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Abstract
This paper proposes an optimal tuning of fractional order proportional integral derivative (FOPID) controller for higher order process using hybrid approach. The proposed hybrid approach is the joint execution of Dynamic Differential Annealed Optimization (DDAO) and Feedback Artificial tree (FAT) algorithm, hence it is named D2AOFAT approach. The FOPID controller parameters likekp,ki,kd, λ andμ. The FOPID controller parameters errors are minimized and predict the optimal parameters by the FAT algorithm. Based on FOPID controller parameters using FAT algorithm, the DDAO optimizes the FOPID controller parameters. The FOPID controller advantage is adjusted to accomplish that needed responses that are resolute with FAT theory and RDF parameters are predictable using DDAO technique. The purpose of the proposed control system is selected in light of the achieved parameters of time delay system (TDS). The proposed technique is carried out in MATLAB / Simulink, its performance is compared to the existing techniques, like Ziegler-Nichols fit, Curve Fit, Wang method, and IWLQR method.
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Abbreviations
- RDFA:
Random Decision Forest Algorithm.
- ACFO:
Advanced Cuttlefish Optimizer.
- ACORDF:
Advanced Cuttlefish Optimizer and Random Decision Forest.
- ZN:
Ziegler-Nichols.
- CF:
Curve Fitting.
- FOPI:
Fractional Order Proportional Integral.
- NMSS:
Non-minimal State Space.
- PFC:
Predictive Functional Control.
- FC:
Fractional-Order.
- CFA:
Cuttlefish algorithm.
- FAT:
Feedback Artificial tree.
- OOB:
Out of Bag.
- NMSS-FOPFC:
Non-minimal state space Predictive functional control Fractional-order.
- PSO:
Particle Swarm Optimization.
- FOPID:
Fractional order proportional-integrator-derivative.
- IAE:
Integral absolute error.
- ITAE:
Integral time absolute error.
- ITSE:
Integral time squared error.
- SMC:
Sliding mode control.
- PV:
Process variable.
- RMSE:
Root mean square error.
- DDAO:
Dynamic Differential Annealed Optimization.
References
Alamdar Ravari M, Yaghoobi M (2019) Optimum design of fractional order pid controller using chaotic firefly algorithms for a control CSTR system. Asian J Control 21(5):2245–2255
Azar AT, Serrano FE. (2018) Fractional order sliding mode PID controller/observer for continuous nonlinear switched systems with PSO parameter tuning. In International conference on advanced machine learning technologies and applications, 22:13–22
Mythili S, Thiyagarajah K, Rajesh P, Shajin FH (2020) Ideal position and size selection of unified power flow controllers (UPFCs) to upgrade the dynamic stability of systems: an antlion optimiser and invasive weed optimisation algorithm. HKIE Trans 27(1):25–37
Liu L, Zhang S, Xue D, Chen Y (2018) General robustness analysis and robust fractional-order PD controller design for fractional-order plants. IET Control Theory Appl 12:1730–1736
Rajesh P, Shajin F. (2020) A Multi-Objective Hybrid Algorithm for Planning Electrical Distribution System. 22(4–5):224–509
Bhimte R, Bhole K, Shah P. (2018) Fractional Order Fuzzy PID Controller for a Rotary Servo System. 2018 2nd International Conference on Trends in Electronics and Informatics 538–542
MohammadiAsl R, Pourabdollah E, SalmaniM. (2017) Optimal fractional order PID for a robotic manipulator using colliding bodies design. Soft Comput 22:4647–4659
Shajin FH, Rajesh P (2020). Trusted secure geographic routing protocol: outsider attack detection in mobile ad hoc networks by adopting trusted secure geographic routing protocol. Int J Pervasive Comp Commun
Chen K, Tang R, Li C, Lu J (2018) Fractional order PI λ controller synthesis for steam turbine speed governing systems. ISA Trans 77:49–57
Thota MK, Shajin FH, Rajesh P (2020) Survey on software defect prediction techniques. Int J Appl Sci Eng 17(4):331–344
Sumathi R, Umasankar P (2018) Optimal design of fractional order PID controller for time-delay systems: an IWLQR technique. Int J Gen Syst 47:714–730
Rahul D, Anwar M. (2018) Design of Fractional Order Pi Controller for First Order Plus Dead Time Process Based on Maximum Sensitivity. 2018 2nd International Conference on Power, Energy and Environment: Towards Smart Technology 1–6
Dabiri A, Moghaddam B, Machado J (2018) Optimal variable-order fractional PID controllers for dynamical systems. J Comput Appl Math 339:40–48
Kommula BN, Kota VR. (2016) Mathematical modeling and fuzzy logic control of a brushless DC motor employed in automobile and industrial applications. In2016 IEEE First International Conference on Control, measurement and Instrumentation (CMI), 1–5. IEEE
Kumar A, Kumar V (2018) Performance analysis of optimal hybrid novel interval type-2 fractional order fuzzy logic controllers for fractional order systems. Expert Syst Appl 93:435–455
Li D, Liu L, Jin Q, Hirasawa K (2015) Maximum sensitivity based fractional IMC–PID controller design for non-integer order system with time delay. J Process Control 31:17–29
Liu L, Pan F, Xue D (2015) Variable-order fuzzy fractional PID controller. ISA Trans 55:227–233
Leena J, Sundaravadivu K, Monisha R, Rajinikanth V. (2018) Design of Fractional-Order PI/PID Controller for SISO System Using Social-Group-Optimization. 2018 IEEE International Conference on System, Computation, Automation and Networking 1–5
Zeng G, Chen J, Dai Y, Li L, Zheng C, Chen M (2015) Design of fractional order PID controller for automatic regulator voltage system based on multi-objective extremal optimization. Neurocomputing 160:173–184
Senberber H, Bagis A (2017) Fractional PID controller design for fractional order systems using ABC algorithm. Electronics 2017:1–7
Li M, Zhou P, Zhao Z, Zhang J (2016) Two-degree-of-freedom fractional order-PID controllers design for fractional order processes with dead-time. ISA Trans 61:147–154
Swain S, Sain S, Mishra S, Ghosh S (2017) Real time implementation of fractional order PID controllers for a magnetic levitation plant. AEU Int J Electron Commun 78:141–156
Zamani A, Tavakoli S, Etedali S (2017) Fractional order PID control design for semi-active control of smart base-isolated structures: a multi-objective cuckoo search approach. ISA Trans 67:222–232
Mandic P, Sekara T, Lazarevic M, Boskovic M (2017) Dominant pole placement with fractional order PID controllers: D-decomposition approach. ISA Trans 67:76–86
Moattari M, Moradi MH (2020) Conflict monitoring optimization heuristic inspired by brain fear and conflict systems. Int J Artif Intell 18(1):45–62
Bingul Z, Karahan O (2018) Comparison of PID and FOPID controllers tuned by PSO and ABC algorithms for unstable and integrating systems with time delay. Optim Control Appl Methods 39:1431–1450
Yumuk E, Güzelkaya M, Eksin I. (2019) Analytical fractional PID controller design based on Bode’s ideal transfer function plus time delay. ISA Transactions
Şenol B, Demiroğlu U (2019) Frequency frame approach on loop shaping of first order plus time delay systems using fractional order PI controller. ISA Trans 86:192–200
Sanatizadeh M, Bigdeli N. (2019) The design of NMSS fractional-order predictive functional controller for unstable systems with time delay. ISA Transactions
Pradhan R, Pradhan S, Pati B (2018) Design and performance evaluation of fractional order PID controller for heat flow system using particle swarm optimization. Adv Intell Syst Comp 711:261–271
Chevalier A, Francis C, Copot C, Ionescu C, De Keyser R (2019) Fractional-order PID design: towards transition from state-of-art to state-of-use. ISA Trans 84:178–186
Choudhary N, Sivaramakrishnan J, Kar I. (2018) Sliding mode control of uncertain fractional order systems with delay. Int J Control 1–10
Gao Z (2019) Analytical method on stabilisation of fractional-order plants with interval uncertainties using fractional-order PIλDμ controllers. Int J Syst Sci 50:935–953
Baviskar SM, Shah P, Agashe SD (2014) Tuning of fractional PID controllers for higher order systems. Int J Appl Eng Res 9(11):1581–1590
Das S, Pan I, Das S (2013) Performance comparison of optimal fractional order hybrid fuzzy PID controllers for handling oscillatory fractional order processes with dead time. ISA Trans 52(4):550–566
Chen SM, Han WH (2019) Multi attribute decision making based on nonlinear programming methodology, particle swarm optimization techniques and interval-valued intuitionistic fuzzy values. Inf Sci 471:252–268
Precup, R. E, David, R. C, Roman, R. C, Szedlak-Stinean, A. I, Petriu, E. M. (2021). Optimal tuning of interval type-2 fuzzy controllers for nonlinear servo systems using Slime Mould Algorithm. Int J Syst Sci, 1–16
Li QQ, He ZC, Li E. (2020) The feedback artificial tree (FAT) algorithm. Soft Comp. 1–28
Ghafil HN, Jármai K (2020) Dynamic differential annealed optimization: new metaheuristic optimization algorithm for engineering applications. Appl Soft Comput 93:106392
Bettayeb M, Mansouri R, Al-Saggaf U, Mehedi I (2016) Smith predictor based fractional-order-filter PID controllers Design for Long Time Delay Systems. Asian J Contro l19:587–598
Pan I, Das S (2015) Fractional-order load-frequency control of interconnected power systems using chaotic multi-objective optimization. Appl Soft Comput 29:328–344
PanI DS, Gupta A (2011) Tuning of an optimal fuzzy PID controller with stochastic algorithms for networked control systems with random time delay. ISATransactions 50:28–36
Pradhan R, Pati B (2019) Comparative performance evaluation of fractional order PID controller for heat flow system using evolutionary algorithms. Int J Appl Metaheuristic Comp 10:68–90
Sumathi R, Umasankar P (2020) New opposition cuttlefish optimizer based two-step approach for optimal design of fractional order proportional integral derivative controller for time delay systems. Int J Numer Model: Elect Netw Devices Fields 33(2):e2708
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Department of Applied Electronics and Instrumentation, Mount Zion College of Engineering, Kadammanitta, Kerala, India
Thomas George
Department of Electrical and Telecommunication Engineering, Sathyabama University, Chennai, India
V. Ganesan
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George, T., Ganesan, V. Optimal tuning of FOPID controller for higher order process using hybrid approach.Appl Intell52, 15345–15367 (2022). https://doi.org/10.1007/s10489-022-03167-2
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