Gain Tuning of Fuzzy PID Controllers for MIMO Systems: A Performance-Driven Approach

Gil, Paulo; Lucena, Catarina; Cardoso, Alberto; Palma, Lúıs Brito

doi:10.1109/tfuzz.2014.2327990

Cited by 54 publications

(27 citation statements)

References 58 publications

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“…Fuzzy gain tuning has been an effective way to tune parameters of a controller online with respect to parameter changes. It has been applied recently to tune PID controller for multiple input multiple output (MIMO) systems [14], continuous stirred-tank reactor (CSTR) systems [15], maximum power point tracking in a photovoltaic system [16], load frequency control [17,18] and many other control applications [19][20][21][22].…”

Section: Wireless Sensor Network -Insights and Innovationsmentioning

confidence: 99%

Fuzzy Adaptive Setpoint Weighting Controller for WirelessHART Networked Control Systems

Hassan

Ibrahim²,

Saad³

et al. 2017

Wireless Sensor Networks - Insights and Innovations

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Gain range limitation of conventional proportional-integral-derivative (PID) controllers has made them unsuitable for application in a delayed environment. These controllers are also not suitable for use in a Wireless Highway Addressable Remote Transducer (WirelessHART) protocol networked control setup. This is due to stochastic networkinduced delay and uncertainties such as packet dropout. The use of setpoint weighting strategy has been proposed to improve the performance of the PID in such environments. However, the stochastic delay still makes it difficult to achieve optimal performance. This chapter proposes an adaptation to the setpoint weighting technique. The proposed approach will be used to adapt the setpoint weighting structure to variation in WirelessHART network-induced delay through fuzzy inference. Result comparison of the proposed approach with both setpoint weighting and proportional-integral (PI) control strategy shows improved setpoint tracking and load regulation. For the first-, second-and third-order systems considered, analysis of the results in the time domain shows that in terms of overshoot, undershoot, rise time, and settling times, the proposed approach outperforms both the setpoint weighting and the PI controller. The approach also shows faster recovery from disturbance effect.

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Section: Wireless Sensor Network -Insights and Innovationsmentioning

confidence: 99%

Fuzzy Adaptive Setpoint Weighting Controller for WirelessHART Networked Control Systems

Hassan

Ibrahim²,

Saad³

et al. 2017

Wireless Sensor Networks - Insights and Innovations

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show abstract

“…However, the main focus is restricted to linear time‐invariant systems in the frequency domain. PID controller for Takagi‐Sugeno fuzzy systems, which are particular gain‐scheduling systems, was considered in Referencs . Reference proposed an LMI‐based iterative algorithm for a proportional‐integral (PI) controller in Takagi‐Sugeno systems under the specific structure of both system and controller.…”

Section: Introductionmentioning

confidence: 99%

Parameterized bilinear matrix inequality techniques for gain‐scheduling proportional integral derivative control design

Shi

Tuan

Apkarian

2020

Intl J Robust & Nonlinear

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Proportional-integral-derivative (PID) structured controller is the most popular class of industrial control but still could not be appropriately exploited in gain-scheduling control systems. To gain the practicability and tractability of gain-scheduling control systems, this paper addresses the  ∞ gain-scheduling PID control. The design of such a controller is based on parameterized bilinear matrix inequalities, which are then solved via a bilinear matrix inequality optimization problem of nonconvex optimization. Several computational procedures are developed for its computation. The merit of the developed algorithms is shown through the benchmark examples. K E Y W O R D SH ∞ proportional-integral-derivative control, bilinear matrix inequality, gain-scheduling control system, nonconvex optimization techniques, parameterized bilinear matrix inequality INTRODUCTIONGain-scheduling systems, which are easily implemented online, have proved as one of the most practical tools for studying complex nonlinear systems. 1 Treating nonlinear systems as gain-scheduling systems allows the application of advanced gain-scheduling control techniques in tackling state feedback and output feedback stabilization of nonlinear systems. [2][3][4][5] Until now, most gain-scheduling controllers are assumed structure-free and full-rank to admit computationally tractable parameterized linear matrix inequality (PLMI)-based or linear matrix inequality (LMI)-based formulations. [4][5][6][7][8][9] Meanwhile, proportional-integral-derivative structured (PID) controller is an indispensable component of industrial control thanks to its simple but versatile structure. PID control theory is still the subject of recent research. 10-19 However, the main focus is restricted to linear time-invariant systems in the frequency domain. PID controller for Takagi-Sugeno fuzzy systems, 20 which are particular gain-scheduling systems, was considered in Referencs 21-24. Reference 25 proposed an LMI-based iterative algorithm for a proportional-integral (PI) controller in Takagi-Sugeno systems under the specific structure of both system and controller. A bilinear matrix inequality (BMI) approach was proposed in Reference 26 to solve the H ∞ gain-scheduling problem in the discrete-time case. This approach, however, is restricted to more favorable state-feedback controllers and PID output-feedback controllers are not considered. Gain-scheduled PID controller design for uncertain linear parameter varying (LPV) systems was studied in Reference 27, where solvability conditions for robust controller synthesis are formulated in terms of BMIs. Note that such characterizations rely on guaranteed-cost performance design of linear quadratic-type and involve matrix terms of degree-3 in the decision variables which seems extremely challenging from a computational viewpoint. Additionally, matrix inequalities were solved using the general 3886

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“… PIDC is mainly designed to work in the systems with single input and single output (SISO), but the circuits with multiple inputs and multiple outputs (MIMO) and multiple inputs and single output (MISO) often fail in providing acceptable performance [4].…”

Section: Introductionmentioning

confidence: 99%

“…To address the problems of nonlinearity and timevariability, new control terms were proposed in [4], [21] and [28], but these techniques were also limited by the fixed range of control parameters, resulting in frequent detuning to accommodate worst-case scenarios, for instance, traction upon icy conditions with old tires.…”

Section: Introductionmentioning

confidence: 99%

Speed Control of Electric Vehicle Propulsion with Autotuning at Changeable Driving Modes and Road Conditions

Aksjonov

Nedoma

Vodovozov

et al. 2019

2019 IEEE International Conference on Mechatronics (ICM)

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Considering the high demands continually imposed on equipment indispensable for intelligent transportation, this paper focuses on the cruise control in propulsion systems of road electric vehicles. The research lays emphasis on how to arrange optimal dynamics in terms of the speed overshoot and the speed rise time at changeable driving modes and road conditions. The paper addresses two aspects, namely, most accurate following the demanded transition process and most rapid achieving the setpoint. The former issue is typical for industrial vehicles operated in rather stable obstacles (loaders, forklift trucks, carriers, etc.), whereas the latter one concerns traditional road electric cars. Online autotuning of the controller is provided directly in the driving process by applying periodically estimated slope and peak input signals for analysing the speed responses and correcting controller settings. Tuning procedures based on binary logic and fuzzy logic approaches are compared. Keywords-road electric vehicle, propulsion, PID controller, binary logic controller, fuzzy logic controller, autotuning.I.

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Gain Tuning of Fuzzy PID Controllers for MIMO Systems: A Performance-Driven Approach

Cited by 54 publications

References 58 publications

Fuzzy Adaptive Setpoint Weighting Controller for WirelessHART Networked Control Systems

Fuzzy Adaptive Setpoint Weighting Controller for WirelessHART Networked Control Systems

Parameterized bilinear matrix inequality techniques for gain‐scheduling proportional integral derivative control design

Speed Control of Electric Vehicle Propulsion with Autotuning at Changeable Driving Modes and Road Conditions

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