INTELLIGENT CONTROLLERS FOR VELOCITY TRACKING OF TWO WHEELED INVERTED PENDULUM MOBILE ROBOT

2016 ◽  
Vol 78 (6-11) ◽  
Author(s):  
Amir A. Bature ◽  
Salinda Buyamin ◽  
Mohamed N. Ahmad ◽  
Mustapha Muhammad ◽  
Auwalu M. Abdullahi
Keyword(s):  
Mobile Robot ◽  
Inverted Pendulum ◽  
Fuzzy Inference ◽  
Open Loop ◽  
Inference System ◽  
Wheeled Inverted Pendulum ◽  
Neuro Fuzzy ◽  
Soft Computing Techniques ◽  
Model Control ◽  
Velocity Tracking

Velocity tracking is one of the important objectives of vehicle, machines and mobile robots. A two wheeled inverted pendulum (TWIP) is a class of mobile robot that is open loop unstable with high nonlinearities which makes it difficult to control its velocity because of its nature of pitch falling if left unattended. In this work, three soft computing techniques were proposed to track a desired velocity of the TWIP. Fuzzy Logic Control (FLC), Neural Network Inverse Model control (NN) and an Adaptive Neuro-Fuzzy Inference System (ANFIS) were designed and simulated on the TWIP model. All the three controllers have shown practically good performance in tracking the desired speed and keeping the robot in upright position and ANFIS has shown slightly better performance than FLC, while NN consumes more energy.  

scholarly journals Velocity control of a two-wheeled inverted pendulum mobile robot: a fuzzy model-based approach

2019 ◽  
Vol 8 (3) ◽  
pp. 808-817
Author(s):  
Mustapha Muhammad ◽  
Amir A. Bature ◽  
Umar Zangina ◽  
Salinda Buyamin ◽  
Anita Ahmad ◽  
...  
Keyword(s):  
Lyapunov Function ◽  
Mobile Robot ◽  
Inverted Pendulum ◽  
Fuzzy Model ◽  
Control Law ◽  
Velocity Control ◽  
Wheeled Inverted Pendulum ◽  
Fuzzy Lyapunov Function ◽  
Tracking Controller ◽  
Velocity Tracking

This paper presents the design of a fuzzy tracking controller for balancing and velocity control of a Two-Wheeled Inverted Pendulum (TWIP) mobile robot based on its Takagi-Sugino (T-S) fuzzy model, fuzzy Lyapunov function and non-parallel distributed compensation (non-PDC) control law. The T-S fuzzy model of the TWIP mobile robot was developed from its nonlinear dynamical equations of motion. Stabilization conditions in a form of linear matrix inequalities (LMIs) were derived based on the T-S fuzzy model of the TWIP mobile robot, a fuzzy Lyapunov function and a non-PDC control law. Based on the derived stabilization conditions and the T-S fuzzy model of the TWIP mobile robot, a state feedback velocity tracking controller was then proposed for the TWIP mobile robot. The balancing and velocity tracking performance of the proposed controller was investigated via simulations. The simulation result shows the effectiveness of the proposed control scheme.

scholarly journals Detection of waterborne bacteria using Adaptive Neuro-Fuzzy Inference System

2020 ◽  
Vol 158 ◽  
pp. 05002
Author(s):  
Farhan Mohammad Khan ◽  
Smriti Sridhar ◽  
Rajiv Gupta
Keyword(s):  
Water Quality ◽  
Membership Function ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Quality Analysis ◽  
Hybrid Technique ◽  
Membership Functions ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Soft Computing Techniques

The detection of waterborne bacteria is crucial to prevent health risks. Current research uses soft computing techniques based on Artificial Neural Networks (ANN) for the detection of bacterial pollution in water. The limitation of only relying on sensor-based water quality analysis for detection can be prone to human errors. Hence, there is a need to automate the process of real-time bacterial monitoring for minimizing the error, as mentioned above. To address this issue, we implement an automated process of water-borne bacterial detection using a hybrid technique called Adaptive Neuro-fuzzy Inference System (ANFIS), that integrates the advantage of learning in an ANN and a set of fuzzy if-then rules with appropriate membership functions. The experimental data as the input to the ANFIS model is obtained from the open-sourced dataset of government of India data platform, having 1992 experimental laboratory results from the years 2003-2014. We have included the following water quality parameters: Temperature, Dissolved Oxygen (DO), pH, Electrical conductivity, Biochemical oxygen demand (BOD) as the significant factors in the detection and existence of bacteria. The membership function changes automatically with every iteration during training of the system. The goal of the study is to compare the results obtained from the three membership functions of ANFIS- Triangle, Trapezoidal, and Bell-shaped with 35 = 243 fuzzy set rules. The results show that ANFIS with generalized bell-shaped membership function is best with its average error 0.00619 at epoch 100.

scholarly journals Modeling Surface Water Quality Using the Adaptive Neuro-Fuzzy Inference System Aided by Input Optimization

2021 ◽  
Vol 13 (8) ◽  
pp. 4576
Author(s):  
Muhammad Izhar Shah ◽  
Taher Abunama ◽  
Muhammad Faisal Javed ◽  
Faizal Bux ◽  
Ali Aldrees ◽  
...  
Keyword(s):  
Water Quality ◽  
Surface Water ◽  
Predictive Models ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Surface Water Quality ◽  
Inference System ◽  
Input Optimization ◽  
Neuro Fuzzy ◽  
Soft Computing Techniques

Modeling surface water quality using soft computing techniques is essential for the effective management of scarce water resources and environmental protection. The development of accurate predictive models with significant input parameters and inconsistent datasets is still a challenge. Therefore, further research is needed to improve the performance of the predictive models. This study presents a methodology for dataset pre-processing and input optimization for reducing the modeling complexity. The objective of this study was achieved by employing a two-sided detection approach for outlier removal and an exhaustive search method for selecting essential modeling inputs. Thereafter, the adaptive neuro-fuzzy inference system (ANFIS) was applied for modeling electrical conductivity (EC) and total dissolved solids (TDS) in the upper Indus River. A larger dataset of a 30-year historical period, measured monthly, was utilized in the modeling process. The prediction capacity of the developed models was estimated by statistical assessment indicators. Moreover, the 10-fold cross-validation method was carried out to address the modeling overfitting issue. The results of the input optimization indicate that Ca2+, Na+, and Cl− are the most relevant inputs to be used for EC. Meanwhile, Mg2+, HCO3−, and SO42− were selected to model TDS levels. The optimum ANFIS models for the EC and TDS data showed R values of 0.91 and 0.92, and the root mean squared error (RMSE) results of 30.6 µS/cm and 16.7 ppm, respectively. The optimum ANFIS structure comprises a hybrid training algorithm with 27 fuzzy rules of triangular fuzzy membership functions for EC and a Gaussian curve for TDS modeling, respectively. Evidently, the outcome of the present study reveals that the ANFIS modeling, aided with data pre-processing and input optimization, is a suitable technique for simulating the quality of surface water. It could be an effective approach in minimizing modeling complexity and elaborating proper management and mitigation measures.

scholarly journals Comparative Analysis of Single and Hybrid Neuro-Fuzzy-Based Models for an Industrial Heating Ventilation and Air Conditioning Control System

2020 ◽  
Author(s):  
Sina Ardabili ◽  
Bertalan Beszedes ◽  
Laszlo Nadai ◽  
Karoly Szell ◽  
Amir Mosavi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Fuzzy Inference System ◽  
Air Conditioning ◽  
High Performance ◽  
Prediction Models ◽  
Fuzzy Inference ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Soft Computing Techniques ◽  
Save Energy

The hybridization of machine learning methods with soft computing techniques is an essential approach to improve the performance of the prediction models. Hybrid machine learning models, particularly, have gained popularity in the advancement of the high-performance control systems. Higher accuracy and better performance for prediction models of exergy destruction and energy consumption used in the control circuit of heating, ventilation, and air conditioning (HVAC) systems can be highly economical in the industrial scale to save energy. This research proposes two hybrid models of adaptive neuro-fuzzy inference system-particle swarm optimization (ANFIS-PSO), and adaptive neuro-fuzzy inference system-genetic algorithm (ANFIS-GA) for HVAC. The results are further compared with the single ANFIS model. The ANFIS-PSO model with the RMSE of 0.0065, MAE of 0.0028, and R2 equal to 0.9999, with a minimum deviation of 0.0691 (KJ/s), outperforms the ANFIS-GA and single ANFIS models.

Design of a Hybrid Adaptive Neuro Fuzzy Inference System (ANFIS) Controller for Position and Angle Control of Inverted Pendulum (IP) Systems

Fuzzy Systems ◽  
2017 ◽  
pp. 308-320
Author(s):  
Ashwani Kharola
Keyword(s):  
Fuzzy Logic ◽  
Inverted Pendulum ◽  
Fuzzy Logic Controller ◽  
Fuzzy Inference ◽  
Fuzzy Controller ◽  
Data Sets ◽  
Inference System ◽  
Simulink Model ◽  
Neuro Fuzzy ◽  
Proposed Model

This paper illustrates a comparison study of Fuzzy and ANFIS Controller for Inverted Pendulum systems. IP belongs to a class of highly non-linear, unstable and multi-variable systems which act as a testing bed for many complex systems. Initially, a Matlab-Simulink model of IP system was proposed. Secondly, a Fuzzy logic controller was designed using Mamdani inference system for control of proposed model. The data sets from fuzzy controller was used for development of a Hybrid Sugeno ANFIS controller. The results shows that ANFIS controller provides better results in terms of Performance parameters including Settling time(sec), maximum overshoot(degree) and steady state error.

Neuro-Fuzzy-Based Control for Parallel Cascade Control

2013 ◽  
Vol 8 (1) ◽  
pp. 15-25 ◽  
Author(s):  
R. Karthikeyan ◽  
K. Manickavasagam ◽  
Shikha Tripathi ◽  
K.V.V. Murthy
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Internal Model Control ◽  
Cascade Control ◽  
Regulatory Control ◽  
Inference System ◽  
Control Approach ◽  
Neuro Fuzzy ◽  
Model Control

Abstract This paper discusses the application of adaptive neuro-fuzzy inference system (ANFIS) control for a parallel cascade control system. Parallel cascade controllers have two controllers, primary and secondary controllers in cascade. In this paper the primary controller is designed based on neuro-fuzzy approach. The main idea of fuzzy controller is to imitate human reasoning process to control ill-defined and hard to model plants. But there is a lack of systematic methodology in designing fuzzy controllers. The neural network has powerful abilities for learning, optimization and adaptation. A combination of neural networks and fuzzy logic offers the possibility of solving tuning problems and design difficulties of fuzzy logic. Due to their complementary advantages, these two models are integrated together to form more robust learning systems, referred to as adaptive neuro-fuzzy inference system (ANFIS). The secondary controller is designed using the internal model control approach. The performance of the proposed ANFIS-based control is evaluated using different case studies and the simulated results reveal that the ANFIS control approach gives improved servo and regulatory control performances compared to the conventional proportional integral derivative controller.

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