Control of grain drying process using self-tuning quantitative feedback theory

Grain drying process is very important in post-harvest technology. Drying is needed to reduce the moisture content of grains fresh from the fields to a safe level for storage. The challenges in grain drying system nowadays are to produce good quality of grains with minimal production cost and support for the green technology. There are not many automatic controllers applied to the commercial grain dryers and most existing grain dryer systems suffer unsatisfactory performance such as lack of accuracy, robustness,energy efficiency and grain quality. The main reason towards this problem is the inaccuracy of the grain dryer mathematical models which is derived based on assumptions and estimations used in designing the control system. The performance of grain drying systems needs to be improved; therefore, this topic is proposed. A laboratory scale conveyor belt type grain dryer was specially fabricated for this study. System identification technique which utilised experimental input/output data was used to model the grain dryer plant. The obtained grain dryer process model in the form of low order transfer function was validated and the performance was compared with autoregressive with exogenous terms (ARX) model. Test result showed the process model has better modelling performance than ARX model. The robust QFT-based controller was designed based on the obtained grain dryer process model. The controller design was done in two stages. In the first stage, the QFTbased controller was designed offline to meet the robust performance specifications and disturbance attenuation despite of uncertainty. Two ranges of uncertainty were considered; small range and wide range uncertainty. The performance of offline QFTbased controller was compared with PID controller tuned by Ziegler Nichols and Partical Swarm Optimisation (PSO). Tests results showed the superiority of QFT-based controller over PID controller tuned by both methods in terms of faster settling time, smaller percentage of overshoot and smaller control effort. However, the performance of QFT-based controller deteriorated when the parameters variation exceeded the defined uncertainty range. Therefore, in the second stage of design, online QFT-based selftuning controller was proposed. The QFT constraints were integrated into the self-tuning algorithm to ensure the robustness of the controller. Superiority of the online self-tuning controller was proven when the tests results showed that the online controller could adapt to larger uncertainty range than offline controller. Better responses were produced by the online controller especially when larger parameters variation acts on the plant. The percentage of overshoot was reduced from 25% to 0.929%, and settling time from 96 to 36.5 samples. The QFT based controller design by standard procedure successfully meets the predefined specifications. However, due to tighter specifications, online QFT-based selftuning controller improves the transient response for larger uncertainty range and at thesame time improves the QFT design method where the controller’s design is done online.

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