A genetically trained simplified ANFIS controller to control nonlinear MIMO systems

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

Utilizing global-best harmony search to train a PID-like ANFIS controller

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

Intelligent modeling and control of a conveyor belt grain dryer using a simplified type 2 neuro-fuzzy controller

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

Neuro-fuzzy modeling of a conveyor-belt grain dryer

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

Intelligent control of grain drying process using fuzzy logic 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

Design of intelligent control system and its application on fabricated conveyor belt grain dryer

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 controller.To demonstrate the effectiveness of the proposed controller, several non-linear plant models were used to evaluate its performance in terms of control accuracy, generalization ability, and robustness against external disturbances and parameter variations in the controlled system. In addition, several comparative studies were conducted with other related controllers, namely a conventional ANFIS controller, a variation of the ANFIS network called complex fuzzy basis function network (CFBFN),and a conventional PID controller. Furthermore, the ability of the simplified ANFIS controller to control nonlinear multi-input multi-output (MIMO) systems was also investigated. The results of all these tests clearly indicated the notable performance of the proposed controller. After fabricating the conveyor-belt grain dryer, a real-time experiment was conducted to dry paddy grains, in particular the MR 219 rice variety. The grains were first re-wetted to a moisture content (MC) of about 18% wet basis (wb). Next, by fixing the dryer operating conditions of temperature, flow rate, and humidity of the drying air, the voltage to the dryer motor was manipulated in a pre-specified sequence to give the required conveyor-belt speed for each paddy sample. The corresponding MC of each of these samples was measured by the XM 120 Moisture Analyzer. The result was a set of 50 input-output samples which were then presented to an ANFIS network to develop the desired process model. The modeling performance achieved by this ANFIS model was then 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 developed ANFIS model. The simplified ANFIS controller was then applied to control the developed ANFIS-based dryer model using different initial conditions based on real data. In addition, five robustness tests were made to evaluate the controller ability in handling unexpected changes in the drying operating conditions. Furthermore, a comparative study with a genetically-tuned PID controller was conducted. From all these tests, the simplified ANFIS controller has proved its remarkable ability in controlling the grain drying process represented by the developed ANFIS model

A simplified adaptive neuro-fuzzy inference system (ANFIS) controller trained by genetic algorithm to control nonlinear multi-input multi-output systems

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

Optimal Backstepping and Feedback Linearization Controllers Design for Tracking Control of Magnetic Levitation System: A Comparative Study

In this paper, the stabilization and trajectory tracking of the magnetic levitation (Maglev) system using optimal nonlinear controllers are considered. Firstly, the overall structure and physical principle represented by the nonlinear differential equations of the Maglev system are established. Then, two nonlinear controllers, including backstepping control (BSC) and feedback linearization (FL), are proposed to force the position of the ball in the Maglev system to track a desired trajectory. In terms of designing the control law of the BSC, the Lyapunov function is utilized to guarantee an exponential convergence of the tracking error to zero. For developing the control law of the FL, an equivalent transformation to convert the nonlinear system into a linear form is used, and then, the state feedback controller (SFC) method is utilized to track the ball to the desired position. In order to obtain a higher accuracy in motion control of the ball, the gains’ selection for the controllers to reach the desired response is achieved using the swarm bipolar algorithm (SBA) based on the integral time absolute error (ITAE) cost function. Computer simulations are conducted to evaluate the performance of the proposed methodology, and the results prove that the proposed control strategy is effective not only in stabilizing the ball but also in rejecting the disturbance present in the system. However, the BSC exhibits better performance than that of the FL-SFC in terms of reducing the ITAE index and improving the transit response even when the external disturbance is applied. The numerical results show that the settling time reduced to 0.2 seconds compared to 1.2 seconds for FL-SFC. Moreover, the ITAE index is reduced to 0.0164 compared to 0.2827 seconds for FL-SFC. In the context of external disturbance, the findings demonstrate that BSC reduced the recovery time to 0.05 seconds compared to 0.65 seconds for FL-SFC

Investigation of Optimal Controllers on Dynamics Performance of Nonlinear Active Suspension Systems with Actuator Saturation

This study investigates designing optimal controllers on the dynamics performance of active suspension systems. The study incorporates nonlinearities and actuator saturation in the mathematical model of the suspension system for more reasonable representation of the real system. To improve ride comfort and stability performance in the presence of road disturbances, this study proposes two control frameworks including the Proportional-Integral-Derivative (PID) controller and the State Feedback (SF) controller. The focus of the study is to overcome the limitations of existing approaches in handling the actuator saturation in the controller design. To attain a better performance of the two proposed controllers including the input control constraint, a Grey Wolf Optimization (GWO) has been introduced to improve the searching process for the optimal values of the controllers’ adjustable parameters. The simulation results using MATLAB show that the proposed controllers exhibit a good performance in normal operation and in a robustness test involving system parameters’ changes. In terms of improving the response of the system, the GWO-PID controller shows a better response than that of the GWO-SF controller. Based on the Integral Square Error (ISE) index, the ISE is reduced by 16.67% using the GWO-PID controller compared to the GWO-SF controller