tuning methods Ziegler Nichols, SIMC, Cohen-Coon, and optimization tuning in addition to δ-tuning and pidtune, was chosen to examine in detail. To obtain model parameters for controller tuning from state space models, a graphical method, an optimization method and the Matlab function procest was used. Pidtune, mftune, megatuner, and optimization based tuning is used directly with SSM and was also tested. For method comparison, both commonly known process models and random models were used. The methods which can be used directly on state space models give the best results in terms of successful tuning attempts. For many higher order SSM, process describing variables such as K, θ , T, R, and L can be found successfully by graphical estimation or optimization. These variables are then used for PID controller tuning. The graphical method is the fastest and gives the highest success-rate, while optimization estimation results in higher closed-loop performanc