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In this article, a nonlinear autoregressive with exogenous input (NARX) network was utilized to model a conveyor belt grain dryer using a set of input–output data collected during an experiment to dry paddy grains. The resulting NARX model has achieved a remarkable modeling accuracy compared to other previously reported modeling techniques. To control the considered dryer, a simplified type 2 adaptive neuro-fuzzy inference system (ANFIS) controller was proposed. The effectiveness of this controller was demonstrated by several performance tests conducted by computer simulations. Moreover, a comparative study with other related controllers further confirmed the superiority of the proposed dryer controller.

The grain drying process is one of the most critical post-harvest operations in modern agricultural production. Efficient control of this process is an indispensable requirement especially in light of recent demands for handling latest increase in energy costs,achieving current requirement for eco-friendly technologies, and producing products of high quality.The grain drying process is characterized by its complex nature. As a result, the mathematical models developed for these systems consist of sets of highly complex and nonlinear partial differential equations (PDEs) which require highly complicated numerical techniques to solve them. Therefore, these models are not suitable for control system design. Moreover, despite the complexity of the drying process, grain dryers, in particular conveyor-belt grain dryers, are still controlled by conventional PID controllers. The major objective of this research is to improve the performance of conveyor-belt grain dryers by designing an intelligent control system utilizing the capabilities of the adaptive neuro-fuzzy inference system (ANFIS) to model and control the drying process. To achieve this objective, a laboratory-scale conveyor-belt grain dryer was specifically fabricated for this study. As the main controller in this work, a simplified ANFIS structure is proposed to act as a proportional-integral-derivative (PID)-like feedback controller to control nonlinear systems. This controller has several advantages over its conventional ANFIS counterpart, particularly the reduction in processing time. Moreover, three evolutionary algorithms (EAs), in particular a real-coded genetic algorithm (GA), a particle swarm optimization (PSO), and a global-best harmony search (GHS), were separately used to train the proposed controller and to determine its scaling factors. These EAs overcome a common problem encountered in derivative-based learning methods, which is the necessity for the teaching signal when applying the ANIFIS as a co

This paper presents a simplified ANFIS (Adaptive Neuro-Fuzzy Inference System) structure acting as a PID-like feedback controller to control nonlinear multi-input multi-output (MIMO) systems. Only few rules have been utilized in the rule base of this controller to provide the control actions, instead of the full combination of all possible rules. As a result, the proposed controller has several advantages over the conventional ANFIS structure particularly the reduction in execution time without sacrificing the controller performance, and hence, it is more suitable for real time control. In addition, the real-coded genetic algorithm (GA) has been utilized to train this MIMO ANFIS controller, instead of the hybrid learning methods that are widely used in the literature. Consequently, the necessity for the teaching signal required by other techniques has been eliminated. Moreover, the GA was used to find the optimal settings for the input and output scaling factors for this controller, instead of the widely used trial and error method. To demonstrate the accuracy and the generalization ability of the proposed controller, two nonlinear MIMO systems have been selected to be controlled by this controller. In addition, this controller robustness to output disturbances has been also evaluated and the results clearly showed the remarkable performance of this MIMO controller.

This paper presents a simplified adaptive neuro-fuzzy inference system (ANFIS) controller to control nonlinear multi-input multi-output (MIMO) systems. This controller uses only few rules to provide the control actions, instead of the full combination of all possible rules. Consequently, the proposed controller possesses several advantages over the conventional ANFIS controller especially the reduction in execution time, and hence, it is more appropriate for real time control. A real-coded genetic algorithm (GA) was utilized to optimize the premise and the consequent parameters of the ANFIS controller, instead of the hybrid learning methods that are widely used in the literature. Accordingly, the necessity for the teaching signal required by other optimization techniques has been eliminated. Furthermore, the GA was employed to determine the input and output scaling factors for this controller, instead of the widely used trial and error method. Two nonlinear MIMO systems were chosen to be controlled by this controller. In addition, the controller robustness to output disturbances was also investigated and the results clearly showed the notable accuracy and the generalization ability of this controller. Moreover, the result of a comparative study with a conventional MIMO ANFIS controller has indicated the superiority of the simplified MIMO ANFIS controller.

The grain drying process is one of the most critical post-harvest operations in modern agricultural production. Development of a reliable control strategy for this process plays an important role in improving the overall efficiency and productivity of the drying process. In control system design, the first problem to be addressed is the availability of a relatively simple and accurate model of the process to be controlled. However, the majority of the models developed for the grain drying process and the numerical methods required to solve them are characterized by their highly complex nature, and thus they are not suitable to be utilized in control system design. This paper presents an application of a neuro-fuzzy system, in particular the adaptive neuro-fuzzy inference system (ANFIS), to develop a data-driven model for a conveyor-belt grain dryer. This model can be easily used in control system design to develop a reliable control strategy for the drying process. By conducting a real-time experiment to dry paddy grains, a set of input-output data were collected from a laboratory-scale conveyor-belt grain dryer. These data were then presented to the ANFIS network in order to learn the nonlinear functional relationship between the input and output data by this network. Based on utilizing a clustering method to identify the structure of the ANFIS network, the resulting ANFIS model has shown a remarkable modeling performance to represent the drying process. In addition, the modeling result achieved by this ANFIS model was compared with those of an autoregressive with exogenous input (ARX) model and an artificial neural network (ANN) model, and the results clearly showed the superiority of the ANFIS model.

This paper presents a PID-like adaptive neuro-fuzzy inference system (ANFIS) controller that can be trained by the global-best harmony search (GHS) technique to control nonlinear systems. Instead of the hybrid learning methods that are widely used in the literature to train the ANFIS structure, the GHS technique alone is used to train the ANFIS as a feedback controller, and hence, the necessity for the teaching signal required by other techniques has been eliminated. Moreover, the input and output scaling factors for this controller are also determined by the GHS. To show the effectiveness of this controller and its learning method, two nonlinear plants, including the continuous stirred tank reactor (CSTR), were used to test its performance in terms of generalization ability and reference tracking. In addition, this controller robustness to output disturbances has been also tested and the results clearly indicate the remarkable performance of this controller.

Controlling grain drying process has always been a challenging task for engineers and researchers in food and agricultural sectors. The main obstacles to obtain the best control system for the grain drying system are due to the long delay process, highly non-linear behaviour and parameter uncertainties exist in the plant. Applying an intelligent controller such as fuzzy logic controller to a grain drying system is a good choice as fuzzy logic controller is a very powerful control methodology that can estimate functions based on partial knowledge of the system in case of parameter uncertainties and can deal with non-linear behaviour. This paper focused on the design and application of fuzzy logic controller in order to obtain the grain output moisture content close to the set-point in spite of disturbances. Two inputs and one output fuzzy logic controller has been designed to drive the grain flow rate which is used as the manipulated variable. A new algorithm of fuzzy logic controller for a grain drying process has been introduced. Simulation tests have been carried out using the process model developed by Liu and Bakker-Arkema for a cross-flow grain dryer. The overall results from the tests are very promising and the fuzzy logic controller is stable and robust towards input disturbance. Although the design process of fuzzy logic controller is simple; however it provides very fast response to make the grain output moisture content close to the set-point and to reject disturbance exists during the grain drying process.