Robust Evolving Cloud-based Controller (RECCo)

This paper presents an autonomous Robust Evolving Cloud-based Controller (RECCo). The control algorithm is a fuzzy type with non-parametric (cloud-based) antecedent part and adaptive PID-R consequent part. The procedure starts with zero clouds (fuzzy rules) and the structure evolves during performing the process control. The PID-R parameters of the first cloud are initialized with zeros and furthermore, they are adapted on-line with a stable adaptation mechanism based on Lyapunov approach. The RECCo controller does not require any mathematical model of the controlled process but just basic information such as input and output range and the estimated value of the dominant time constant. Due to the problem space normalization the design parameters are fixed. The proposed controller with the same initial design parameters was tested on two different simulation examples. The experimental results show the convergence of the adaptive parameters and the effectiveness of the proposed algorithm.

[1]  Igor Škrjanc,et al.  On-line Evolving Cloud-based Model Identification for Production Control , 2016 .

[2]  K. Narendra,et al.  Bounded error adaptive control , 1980, 1980 19th IEEE Conference on Decision and Control including the Symposium on Adaptive Processes.

[3]  Plamen P. Angelov,et al.  Simplified fuzzy rule-based systems using non-parametric antecedents and relative data density , 2011, 2011 IEEE Workshop on Evolving and Adaptive Intelligent Systems (EAIS).

[4]  K. Narendra,et al.  Stable model reference adaptive control in the presence of bounded disturbances , 1982 .

[5]  Igor Skrjanc,et al.  Robust evolving cloud-based control for the distributed solar collector field , 2016, 2016 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE).

[6]  Igor Skrjanc,et al.  Robust evolving cloud-based PID control adjusted by gradient learning method , 2014, 2014 IEEE Conference on Evolving and Adaptive Intelligent Systems (EAIS).

[7]  Anuradha M. Annaswamy,et al.  Robust Adaptive Control , 1984, 1984 American Control Conference.

[8]  Edwin Lughofer,et al.  A Comparison of RECCo and FCPFC Controller on Nonlinear Chemical Reactor , 2017 .

[9]  Igor Skrjanc,et al.  A practical implementation of Robust Evolving Cloud-based Controller with normalized data space for heat-exchanger plant , 2016, Appl. Soft Comput..

[10]  Igor Skrjanc,et al.  Robust Evolving Cloud-based Controller in normalized data space for heat-exchanger plant , 2015, 2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE).

[11]  Petar V. Kokotovic,et al.  Instability analysis and improvement of robustness of adaptive control , 1984, Autom..

[12]  Igor Skrjanc,et al.  Robust evolving cloud-based controller for a hydraulic plant , 2013, 2013 IEEE Conference on Evolving and Adaptive Intelligent Systems (EAIS).

[13]  Igor Skrjanc,et al.  Analysis of adaptation law of the robust evolving cloud-based controller , 2015, 2015 IEEE International Conference on Evolving and Adaptive Intelligent Systems (EAIS).

[14]  Igor Skrjanc,et al.  A practical implementation of self-evolving cloud-based control of a pilot plant , 2013, 2013 IEEE International Conference on Cybernetics (CYBCO).