Virtual Reference Feedback Tuning of Model-Free Control Algorithms for Servo Systems

Abstract

This paper proposes the combination of two data-driven techniques, namely virtual reference feedback tuning (VRFT) and model-Free Control (MFC) in terms of the VRFT of MFC algorithms dedicated to servo systems. VRFT ensures the automatic optimal computation of the parameters of three MFC algorithms represented by intelligent proportional (iP), intelligent proportional-integral (iPI), and intelligent proportional-integral-derivative (iPID) controllers. The combination of MFC and VRFT leads to a novel mixed MFC-VRFT approach. The approach is validated by experimental results related to the angular speed control of modular servo system laboratory equipment. The performance of the control systems with the MFC algorithms (iP, iPI, and iPID controllers) tuned by the mixed MFC-VRFT approach is compared with that of control systems with MFC algorithms tuned by a metaheuristics gravitational search algorithm (GSA) optimizer, and of control systems with I, PI and PID controllers optimally tuned by VRFT and GSA in the same optimization problem.

Combination of Data-Driven Active Disturbance Rejection and Takagi-Sugeno Fuzzy Control with Experimental Validation on Tower Crane Systems

“…This PDTSFC1 component is built around a Two Inputs-Single Output Fuzzy Controller (TISO-FC) as shown in Figure 2, where Bê and B ∆ê are the parameters of the membership functions, and the SUM and PROD operators are used in the inference engine of the fuzzy component [56]. The rule base consists of nine rules presented in Table 1, with the rule consequents: (11) This PDTSFC1 component is built around a Two Inputs-Single Output Fuzzy Controller (TISO-FC) as shown in Figure 2, where e Bˆ and e BΔ are the parameters of the membership functions, and the SUM and PROD operators are used in the inference engine of the fuzzy component [56]. The rule base consists of nine rules presented in Table 1, with the rule consequents: 12lead to the conclusion that the PDTSFC1 component is practically a nonlinear combination of three discrete-time PD controllers placed in the rule consequents, which change according to the fired rules.…”

“…The control system structure with second-order discrete-time ADRC-PDTSFC1 algorithm is presented in Figure 3. Table 1, (11) and 12lead to the conclusion that the PDTSFC1 component is practically a nonlinear combination of three discrete-time PD controllers placed in the rule consequents, which change according to the fired rules. The main purpose of the parameter i γ is used to adjust the overshoot of the CS with the ADRC-PDTSFC1 technique for TCS control [56].…”

“…ADRC [1][2][3][4] is one of the most popular data-driven techniques along with Model-Free Adaptive Control [5][6][7][8][9], Model-Free Control [10][11][12][13][14] or Virtual Reference Feedback Tuning [15][16][17][18]. The main advantage that made data-driven techniques so popular is that they use only the input/output data from the process.…”

Supplementary Control of Air–Fuel Ratio Using Dynamic Matrix Control for Thermal Power Plant Emission

“…The main advantage of the MFC technique is that the process model is approximated through a fast estimator using an approximation of the process model, which is locally valid and, furthermore, on a relatively short time window. The MFC techniques were applied to a wide range of processes, which include immune systems [13], robot systems [14,15], twin rotor aerodynamic systems (TRASs) [16][17][18], aircraft system [19] and servo systems [20].…”

System Identification Algorithm for Computing the Modal Parameters of Linear Mechanical Systems

“…Another important application of the system identification scientific discipline is the refinement of a finite element model through dynamic testing for the design of vibration control systems based on open-loop and closed-loop control schemes [51][52][53][54]. The methodologies of time domain analysis employed in the general area of applied system identification are of interest for this investigation because the state-space models obtained through these numerical procedures can be readily used for developing effective control actions employing well-established and robust algorithms, such as, for example, the pole placement method, the linear-quadratic regulator technique and the H-infinity method, as well as more advanced nonlinear control approaches [55][56][57][58][59][60][61]. In the case of the parametric identification problem, one needs to construct analytical expressions in terms of the desired parameters and correlate them with experimental data in order to identify the parameters of interest [62,63].…”