Innovative Strategy to Improve Precision and to Save Power of a Real-Time Control Process Using an Online Adaptive Fuzzy Logic Controller

The main objective of this paper is to prove the great advantage that brings our novel approach to the intelligent control area. A set of various types of intelligent controllers have been designed to control the temperature of a room in a real-time control process in order to compare the obtained results with each other. Through a training board that allows us to control the temperature, all the used algorithms should present their best performances in this control process; therefore, our self-organized and online adaptive fuzzy logic controller (FLC) will be required to present great improvements in the control task and a real high control performance. Simulation results can show clearly that the new approach presented and tested in this work is very efficient. Thus, our adaptive and self-organizing FLC presents the best accuracy compared with the remaining used controllers, and, besides that, it can guarantee an important reduction of the power consumption during the control process.

[1]  Rajkumar Roy,et al.  Advances in Soft Computing , 2018, Lecture Notes in Computer Science.

[2]  Timothy J. Gale,et al.  Direct adaptive fuzzy control with a self-structuring algorithm , 2008, Fuzzy Sets Syst..

[3]  Hon-Son Don,et al.  Design of an adaptive self-organizing fuzzy neural network controller for uncertain nonlinear chaotic systems , 2012, Neural Computing and Applications.

[4]  Meng Joo Er,et al.  Self-constructing Fuzzy Neural Networks with Extended Kalman Filter , 2010 .

[5]  Shun-Feng Su,et al.  Fuzzy Sliding Controller Design with Adaptive Approximate Error Feedback , 2009 .

[6]  Woei Wan Tan,et al.  Stable adaptive fuzzy PD plus PI controller for nonlinear uncertain systems , 2011, Fuzzy Sets Syst..

[7]  Ruey-Jing Lian,et al.  Intelligent Controller for Robotic Motion Control , 2011, IEEE Transactions on Industrial Electronics.

[8]  Ebrahim H. Mamdani,et al.  A linguistic self-organizing process controller , 1979, Autom..

[9]  Bore-Kuen Lee,et al.  Adaptive Two-Stage Fuzzy Temperature Control for an Electroheat System , 2009 .

[10]  Shengyuan Xu,et al.  Adaptive Output-Feedback Fuzzy Tracking Control for a Class of Nonlinear Systems , 2011, IEEE Transactions on Fuzzy Systems.

[11]  Kyoung Kwan Ahn,et al.  Velocity control of a secondary controlled closed-loop hydrostatic transmission system using an adaptive fuzzy sliding mode controller , 2013 .

[12]  Chia-Ju Wu,et al.  A Genetic-Based Design of Auto-Tuning Fuzzy PID Controllers , 2009 .

[13]  C. Peng,et al.  On Delay-dependent Robust Stability Criteria for Uncertain T-S Fuzzy Systems with Interval Time-varying Delay , 2011 .

[14]  Gwo-Ruey Yu,et al.  Nonlinear Robust Control of Fuzzy Time-Delay Systems , 2009 .

[15]  C. H. Chiou,et al.  The application of fuzzy control on energy saving for multi-unit room air-conditioners , 2009 .

[16]  Ruey-Jing Lian,et al.  Enhanced Adaptive Self-Organizing Fuzzy Sliding-Mode Controller for Active Suspension Systems , 2013, IEEE Transactions on Industrial Electronics.

[17]  Seongwon Cho,et al.  Self-organizing Fuzzy Controller Based on Fuzzy Neural Network , 2007, Analysis and Design of Intelligent Systems using Soft Computing Techniques.

[18]  Guoying Liu,et al.  Adaptive fuzzy control for unknown nonlinear time-delay systems with virtual control functions , 2011 .

[19]  Chih-Min Lin,et al.  Adaptive Dynamic RBF Fuzzy Neural Controller Design with a Constructive Learning , 2011 .

[20]  E. H. Mamdani,et al.  An Experiment in Linguistic Synthesis with a Fuzzy Logic Controller , 1999, Int. J. Man Mach. Stud..

[21]  Abdullah M. Abusorrah,et al.  Optimal Power Flow Using Adaptive Fuzzy Logic Controllers , 2013 .

[22]  Ruey-Jing Lian Design of an enhanced adaptive self-organizing fuzzy sliding-mode controller for robotic systems , 2012, Expert Syst. Appl..

[23]  R. Talebi-Daryani,et al.  Intelligent control and power management of air conditioning systems using fuzzy logic and local operation networks , 2002, Proceedings of the 5th Biannual World Automation Congress.

[24]  Hicham Chaoui,et al.  Adaptive Fuzzy Logic Control of Permanent Magnet Synchronous Machines With Nonlinear Friction , 2012, IEEE Transactions on Industrial Electronics.

[25]  Teresa Orlowska-Kowalska,et al.  Adaptive Sliding-Mode Neuro-Fuzzy Control of the Two-Mass Induction Motor Drive Without Mechanical Sensors , 2010, IEEE Transactions on Industrial Electronics.

[26]  J. G. Ziegler,et al.  Optimum Settings for Automatic Controllers , 1942, Journal of Fluids Engineering.

[27]  Abdesselem Boulkroune,et al.  Adaptive fuzzy controller for multivariable nonlinear state time-varying delay systems subject to input nonlinearities , 2011, Fuzzy Sets Syst..

[28]  H. P. Jorgl,et al.  An integrated control system for optimizing the energy consumption and user comfort in buildings , 2002, Proceedings. IEEE International Symposium on Computer Aided Control System Design.

[29]  Ying-Wen Bai,et al.  Using an embedded controller with fuzzy logic to reduce power consumption of mobile computers , 2010, IECON 2010 - 36th Annual Conference on IEEE Industrial Electronics Society.

[30]  Hung-Ching Lu,et al.  Adaptive self-constructing fuzzy neural network controller for hardware implementation of an inverted pendulum system , 2011, Appl. Soft Comput..

[31]  Héctor Pomares,et al.  Adaptive fuzzy controller: Application to the control of the temperature of a dynamic room in real time , 2006, Fuzzy Sets Syst..

[32]  Ruey-Jing Lian,et al.  Self-organizing fuzzy control of active suspension systems , 2005, Int. J. Syst. Sci..

[33]  Karim Salahshoor,et al.  A novel real-time fuzzy adaptive auto-tuning scheme for cascade PID controllers , 2011 .

[34]  Meng Joo Er,et al.  Adaptive Fuzzy Control With Guaranteed Convergence of Optimal Approximation Error , 2011, IEEE Transactions on Fuzzy Systems.