Robust adaptive control of switched-reluctance motors without velocity measurements

Chumacero, Erik; Lorı́a, Antonio; Espinosa-Pérez, G.

doi:10.23919/ecc.2013.6669158

Cited by 3 publications

(6 citation statements)

References 10 publications

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“…The on-line LSFF algorithm (39) was experimentally implemented using the next values: P 0 = diag{150, 200, 50, 50}, = 2, r 1 = 20, and r 2 = 400. On the other hand, the off-line LS algorithm (46) was implemented using w = 30 in filter (43). Besides, for the off-line LS algorithm [45], the position quantization error was reduced by filtering position measurements through a low-pass non-causal zero-phase digital filter, implemented in Matlab using the filtfilt function, with a cut-off frequency of 0.007 radians.…”

Section: Resultsmentioning

confidence: 99%

“…Formulas ( , with̄> 0 a positive arbitrary small number, and > 0. At the time interval Ω 1 = [t r , t r +̄) we may assign to the corresponding estimated signal constant values or approximations using the dirty derivative [42,43], which allows avoiding the singularity at t = t r . The whole approximation is valid within the interval Ω = Ω 1 ∪ Ω 2 .…”

Section: Algebraic State Estimation and Persistent Excitationmentioning

confidence: 99%

“…Then, the formula for computing the estimation

{\overset{\overset{x}{true}}{true}}_{2}

for a resetting time t r >0 is

\begin{matrix} right & left {\dot{\tilde{x}}}_{2} = \frac{1}{{[t - t_{r}]}^{6}} [- 720 \int^{(4)} x_{1} & right \\ right & left + 4320 \int^{(3)} [t - t_{r}] x_{1} - 5400 \int^{(2)} {[t - t_{r}]}^{2} x_{1} \\ right & left + 2400 \int {[t - t_{r}]}^{3} x_{1} - 300 {[t - t_{r}]}^{4} x_{1} \\ right & left + 24 {[t - t_{r}]}^{5} {\tilde{x}}_{2}] . \end{matrix}

Formulas – are valid during the time interval

{normal Ω}_{2} = ([), t_{r} + \overset{ϵ}{true}, t_{r} + κ)

, with

\overset{ϵ}{true} > 0

a positive arbitrary small number, and κ >0. At the time interval

{normal Ω}_{1} = [t_{r}, t_{r} + \overset{ϵ}{true})

we may assign to the corresponding estimated signal constant values or approximations using the dirty derivative , which allows avoiding the singularity at t = t r . The whole approximation is valid within the interval

…”

Section: Algebraic State Estimation and Persistent Excitationmentioning

confidence: 99%

“…This implies that η must be selected sufficiently large. However, if measurement noise is significative, a large value of η may lead to huge errors at the initial time of velocity estimation . For this simulation with the DC servomechanism, the best estimations were obtained by selecting η =100[( r a d / s ) −1 ].…”

Section: Algebraic State Estimation and Persistent Excitationmentioning

confidence: 99%

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An Efficient on‐Line Parameter Identification Algorithm for Nonlinear Servomechanisms with an Algebraic Technique for State Estimation

Miranda-Colorado

Moreno–Valenzuela

2017

Asian Journal of Control

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This paper presents a methodology for on-line closed-loop identification of a class of nonlinear servomechanisms. First, a system is defined with the same structure as the actual servomechanism, but using time-varying estimated parameters. No coupling between the actual and the estimation systems is present. Position, velocity and acceleration errors, defined as the difference of the actual respective signals and the signals coming from the estimation system, are required in the identification method. Then, a recursive algorithm for on-line identification of the system parameters is derived from a cost function depending on a linear combination of all the estimation errors. Velocity and acceleration estimates, required in the proposed parameter identification algorithm, are obtained by using an algebraic methodology. The identification algorithm is compared by means of real-time experiments with an on-line least squares algorithm with forgetting factor and an off-line least squares algorithm with data preprocessing. Experimental results show that the proposed approach has a performance comparable to that obtained with the off-line least squares algorithm, but with the advantage of avoiding any preprocessing.

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Section: Resultsmentioning

confidence: 99%

Section: Algebraic State Estimation and Persistent Excitationmentioning

confidence: 99%

“…Then, the formula for computing the estimation

{\overset{\overset{x}{true}}{true}}_{2}

for a resetting time t r >0 is

\begin{matrix} right & left {\dot{\tilde{x}}}_{2} = \frac{1}{{[t - t_{r}]}^{6}} [- 720 \int^{(4)} x_{1} & right \\ right & left + 4320 \int^{(3)} [t - t_{r}] x_{1} - 5400 \int^{(2)} {[t - t_{r}]}^{2} x_{1} \\ right & left + 2400 \int {[t - t_{r}]}^{3} x_{1} - 300 {[t - t_{r}]}^{4} x_{1} \\ right & left + 24 {[t - t_{r}]}^{5} {\tilde{x}}_{2}] . \end{matrix}

Formulas – are valid during the time interval

{normal Ω}_{2} = ([), t_{r} + \overset{ϵ}{true}, t_{r} + κ)

, with

\overset{ϵ}{true} > 0

a positive arbitrary small number, and κ >0. At the time interval

{normal Ω}_{1} = [t_{r}, t_{r} + \overset{ϵ}{true})

…”

Section: Algebraic State Estimation and Persistent Excitationmentioning

confidence: 99%

Section: Algebraic State Estimation and Persistent Excitationmentioning

confidence: 99%

See 2 more Smart Citations

An Efficient on‐Line Parameter Identification Algorithm for Nonlinear Servomechanisms with an Algebraic Technique for State Estimation

Miranda-Colorado

Moreno–Valenzuela

2017

Asian Journal of Control

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show abstract

“…Control of SRMs is a nontrivial problem because their dynamic model is a highly nonlinear one [9][10][11][12][13][14]. Stability and robustness are also features of primary importance in the development of SRM control schemes [15][16][17][18][19][20]. It is noteworthy that the use of SRMs in traction of electric vehicles is gaining ground [21][22][23][24][25][26].…”

Section: Introductionmentioning

confidence: 99%

Nonlinear H-infinity control for switched reluctance machines

Rigatos

Siano

Ademi

2019

Nonlinear Engineering

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The article proposes a nonlinear H-infinity control method for switched reluctance machines. The dynamic model of the switched reluctance machine undergoes approximate linearization round local operating points which are redefined at each iteration of the control algorithm. These temporary equilibria consist of the last value of the reluctance machine’s state vector and of the last value of the control signal that was exerted on it. For the approximate linearization of the reluctance machine’s dynamics, Taylor series expansion is performed through the computation of the associated Jacobian matrices. The modelling errors are compensated by the robustness of the control algorithm. Next, for the linearized equivalent model of the reluctance machine an H-infinity feedback controller is designed. This requires the solution of an algebraic Riccati equation at each time-step of the control method. It is shown that the control scheme achieves H-infinity tracking performance, which implies maximum robustness to modelling errors and external perturbations. The stability of the control loop is proven through Lyapunov analysis.

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Chattering-Free Robust Adaptive Sliding Mode Speed Control for Switched Reluctance Motor

Namazi¹,

Koofigar²,

Ahn³

2020

Control Theory in Engineering [Working Title]

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This study describes an adaptive sliding mode control (ASMC) for the control of switched reluctance motor (SRM). The main objective is to minimize torque ripples with controller effort smoothness while the system is under perturbation by structured uncertainties, unknown parameters, and external disturbances. The control algorithm employs an adaptive approach to remove the need for prior knowledge within the bound of perturbations. This is suitable for tackling the chattering problem in the sliding motion of ASMC. In order to achieve control effort smoothness and more effective elimination of chattering, the algorithm then incorporates proper modifications in order to build a chattering-free robust adaptive sliding mode control (RASMC) using Lyapunov stability theory. A final advantage of the algorithm is that system stability and error convergence are guaranteed. The effectiveness of the proposed controller in improving robustness and minimizing ripples is demonstrated by numerical simulation. Experimental validation is used to demonstrate the efficiency of the proposed scheme. The results indicate that RASMC provides a superior performance with respect to speed tracking and disturbance rejection over the conventional sliding mode control (CASMC) in the face of uncertainties in model and dynamic loads.

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Robust adaptive control of switched-reluctance motors without velocity measurements

Cited by 3 publications

References 10 publications

An Efficient on‐Line Parameter Identification Algorithm for Nonlinear Servomechanisms with an Algebraic Technique for State Estimation

An Efficient on‐Line Parameter Identification Algorithm for Nonlinear Servomechanisms with an Algebraic Technique for State Estimation

Nonlinear H-infinity control for switched reluctance machines

Chattering-Free Robust Adaptive Sliding Mode Speed Control for Switched Reluctance Motor

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