LMI-Based Sensorless Control of Permanent-Magnet Synchronous Motors

Lian, Kuang‐Yow; Chiang, Che-Hsueh; Tu, Hui-Wen

doi:10.1109/tie.2007.899829

Cited by 60 publications

(7 citation statements)

References 27 publications

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“…Sensor reduction based control approach can be formulated as an output feedback control problem [23]. To reduce the number of feedback sensors, we first derive the conditions to design a fuzzy output feedback controller using Lyapunov stability arguments and LMI techniques.…”

Section: Sensor Reduction Via Fuzzy Dynamic Output Feedback Controlmentioning

confidence: 99%

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Sensor Reduction for Driver-Automation Shared Steering Control via an Adaptive Authority Allocation Strategy

Nguyen

Sentouh

Popieul

2018

IEEE/ASME Trans. Mechatron.

108

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This paper presents a new shared control method for lane keeping assist (LKA) systems of intelligent vehicles. The proposed method allows the LKA system to effectively share the control authority with a human driver by avoiding or minimizing the conflict situations between these two driving actors. To realize the shared control scheme, the unpredictable driver-automation interaction is explicitly taken into account in the control design via a fictive driver activity variable. This latter is judiciously introduced into the driver-road-vehicle system to represent the driver's need for assistance in accordance with his/her real-time driving activity. Using Lyapunov stability arguments, Takagi-Sugeno fuzzy model-based design conditions are derived to handle not only the time-varying driver activity variable but also a large variation range of vehicle speed. Both simulation and hardware experiments are presented to demonstrate that the proposed control strategy together with an LMI (linear matrix inequality) design formulation provide an effective tool to deal with the challenging shared steering control issue. In particular, a fuzzy output feedback control scheme is exploited to achieve the shared control goal without at least two important vehicle sensors. These physical sensors are widely employed in previous works to measure the lateral speed and the steering rate for the control design and real-time implementation. The successful results of this idea of sensor-reduction control has an obvious interest from practical viewpoint.

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Section: Sensor Reduction Via Fuzzy Dynamic Output Feedback Controlmentioning

confidence: 99%

“…Let us define Π = X 11 I X 12 0 . Pre-and postmultiplying (23) with diag Π , I, I and its transpose leads to…”

Section: B Fuzzy Model-based Output Feedback Control Designmentioning

confidence: 99%

Sensor Reduction for Driver-Automation Shared Steering Control via an Adaptive Authority Allocation Strategy

Nguyen

Sentouh

Popieul

2018

IEEE/ASME Trans. Mechatron.

108

View full text Add to dashboard Cite

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“…, F T F I, and lemma 2 can be applied. Another method is referred to as the bounding method [37][38][39][40], which uses the Lipschitz condition, matrix norm, differential mean value theorem, or simple assumptions to obtain a bound on µ i (ξ ) − µ i ( ξ ) which then results in an inequality. However, it has been found that the above-mentioned methods may have problems in obtaining feasible solutions to the observer design of system (43).…”

Section: Ts Fuzzy Observer Designmentioning

confidence: 99%

Direct voltage control of magnetorheological damper for vehicle suspensions

Lam

Cheung

et al. 2013

Smart Mater. Struct.

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The paper presents a study on the direct voltage control of a magnetorheological (MR) damper for application in vehicle suspensions. As MR damper dynamics is highly nonlinear, the direct control system design for an MR damper is difficult. Representing an MR damper by a Takagi–Sugeno (TS) fuzzy model enables the linear control theory to be directly applied to design the MR damper controller. In this paper, first the MR damper dynamics is represented by a TS fuzzy model, and then an H∞ controller that considers the suspension performance requirements and the constraint on the input voltage for the MR damper is designed. Furthermore, considering the case that not all the state variables are measurable in practice, the design of an H∞ observer with immeasurable premise variables and the design of a robust controller are proposed, respectively. Numerical simulations are used to validate the effectiveness of the proposed approaches.

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“…Although the controller did not require any precise knowledge of motor parameters, there were no tested uncertain parameters. Recently, several researchers have proposed many advanced control strategies including fuzzy logic control (FLC) with nonlinear optimal control for the effective control of PMSM systems, nonlinear optimal control (NOC) with sliding mode control (SMC), neural network control, and adaptive control FLC [14]. Because of its fuzzy reasoning ability [15] has become a research hotspot.…”

Section: Introductionmentioning

confidence: 99%

Adaptive PI and RBFNN PID Current Decoupling Controller for Permanent Magnet Synchronous Motor Drives: Hardware-Validated Results

Zeng

Wang

2022

Energies

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This study presents an adaptive proportional-integral (PI) and radial basis function neural network proportional-integral-derivative (PID) current control solution for permanent magnet synchronous motor (PMSM) drives. The proposed controller includes four controls: a decoupling term, a PI term, a supervision term, and a radial basis neural network-PID (RBFNN-PID) term. The first control term makes up the nonlinear factors, the second automatically adjusts the control gains, the third guarantees the system stability, and the fourth optimizes the PID parameters to achieve optimal system performance. Unlike off-line tuned PID controllers, the adaptive controller includes an adaptive tuning method for the on-line adjustment of control gain based on the gradient descent strategy. Therefore, it can be adjusted and handle the uncertainty of any system parameters. The program is not only simple and easy to be implemented but also ensures the accuracy and rapidity of the tracking speed. The control system has proven to be asymptotically stable. To verify the theory and application of the algorithm, a comparative experiment between the adaptive PI + RBFNN-PID controller and traditional PI + PID controller was conducted, demonstrating good robustness, which can effectively improve the PMSM. The results confirm that the proposed design achieves excellent control stability (i.e., quicker transient responding and smaller steady state error) in the presence of parametric uncertainty compared to conventional PID methods.

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LMI-Based Sensorless Control of Permanent-Magnet Synchronous Motors

Cited by 60 publications

References 27 publications

Sensor Reduction for Driver-Automation Shared Steering Control via an Adaptive Authority Allocation Strategy

Sensor Reduction for Driver-Automation Shared Steering Control via an Adaptive Authority Allocation Strategy

Direct voltage control of magnetorheological damper for vehicle suspensions

Adaptive PI and RBFNN PID Current Decoupling Controller for Permanent Magnet Synchronous Motor Drives: Hardware-Validated Results

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