Abstract
The aim is to carry out the input–output controllability analysis of a multitude of time delayed and disturbed single-input single-output plants considering all the limitations that may have a direct or indirect influence on the system. In a practical world, it is very important to ensure that all the variables of control systems are realizable. The control error and output must not be too high for a plant to realize. This emphasizes that it is necessary to have some sort of a numerical threshold value for the input–output controllability characteristic value. A fitting controller is designed for each plant model and disturbance, respectively. Larger values for the controllability would indicate higher controllability and all the values less than the threshold value would be treated as uncontrollable. This helps us in making decisions beforehand and predicts the successful control. The reason behind adopting this approach is that the classical control cannot correctly evaluate the bounded systems.
Similar content being viewed by others
References
Skogestad S (1996) A procedure for SISO controllability analysis-with application to design of pH neutralization processes. Comput Chem Eng 20(4):373–386
Li Y, Li J, Ma Z, Feng J-E, Wang H (2019) Controllability and observability of state-dependent switched Boolean control networks with input constraints. Asian J Control 21(6):2662–2673
Xiang L, Wang P, Chen F, Chen G (2019) Controllability of directed networked MIMO systems with heterogeneous dynamics. IEEE Trans Control Netw Syst 7(2):807–817
Moothedath S, Chaporkar P, Belur MN (2019) Approximating constrained minimum cost input–output selection for generic arbitrary pole placement in structured systems. Automatica 107:200–210
Albertos P, Antonio S (2006) Multivariable control systems: an engineering approach. Springer, Berlin
Skogestad S, Postlethwaite I (2005) Multivariable feedback control: analysis and design, 2nd edn. Wiley, West Sussex
Alla A, Kutz JN (2019) Randomized model order reduction. Adv Comput Math 45(3):1251–1271
Besselink B, Tabak U, Lutowska A, van de Wouw N, Nijmeijer H, Rixen D, Hochstenbach M, Schilders W (2013) A comparison of model reduction techniques from structural dynamics, numerical mathematics and systems and control. J Sound Vib 332(19):4403–4422
Commault C, van der Woude J, Frasca P (2019) Functional target controllability of networks: structural properties and efficient algorithms. IEEE Trans Netw Sci Eng 7(3):1521–1530
Guan Y, Wang L (2019) Target controllability of multiagent systems under fixed and switching topologies. Int J Robust Nonlinear Control 29(9):2725–2741
Zheng G, Goury O, Thieffry M, Kruszewski A, Duriez C (2019) Controllability pre-verification of silicone soft robots based on finite-element method. In: 2019 International conference on robotics and automation (ICRA). IEEE, pp 7395–7400
Xia Y, Li R, Yin M, Zou Y (2021) A quantitative measure of the degree of output controllability for output regulation control systems: concept, approach, and applications. J Vib Control. https://doi.org/10.1177/10775463211020185
Smagin A, Dolgov O, Pocebneva I (2020) On the issue of increasing the stability and controllability of aircraft of non-traditional schemes when moving on the ground In: 2020 International Russian automation conference (RusAutoCon). IEEE, pp 920–925
Baggio G, Zampieri S, Scherer CW (2019) Gramian optimization with input-power constraints. In: 2019 IEEE 58th conference on decision and control (CDC). IEEE, pp 5686–5691
Danhane B, Lohéac J, Jungers M (2020) Characterizations of output controllability for LTI systems. https://hal.archives-ouvertes.fr/hal-03083128/
Rugh W (1996) Linear system theory. Prentice-Hall information and system sciences series. Prentice Hall, Hoboken
Wicks MA, DeCarlo RA (1990) Gramian assignment based on the Lyapunov equation. IEEE Trans Autom Control 35:465–468
Antsaklis PJ, Michel AN (2007) Controllability and observability: fundamental results. Birkhäuser Boston, Boston, pp 195–236
Łakomy K, Michałek MM, Adamski W (2019) Scaling of commanded signals in a cascade control system addressing velocity and acceleration limitations of robotic UAVs. In: 21st IFAC Symposium on Automatic Control in Aerospace ACA IFAC-PapersOnLine, vol 52(12). pp 73–78
Saunders C, Shlomo N (2020) A new approach to assess the normalization of differential rates of protest participation. Qual Quant 55:1–24. https://doi.org/10.1007/s11135-020-00995-7
Rabatel G, Marini F, Walczak B, Roger J-M (2020) VSN: Variable sorting for normalization. J Chemom. 34(2):e3164
Franci A, Drion G, Sepulchre R (2019) The sensitivity function of excitable feedback systems. In: 2019 IEEE 58th conference on decision and control (CDC). IEEE, pp 4723–4728
Goodwin GC, Graebe SF, Salgado ME et al (2001) Control system design. Prentice Hall, Upper Saddle River
Saiphet J, Chantasri A, Suwanna S (2019) Effects of time delay in no-knowledge quantum feedback control. J Phys Conf Ser 1380:012113 IOP Publishing
Dihovični D, Ašonja A, Radivojević N, Cvijanovic D, Škrbić S (2020) Stability issues and program support for time delay systems in state over finite time interval. Phys A Stat Mech Its Appl 538:122815
Dahleh M, Dahleh MA, Verghese G (2004) Lectures on dynamic systems and control. A+A 4(100):1–100
Yumuk E, Güzelkaya M, Eksin İ (2019) Analytical fractional PID controller design based on Bode’s ideal transfer function plus time delay. ISA Trans 91:196–206
Chakraborty S, Naskar AK, Ghosh S (2020) Inverse plant model and frequency loop shaping-based PID controller design for processes with time-delay. Int J Autom Control 14(4):399–422
Ulusoy S, Nigdeli SM, Bekdaş G (2021) Novel metaheuristic-based tuning of PID controllers for seismic structures and verification of robustness. J Build Eng 33:101647
Meng Z, Pan J-S, Tseng K-K (2019) PaDE: An enhanced differential evolution algorithm with novel control parameter adaptation schemes for numerical optimization. Knowl Based Syst 168:80–99
Jin QB, Liu Q (2014) IMC-PID design based on model matching approach and closed-loop shaping. ISA Trans 53(2):462–473
Saikumar N, Valério D, HosseinNia SH (2019) Complex order control for improved loop-shaping in precision positioning. In: 2019 IEEE 58th conference on decision and control (CDC). IEEE, pp 7956–7962
Hahn J, Edison T, Edgar TF (2001) A note on stability analysis using Bode plots. Chem Eng Educ 35(3):208–211
Ran M, Wang Q, Dong C, Xie L (2020) Active disturbance rejection control for uncertain time-delay nonlinear systems. Automatica 112:108692
Gubbins KE (1997) Thermodynamics. AIChE J 43(1):285–285 By K. S. Pitzer, 3rd ed., McGraw-Hill, New York
Funding
Not applicable.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Declarations
Not applicable.
Rights and permissions
About this article
Cite this article
Ibrahim, M., Hameed, I. Disturbance rejection and reference tracking of time delayed systems using Gramian controllability. Int. J. Dynam. Control 10, 810–817 (2022). https://doi.org/10.1007/s40435-021-00842-z
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s40435-021-00842-z