Observer-based adaptive emotional controller for a class of uncertain nonlinear systems

Document Type : Power Article

Author

Department of Electrical and Computer Engineering/Semnan University/Semnan/Iran

Abstract

Uncertainties and complexities of the actual control problems, such as unknown dynamics, unmeasurable states, external disturbances, and measurement noise, require powerful control structures capable of handling such complexities. Emotional controllers offer fast system response while also carrying a simple structure. However, the emotional controllers to date have not been evaluated rigorously. Here, the continuous radial basis emotional neural network (CRBENN) is employed to approximate the unknown dynamics in observer-based adaptive control structures for uncertain affine nonlinear systems. The system dynamics are unknown. Also, external disturbance and measurement noise affect system performance. Compared to the previous emotional controllers, the system states are not measurable and are estimated using a state estimator. The H∞ tracking performance is verified using Lyapunov stability theory, and suitable adaptive laws are designed for the weights of the proposed emotional networks that are consistent with the basic brain emotional learning model. Results indicate that the proposed controllers reach a lower tracking error with similar control energy consumption compared to another neuro-controller.

Keywords

Main Subjects

References

]1[ محمد مهدی ذبیحی شش پلی، مهدی علیاری شوره دلی و علی معرفیان پور، " تحلیل پایداری لیاپانوف در آموزش سیستم فازی- عصبی نوع 2 با یک الگوریتم ترکیبی مبتنی بر گرادیان نزولی و فیلتر کالمن"، نشریه مدل‌سازی در مهندسی، دوره 20، شماره 68، فروردین 1401، صفحه 85- 100.
]2[ مجتبی رادمهر و حسن زرآبادی پور، " کنترل مد لغزشی فازی برای ردیابی پروفایل بهینه سرعت قطار با وجود نامعینی"، نشریه مدل‌سازی در مهندسی، دوره 20، شماره 68، فروردین 1401، صفحه 139-152.
[3] J. Moren and C. Balkenius, “A Computational Model of Emotional Learning in the Amygdala”, From Anim. to Animat. From animals to animats 6, 2000, pp. 115-124.
[4] J. Moren, Emotion and Learning- A Computational Model of the Amygdala, Lund University, Lund, Sweden., 2002.
[5] E. Lotfi and M.-R. Akbarzadeh-T, “Practical Emotional Neural Networks”, Neural networks, Vol. 59, 2014, pp. 61–72.
[6] E. Lotfi and M.-R. Akbarzadeh-T, “A Winner-Take-All Approach to Emotional Neural Networks with Universal Approximation Property”, Inf. Sci., Vol. 346, 2016, pp. 369–388.
]7[ مهدی گلشن، محمد تشنه لب و آرش شریفی، " توسعه ماشین یادگیری هیجانی مغز با الهام از ماشین یادگیر مفروط ترتیبی آنلاین حافظه‌دار بازگشتی مبتنی بر شبکه‌های عصبی"، نشریه مدل‌سازی در مهندسی، دوره 20، شماره 70، مهر 1401، صفحه 1-21.
[8] C. Lucas, D. Shahmirzadi, and N. Sheikholeslami, “Introducing BELBIC: Brain Emotional Learning Based Intelligent Controller”, Intell. Autom. Soft Comput., Vol. 10, No. 1, 2004, pp. 11–21.
[9] M. R. Khalghani, M. H. Khooban, E. Mahboubi-Moghaddam, N. Vafamand, and M. Goodarzi, “A Self-Tuning Load Frequency Control Strategy for Microgrids: Human Brain Emotional Learning”, Int. J. Electr. Power Energy Syst., Vol. 75, 2016, pp. 311–319.
[10] A. Sadeghieh, H. Sazgar, K. Goodarzi, and C. Lucas, “Identification and Real-Time Position Control of a Servo-Hydraulic Rotary Actuator by Means of a Neurobiologically Motivated Algorithm”, ISA Trans., Vol. 51, No. 1, 2012, pp. 208–219.
[11] F. Baghbani, M.-R. Akbarzadeh-T, and M.-B. N. Sistani, “Stable Robust Adaptive Radial Basis Emotional Neurocontrol for a Class of Uncertain Nonlinear Systems,” Neurocomputing, vol. 309, 2018, pp. 11–26.
[12] F. Baghbani, M.-R. Akbarzadeh-T, M.-B. Naghibi-Sistani, and A. Akbarzadeh, “Emotional Neural Networks with Universal Approximation Property for Stable Direct Adaptive Nonlinear Control Systems”, Eng. Appl. Artif. Intell., Vol. 89, 2020, p. 103447.
[13] T. L. Le, C. M. Lin, and T. T. Huynh, “Self-Evolving Type-2 Fuzzy Brain Emotional Learning Control Design for Chaotic Systems Using PSO”, Appl. Soft Comput. J., Vol. 73, 2018, pp. 418–433.
[14] Q. Wu et al., “Self-Organizing Brain Emotional Learning Controller Network for Intelligent Control System of Mobile Robots”, IEEE Access, Vol. 6, 2018, pp. 59096–59108.
[15] W. Fang, F. Chao, C. M. Lin, L. Yang, C. Shang, and C. Zhou, “An Improved Fuzzy Brain Emotional Learning Model Network Controller for Humanoid Robots”, Front. Neurorobot., Vol. 13, 2019, pp. 1–16.
[16] S. Khorashadizadeh, S. M. Hashem Zadeh, M. R. Koohestani, S. Shekofteh, and S. Erkaya, “Robust Model-Free Control of a Class of Uncertain Nonlinear Systems Using BELBIC: Stability Analysis and Experimental Validation”, J. Brazilian Soc. Mech. Sci. Eng., Vol. 41, No. 8, 2019, pp. 1–12.
[17] A. Naderi Akhormeh, J. Roshanian, H. MoradiMaryamnegari, and A. M. Khoshnood, “Online and Stable Parameter Estimation Based on Normalized Brain Emotional Learning Model (NBELM)”, Int. J. Adapt. Control Signal Process., Vol. 33, No. 7, 2019, pp. 1047–1065.
[18] F. Baghbani, M. R. Akbarzadeh-T, and M. B. Naghibi Sistani, “Cooperative Adaptive Emotional Neuro-Control for a Class of Higher-Ordered Heterogeneous Uncertain Nonlinear Multi-Agent Systems”, Neurocomputing, Vol. 447, 2021, pp. 196–212.
[19] P. Parsa, M. R. Akbarzadeh-T, and F. Baghbani, “Command-Filtered Backstepping Robust Adaptive Emotional Control of Strict-Feedback Nonlinear Systems with Mismatched Uncertainties,” Inf. Sci., vol. 579, 2021, pp. 434–453, doi: 10.1016/j.ins.2021.07.090.
[20] H. Mirhajianmoghadam and M. R. Akbarzadeh-T., “Predictive Hierarchical Harmonic Emotional Neuro-Cognitive Control of Nonlinear Systems”, Eng. Appl. Artif. Intell., Vol. 111, 2022, p. 104781.
[21] I. R. Scola, L. R. G. Carrillo, and J. P. Hespanha, “Limbic System-Inspired Performance-Guaranteed Control for Nonlinear Multi-Agent Systems with Uncertainties”, IEEE Trans. Neural Networks Learn. Syst., 2021, pp. 1–12.
[22] P. Parsa, M. R. Akbarzade-T, And F. Baghbani, “Observer-Based Adaptive Emotional Command-Filtered Backstepping For Cooperative Control Of Inpud-Saturated Uncertain Strict-Feedback Multi-Agent Systems”, IET Control Theory & Applications , Vol. 17, No. 7, 2003, pp. 906-926, Doi: 10.1049/Cth2.12426.
[23] W.-Y. Wang, Y.-H. Chien, and T.-T. Lee, “Observer-Based T – S Fuzzy Control for a Class of General Nonaffine Nonlinear Systems Using Generalized Projection-Update Laws”, IEEE Trans. Fuzzy Syst., Vol. 19, No. 3, 2011, pp. 493–504.
[24] L. X. Wang, A Course in Fuzzy Systems and Control. Prentice-Hall International, Inc., 1997.
Journal of Modeling in Engineering
Volume 21, Issue 74
November 2023
Pages 37-49

Files

History

  • Receive Date: 25 November 2022
  • Revise Date: 23 March 2023
  • Accept Date: 11 April 2023

Share

How to cite

Statistics