Performance Analysis of Enhanced MFO-Based Online-Tuned Split-Range PID Controller

This study presents online-tuning approach using the moth flame optimization (MFO) algorithm to optimize the parameters of PID and modified PID (I-PD) controllers used in the split-range scheme to control the temperature of the mixing process. The performance of these controllers is investigated for the individual temperature setpoints in terms of settling time and compared with performances obtained using offline-tuning approach with the MFO algorithm. The simulation results show a significant improvement with online-tuning approach as compared to offline approach. To further improve the performance, this study proposes modifications in the original MFO algorithm in three phases: by changing the spiral path, by changing the initial population based on the opposition theory, and by a change in the selection of the flames for the updating mechanism. A new version of MFO algorithm is obtained by combining the above-mentioned modifications and used to tune the PID and I-PD controllers in both offline and online modes. Further, the new algorithm is tested for both the controllers with respect to the effect of system dynamics and the effect of process disturbance. The results obtained after validation show that the use of the new version of the MFO algorithm further improves the online tuning of both the controllers. The simulation results also clearly establish the superior performance of the modified PID (I-PD) controller over the PID controller under all conditions.

[1]  Madhav J. Nigam,et al.  A hybrid grey wolf optimizer and artificial bee colony algorithm for enhancing the performance of complex systems , 2018, J. Comput. Sci..

[2]  Kamlesh Mistry,et al.  Intelligent facial emotion recognition using moth-firefly optimization , 2016, Knowl. Based Syst..

[3]  Shahryar Rahnamayan,et al.  Opposition versus randomness in soft computing techniques , 2008, Appl. Soft Comput..

[4]  Rohit Salgotra,et al.  An enhanced moth flame optimization , 2018, Neural Computing and Applications.

[5]  Ali Rıza Yıldız,et al.  Moth-flame optimization algorithm to determine optimal machining parameters in manufacturing processes , 2017 .

[6]  Hui Huang,et al.  Toward an optimal kernel extreme learning machine using a chaotic moth-flame optimization strategy with applications in medical diagnoses , 2017, Neurocomputing.

[7]  Xian Wei,et al.  An Improved Whale Optimization Algorithm Based on Different Searching Paths and Perceptual Disturbance , 2018, Symmetry.

[8]  Kusum Deep,et al.  An Efficient Grey Wolf Optimizer with Opposition-Based Learning and Chaotic Local Search for Integer and Mixed-Integer Optimization Problems , 2019, Arabian Journal for Science and Engineering.

[9]  Yong Deng,et al.  An Improved Moth-Flame Optimization algorithm with hybrid search phase , 2020, Knowl. Based Syst..

[10]  G. M. Tamilselvan,et al.  Online tuning of fuzzy logic controller using Kalman algorithm for conical tank system , 2017 .

[11]  Sheela Tiwari,et al.  Performance Analysis of Moth Flame Optimization-Based Split-Range PID Controller , 2020, MAPAN.

[12]  Aboul Ella Hassanien,et al.  Whale Optimization Algorithm and Moth-Flame Optimization for multilevel thresholding image segmentation , 2017, Expert Syst. Appl..

[13]  Tarek H. M. Abou-El-Enien,et al.  Modified Moth-Flame Optimization Algorithms for Terrorism Prediction , 2016 .

[14]  Malek Alzaqebah,et al.  Neighborhood search methods with Moth Optimization algorithm as a wrapper method for feature selection problems , 2020, International Journal of Electrical and Computer Engineering (IJECE).

[15]  Marcello Colombino,et al.  Online Optimization as a Feedback Controller: Stability and Tracking , 2018, IEEE Transactions on Control of Network Systems.

[16]  Amit Mishra,et al.  Performance Optimization of Cognitive Decision Engine for CR-Based IoTs Using Various Parameter-Less Meta-Heuristic Techniques , 2019, Arabian Journal for Science and Engineering.

[17]  Kusum Deep,et al.  An efficient opposition based Lévy Flight Antlion optimizer for optimization problems , 2018, J. Comput. Sci..

[18]  Asim Kumar Naskar,et al.  I–PD controller for integrating plus time-delay processes , 2017 .

[19]  Anastasios I. Dounis,et al.  Online Tuning of a PID Controller with a Fuzzy Reinforcement Learning MAS for Flow Rate Control of a Desalination Unit , 2019, Electronics.

[20]  K. Deep,et al.  Accelerated Opposition-Based Antlion Optimizer with Application to Order Reduction of Linear Time-Invariant Systems , 2018, Arabian Journal for Science and Engineering.

[21]  Chunquan Li,et al.  Parameters Extraction of Photovoltaic Models Using an Improved Moth-Flame Optimization , 2019, Energies.

[22]  Seyed Mohammad Mirjalili,et al.  Moth-flame optimization algorithm: A novel nature-inspired heuristic paradigm , 2015, Knowl. Based Syst..

[23]  Bing Wang,et al.  A novel artificial bee colony algorithm based on modified search strategy and generalized opposition-based learning , 2015, J. Intell. Fuzzy Syst..

[24]  Ghulam Fareed Laghari,et al.  Modified PID controller for automatic generation control of multi-source interconnected power system using fitness dependent optimizer algorithm , 2020, PloS one.

[25]  Thomas A. Fuhlbrigge,et al.  Controller parameter optimization for complex industrial system with uncertainties , 2019, Measurement and Control.

[26]  M.M.A. Salama,et al.  Opposition-Based Differential Evolution , 2008, IEEE Transactions on Evolutionary Computation.

[27]  Luis Govinda García-Valdovinos,et al.  Neural Network-Based Self-Tuning PID Control for Underwater Vehicles , 2016, Sensors.

[28]  David J. Allstot.  Dear CAS Member's [President's Message] , 2009 .

[29]  Cheng Shao,et al.  An Optimal Approach to Online Tuning Method for PID Type Iterative Learning Control , 2020 .

[30]  Banaja Mohanty,et al.  Performance analysis of moth flame optimization algorithm for AGC system , 2019 .

[31]  Bijaya K. Panigrahi,et al.  Solution to unit commitment in power system operation planning using binary coded modified moth flame optimization algorithm (BMMFOA): A flame selection based computational technique , 2017, J. Comput. Sci..

[32]  Sabry F. Saraya,et al.  Applying hybrid genetic–PSO technique for tuning an adaptive PID controller used in a chemical process , 2020, Soft Comput..

[33]  Dalia Yousri,et al.  Identifying the Parameters of Cole Impedance Model Using Magnitude Only and Complex Impedance Measurements: A Metaheuristic Optimization Approach , 2020, Arabian Journal for Science and Engineering.

[34]  M. Davanipour,et al.  Chaotic Self-Tuning PID Controller Based on Fuzzy Wavelet Neural Network Model , 2018, Iranian Journal of Science and Technology, Transactions of Electrical Engineering.

[35]  Akash Saxena,et al.  An opposition theory enabled moth flame optimizer for strategic bidding in uniform spot energy market , 2019, Engineering Science and Technology, an International Journal.

[36]  Soheyl Khalilpourazari,et al.  An efficient hybrid algorithm based on Water Cycle and Moth-Flame Optimization algorithms for solving numerical and constrained engineering optimization problems , 2017, Soft Computing.

[37]  L. Fortuna,et al.  Microfluidic circuits and systems , 2009, IEEE Circuits and Systems Magazine.

[38]  Hamdan Daniyal,et al.  Optimal reactive power dispatch solution by loss minimization using moth-flame optimization technique , 2017, Appl. Soft Comput..

[39]  Hao Gao,et al.  Parameter Optimization on FNN/PID Compound Controller for a Three-Axis Inertially Stabilized Platform for Aerial Remote Sensing Applications , 2019, J. Sensors.

[40]  Chunquan Li,et al.  A Double Evolutionary Learning Moth-Flame Optimization for Real-Parameter Global Optimization Problems , 2018, IEEE Access.

[41]  B. Yalçin,et al.  Three-axis gap clearance I-PD controller design based on coefficient diagram method for 4-pole hybrid electromagnet , 2017 .

[42]  A. J. McDaid,et al.  Control of IPMC Actuators for Microfluidics With Adaptive “Online” Iterative Feedback Tuning , 2012, IEEE/ASME Transactions on Mechatronics.

[43]  S. Mini,et al.  Opposition-based moth flame optimization with Cauchy mutation and evolutionary boundary constraint handling for global optimization , 2018, Soft Comput..