Data-driven Model-Free Adaptive Control of twin rotor aerodynamic systems

Roman, Raul‐Cristian; Rădac, Mircea-Bogdan; Precup, Radu‐Emil

doi:10.1109/saci.2014.6840079

Cited by 6 publications

(8 citation statements)

References 35 publications

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“…The linearization of (1) at the equilibrium point leads to the linearized state-space model of the process, which consists of the third to eighth equations plus the first two equations replaced by [17,18]:…”

Section: Twin Rotor Aerodynamic Systemmentioning

confidence: 99%

“…This procedure involving a process model is usually an optimization problem, which is solved for a specified control scenario as illustrated in [17,18]. However, this defies the purpose of MFAC and prevents it from being a truly model-free approach.…”

Section: Mfacmentioning

confidence: 99%

“…The bounds in (6) (18) for pitch control. VRFT is next applied to compute the controllers initial parameters using a GSA that minimizes the o.f.…”

Section: Siso Experimental Validationmentioning

confidence: 99%

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Data-driven Model-Free Adaptive Control Tuned by Virtual Reference

2016

APH

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This paper proposes a new tuning approach, by which, all parameters of a datadriven Model-Free Adaptive Control (MFAC) algorithm are automatically determined using a nonlinear Virtual Reference Feedback Tuning (VRFT) algorithm. The approach is referred to as mixed MFAC-VFRT control and it leads to mixed MFAC-VFRT algorithms. An advantage of mixed MFAC-VFRT control, is that it combines systematically, the features of VRFT (it computes the controller parameters using only the input/output data) with those of MFAC. This is especially illustrated by comparison with the classical MFAC algorithms, the initial values of the parameters, which are obtained through a process that involves solving an optimization problem. The application that validates the mixed MFAC-

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Section: Twin Rotor Aerodynamic Systemmentioning

confidence: 99%

Section: Mfacmentioning

confidence: 99%

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Data-driven Model-Free Adaptive Control Tuned by Virtual Reference

2016

APH

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“…Building upon our recent results on data-driven tuning approaches for twin rotor aerodynamic systems [24], on sliding mode-based design of MFC algorithms [18] and on the pole placement-based design of MFC algorithms in [20], this paper suggests two new contributions. First, a novel optimal design approach for discrete-time MIMO MFC algorithms is proposed on the basis of a Linear Quadratic Regulator (LQR)-based formulation.…”

Section: Introductionmentioning

confidence: 99%

“…MFC is applied in automotive processes [11], [12], hydroelectric power plants [13], active magnetic bearings [14], laboratory manipulators [15], electro-hydraulic systems [16], DC motor servo systems [17], [18], shape memory alloy active springs [19] and aerodynamic systems [20]. Another similar adaptive MFC approach is proposed in [21] and [22] and successfully applied in [21]- [24].…”

Section: Introductionmentioning

confidence: 99%

Data-driven optimal model-free control of twin rotor aerodynamic systems

Roman

Rădac

Precup

et al. 2015

2015 IEEE International Conference on Industrial Technology (ICIT)

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This paper proposes data-driven Model-Free Control (MFC) algorithms for Multi Input-Multi Output (MIMO) twin rotor aerodynamic systems. A discrete-time formulation of the algorithms is given in the framework of a MIMO control system structure with azimuth and pitch position control loops. An optimal design approach of the MIMO MFC algorithms is proposed using a Linear Quadratic Regulator formulation. The sensitivity of the new MFC structure with respect to parametric variations of the aerodynamic system is checked against two additional structures that are also optimally designed. The case study also reveals how the sampling time influences the control system performance.

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