OPTIMIZATION OF WATER REQUIREMENT FOR REDUCED DISEASE SUSCEPTIBIITY OF TOMATO (Solanum lycopersicum L.) IN LAFIA, NASARAWA STATE NIGERIA
Keywords:
Disease Incidence, Lafia, Optimization, Tomato Varieties, Water RegimesReferences
Abbasi E, Naghavi N. (2017). Offline Auto-Tuning of a PID Controller Using Extended Classifier System (XCS) Algorithm. Journal of Advances in Computer Engineering and Technology, 3(1), pp. 39-44.
Anbumani, K., Malini, R., Pechinathan G. (2017). GWO based tuning of PID controller for a Heat Exchanger process. IEEE 3rd International Conference on Sensing, Signal Processing and Security (ICSSS), pp. 417-421.
Astrom KJ, Hagglund T. (2001). The future of PID control. Contr Eng Pract; 9(11):1163–75.
Biplab S, Chiranjib K., Subhashis D. (2014). Robust PID controller design using particle swarm optimization-enabled automated quantitative feedback theory approach for a first-order lag system with minimal dead time, Systems Science & Control Engineering: An Open Access Journal, 2(1): 502- 511, DOI: 10.1080/21642583.2014.912570.
Chen PC, Mills JK. (1997). Synthesis of neural networks and PID control for performance improvement of industrial robots. J Intell Robot Syst 20(2–4):157–80.
Cohen GH, Coon GA. (1953) Theoretical consideration of retarded control. Trans ASME 75:827–34.
Cominos P, Munro N. (2002). PID controllers: recent tuning methods and design to specification. IEE Proc-Contr Theory Application 149(1):46–53.
Dos Santos Coelho L, (2007). Tuning of PID controller for an automatic regulator voltage ...,Chaos, Solitons & Fractals, doi:10.1016/j.chaos.2007.06.018
Ho SJ, Shu LS, Ho SY. (2006). Optimizing fuzzy neural networks for tuning PID controllers using an orthogonal simulated annealing algorithm OSA. IEEE Trans Fuzzy Syst; 14(3):421–34.
Ho WK, Hang CC, CaoLS. (1995). Tuning of PID controllers based on gain and phase margin specifications. Automatica 31(3):497–502.
Lam, J. (2004). “Control of an Inverted Pendulum”, University of California, Santa Barbara, 10 June 2004, http://www.ece.ucsb.edu/~roy/student_projects/Johnn
y_Lam_report_238.pdf.
Li HX, Zhang L, Chen G. (2005). An improved robust fuzzy-PID controller with optimal fuzzy reasoning. IEEE Trans Syst, Man, Cybernet – Part B: Cybernet; 35(6):1283–94.
Manoj K and Ashis P. (2014). Tuning PID Controller for Speed Control of DC Motor Using Soft Computing Techniques-A Review. Advance in Electronic and Electric Engineering, Volume 4, Number 2, pp. 141-148.
Muliadi J and Kusumoputro B. (2018). Neural Network Control System of UAV Altitude Dynamics and Its Comparison with the PID Control System. Hindawi Journal of Advanced Transportation Volume 2018, Article ID 3823201, 18 pages. https://doi.org/10.1155/2018/3823201.
Ooi, R. (2003). “Balancing a Two-Wheeled Autonomous Robot”, University of Western Australia, 3 Nov. 2003, http://www.cs.cmu.edu/~mmcnaugh/kdc/as7/2003-
Balance-Ooi.pdf.
Van Overschee, De Moor B. (2000). RAPID: the end of heuristic PID tuning. In: Proceedings of the IFAC workshop on digital control:past, present and future
of PID control, Terrassa, Spain; pp. 595–600.
Vlachos C, Williams D, Gomm JB. (2002). Solution to the Shell standard control problem using genetically tuned PID controllers. Contr. Eng Pract 10(2):151–63.
Wojsznis WK, Blevins TL. (2002). Evaluating PID adaptive techniques for industrial implementation. In: Proceedings of American control conference, Anchorage, AK, USA, p. 1151–5.
Ziegler JG, Nichols NB. (1942). Optimum settings for automatic controllers. Trans ASME; 64:759–68.
Published
How to Cite
Issue
Section
FUDMA Journal of Sciences