Data-Driven Neuroendocrine-PID Tuning Based on Safe Experimentation Dynamics for Control of TITO Coupled Tank System with Stochastic Input Delay
This paper addresses a data-driven neuroendocrine-PID tuning for control a two-input-two-output (TITO) coupled tank system with stochastic input time delay based on safe experimentation dynamics (SED). The SED algorithm is an optimization method used as data-driven tools to find the optimal control parameters by using the input-output (I/O) data measurement in an actual system. The advantages of the SED algorithm are that provides a fast solution, able to solve the high dimensional problem and provides high-performance accuracy by keeping the best parameter value while finding the control parameters. Moreover, the gain sequences of the SED algorithm is independent of the number of iterations by fixed the interval size in finding the optimal solution. Hence, this allows the SED method to have enough strength to re-tune in the attempted of finding the new optimal solution when the delay occurs during the tuning process. Apart from that, a neuroendocrine-PID controller structure is chosen due to its provide effective and accurate control performances by a combination of PID and neuroendocrine structures. On another note, the neuroendocrine structure is a biologically inspired designed that derived from general secretion rules of the hormone in the human body. In order to evaluate the performances of the data-driven neuroendocrine-PID control based on SED, it is applied to a numerical example of TITO coupled tank plant and the control performance tracking and the computational time are observed. The simulation results show that the data-driven neuroendocrine-PID control based on SED capable to track the desired value of liquid tanks level although the stochastic input delay occurred in the system. In addition, the SED based method also attained good control performance without any theoretical assumptions about plant modelling.
KeywordsTITO Neuroendocrine-PID Coupled tank SED Stochastic delay
This study was partly supported by the Ministry of Higher Education, Government of Malaysia through the Fundamental Research Grant Scheme (FRGS) (RDU 160146) and Universiti Malaysia Pahang.
- 1.Ramli, M.S., Ahmad, M.A., Ismail, R.M.T.R.: Comparison of swarm adaptive neural network control of a coupled tank liquid level system. In: 2009 International Conference on Computer Technology and Development (ICCTD 2009), vol. 1, pp. 130–135 (2009)Google Scholar
- 2.Numsomran, A., Suksri, T., Thumma, M.: Design of 2-DOF PI controller with decoupling for coupled-tank process. In: International Conference on Control, Automation and System (ICCAS 2007), vol. 2, no. I, pp. 339–344 (2007)Google Scholar
- 3.Numsomran, A., Kangwanrat, S., Tipsuwannaporn, V.: Design of PI controller using MRAC techniques for coupled-tanks process. World Acad. Sci. Eng. Technol. 59, 67–72 (2009)Google Scholar
- 4.Gupta, A., Goindi, S., Singh, G., Kumar, R.: Optimal design of PID controllers for time delay systems using genetic algorithm and simulated annealing. In: International Conference on Innovative Mechanism for Industry Applications (ICIMIA 2017), pp. 66–69 (2017)Google Scholar
- 6.Mingmei, W., Qiming, C., Yinman, C., Yingfei, W.: Model-free adaptive control method for nuclear steam generator water level. In: 2010 International Conference on Intelligent Computation Technology and Automation, pp. 696–699 (2010)Google Scholar
- 10.Aksu, I. O., Coban, R.: Second order sliding mode control of MIMO nonlinear coupled tank system. In: 2018 14th International Conference on Advanced Trends Radioelecrtronics, Telecommunications and Computer Engineering, pp. 826–830 (2018)Google Scholar
- 11.Puralachetty, M.M., Pamula, V.K.: Differential evolution and particle swarm optimization algorithms with two stage initialization for PID controller tuning in coupled tank liquid level system. In: 2016 International Conference on Advanced Robotics and Mechatronics Differentiation, pp. 507–511 (2016)Google Scholar
- 12.Shukor, N.S.A., Ahmad, M.A.: Data-Driven PID Tuning Based on Safe Experimentation Dynamics for Control of Double-Pendulum-Type Overhead Crane. In: Hassan, M. (ed.) Intelligent Manufacturing & Mechatronics. LNME, pp. 295–308. Springer, Singapore (2018). https://doi.org/10.1007/978-981-10-8788-2_27CrossRefGoogle Scholar
- 13.Ghazali, M.R., Ahmad, M.A., Falfazli, M., Jusof, M., Ismail, R.M.T. R.: A data-driven Neuroendocrine-PID controller for underactuated systems based on safe experimentation dynamics. In: 2018 IEEE 14th International Colloquium on Signal Processing and its Applications (CSPA 2018), March, pp. 9–10 (2018)Google Scholar