Abstract
In this paper a second-order data-driven Active Disturbance Rejection Control (ADRC) is merged with a proportional-derivative Takagi-Sugeno Fuzzy (PDTSF) logic controller, resulting in two new control structures referred to as second-order data-driven Active Disturbance Rejection Control combined with Proportional-Derivative Takagi-Sugeno Fuzzy Control (ADRC–PDTSFC). The data-driven ADRC–PDTSFC structure was compared with a data-driven ADRC structure and the control system structures were validated by real-time experiments on a nonlinear Multi Input-Multi Output tower crane system (TCS) laboratory equipment, where the cart position and the arm angular position of TCS were controlled using two Single Input-Single Output control system structures running in parallel. The parameters of the data-driven algorithms were tuned in a model-based way using a metaheuristic algorithm in order to improve the efficiency of energy consumption.
Highlights
In this paper the authors propose to merge two techniques, the second-order data-driven ActiveDisturbance Rejection Control (ADRC) and the proportional-derivative Takagi-Sugeno Fuzzy (PDTSF)logic control in two ways resulting in two control structures referred to as second-order data-drivenActive Disturbance Rejection Control combined with Proportional-Derivative Takagi-Sugeno FuzzyControl (ADRC–PDTSFC)
This section is dedicated to the presentation of three case studies and experimental scenarios to validate the new controllers on tower crane system (TCS) laboratory equipment in order to control the cart position and the arm angular position
K =1 where Λ = [Λc Λθ ]T is a vector variable that contains the tunable parameters of the ADRC, ADRC–PDTSFC1 or ADRC–PDTSFC2 control laws, with the subscripts c and θ related to cart position and arm angular position, respectively, Λ is inserted as an additional argument to the variables in (16) in order to point out that all variables depend on the controller parameters gathered in Λ, Λ∗ = [Λ∗c Λ∗θ ]T is a vector variable that contains the optimal parameters of ADRC, ADRC–PDTSFC1 or ADRC–PDTSFC2 control laws, Λ contains the tunable parameters of ADRC control law expressed as: Λc = [K1c K2c a0c ]T, (17)
Summary
In this paper the authors propose to merge two techniques, the second-order data-driven Active. For the first ADRC–PDTSFC structure (ADRC–PDTSFC1) the proportional-derivative (PD) component in data-driven ADRC is fuzzified using a PDTSF logic controller. For the second ADRC–PDTSFC structure (ADRC–PDTSFC2) a PDTSF logic controller is added to the ADRC algorithm. Two control structures (ADRC–PDTSFC1 and ADRC–PDTSFC2) obtained by the combination of ADRC and Takagi-Sugeno fuzzy control; Experimental validation of the proposed ADRC–PDTSFC1 and ADRC–PDTSFC2 structures on the TCS equipment, a process with nonlinearities; Comparison between the ADRC–PDTSFC structures; Optimal tuning of ADRC, ADRC–PDTSFC1 and ADRC–PDTSFC2 parameters in terms of using more efficient the energy consumption. The second version (“ADRC–PDTSFC2 structure”) is a modification of the same initial ADRC controller, too, by adding further PD feedback terms to the original ones before fuzzifying them This latter structure has more independent parameters than the previous one.
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