Comparative Analysis of Different Types of Membership Functions for Fuzzy Logic Controller in Direct Torque Control of Induction Motor

Hanumanthakari, Sudheer

doi:10.1007/978-981-15-8439-8_33

Cited by 4 publications

(4 citation statements)

References 10 publications

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“…, determine what elements comprise the fuzzy set associated with a given linguistic value (Hanumanthakari et al, 2021).…”

Section: Zn Mmentioning

confidence: 99%

“…Linguistic values describe their associated variable's performance with descriptors like fast and slow. Membership functions,

{\mu }_{{Z}_{n,m}},

determine what elements comprise the fuzzy set associated with a given linguistic value (Hanumanthakari et al, 2021).

\text{IF}\,{I}_{1}\,\text{is}\,{A}_{1,2}\,\text{AND/OR}\,{I}_{2}\,\text{is}\,{A}_{2,5}\,\text{THEN}\,{O}_{1}\,\text{is}\,{B}_{1,1}

…”

Section: Control Designmentioning

confidence: 99%

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Hierarchical rule‐base reduction fuzzy control for constant velocity path tracking of a differential steer vehicle

Dekhterman,

Cichon,

Norris

et al. 2024

Journal of Field Robotics

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Hierarchical rule‐base reduction (HRBR) was used to create a new form of waypoint navigation control for a skid‐steer vehicle, which consists of a multiple input‐single output nonlinear fuzzy angular velocity controller. HRBR enabled an increase in system complexity by only selecting the rules most influential on state errors. The membership functions of the fuzzy controller employed a trapezoidal structure with a completely symmetric rule‐base. The work was motivated by a desire to find the limitations of fuzzy control in terms of accuracy and robustness across environments. To accomplish this, an examination of the proposed controller is completed by employing test courses. The test courses examine the effects of steering disturbance, phase lag, and overshoot. The controller's performance was compared with existing waypoint navigation controllers, pure pursuit, and the Huskić Buck Zell (HBZ) controller. After initial controller development/tuning was completed in the simulation, the controllers were tested outdoors. The HRBR fuzzy was found to outperform the pure pursuit and HBZ in simulation (36.74% and 80.56% improvement in Root Mean Square Error (RMSE) on one course) and on concrete with mixed results on a surface with different dynamics, namely grass (23.13% and 55.64% improvement in RMSE averaging the two on the same course). If a moderate amount of tuning of the fuzzy controller had not been performed in simulation, failure across different environments would be more likely. The present study is important as it illustrates the potential effectiveness of the HRBR for control applications beyond waypoint navigation.

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“…, determine what elements comprise the fuzzy set associated with a given linguistic value (Hanumanthakari et al, 2021).…”

Section: Zn Mmentioning

confidence: 99%

“…Linguistic values describe their associated variable's performance with descriptors like fast and slow. Membership functions,

{\mu }_{{Z}_{n,m}},

determine what elements comprise the fuzzy set associated with a given linguistic value (Hanumanthakari et al, 2021).

\text{IF}\,{I}_{1}\,\text{is}\,{A}_{1,2}\,\text{AND/OR}\,{I}_{2}\,\text{is}\,{A}_{2,5}\,\text{THEN}\,{O}_{1}\,\text{is}\,{B}_{1,1}

…”

Section: Control Designmentioning

confidence: 99%

Hierarchical rule‐base reduction fuzzy control for constant velocity path tracking of a differential steer vehicle

Dekhterman,

Cichon,

Norris

et al. 2024

Journal of Field Robotics

View full text Add to dashboard Cite

show abstract

“…Linguistic values describe their associated variable's performance with descriptors like fast and slow. Membership functions, µ Zn,m , determine what elements comprise the fuzzy set associated with a given linguistic value (Hanumanthakari et al, 2021).…”

Section: Control Designmentioning

confidence: 99%

Hierarchical Rule-Base Reduction Fuzzy Control for Constant Velocity Path Tracking of a Differential Steer Vehicle

Dekhterman

Cichon

Norris

et al. 2023

Preprint

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A new form of waypoint navigation controller for a skid-steer vehicle is presented, which consisted of a multiple input-single output nonlinear fuzzy angular velocity controller. The mem- bership functions of the fuzzy controller employed a trape- zoidal structure with a completely symmetric rule-base. No- tably, Hierarchical Rule-Base Reduction (HRBR) was incorpo- rated into the controller to select only the rules most influen- tial on state errors. This was done by selecting inputs/outputs and generating a hierarchy of inputs using a Fuzzy Relations Control Strategy (FRCS). Similar to some traditional fuzzy con- trollers, the system provided coverage for the global operat- ing environment. However, a rule for every possible combi- nation of variables and states was no longer necessary. Con- sequently, HRBR fuzzy controllers effectively increase both the number of inputs and their associated fidelity without the rule-base dramatically increasing. To contextualize the performance of the controller, a background on vehicle dy- namic modeling methodologies and an in-depth explanation of the related simulation model are provided. An examina- tion of the proposed controller is then completed employing test courses. The test courses examine the effects of steer- ing disturbance, phase lag, and overshoot as expressed in Root Mean Square Error (RMSE), Max Error (ME), and Course Completion Time (CCT). Finally, simulation and experimental results for the controller’s performance were compared with a state-of-the-art waypoint navigation vehicle controller, ge- ometric pure pursuit. The fuzzy was found to outperform the pure pursuit experimentally by 52.1 percent in RMSE, 26.8 percent in ME, and 1.07 percent in CCT, on average, validat- ing the viability of the controller.

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“…Furthermore, a level of membership between zero and one is assigned to a given linguistic value upon fuzzification. Membership functions, µ Zn,m , determine what elements comprise the fuzzy set associated with each linguistic value [23].…”

Section: A General Structurementioning

confidence: 99%

Hierarchical Rule-Base Reduction Fuzzy Control for Path Tracking Variable Linear Speed Differential Steer Vehicles

Dekhterman,

Norris,

Sreenivas

et al. 2023

Preprint

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show abstract

Comparative Analysis of Different Types of Membership Functions for Fuzzy Logic Controller in Direct Torque Control of Induction Motor

Cited by 4 publications

References 10 publications

Hierarchical rule‐base reduction fuzzy control for constant velocity path tracking of a differential steer vehicle

Hierarchical rule‐base reduction fuzzy control for constant velocity path tracking of a differential steer vehicle

Hierarchical Rule-Base Reduction Fuzzy Control for Constant Velocity Path Tracking of a Differential Steer Vehicle

Hierarchical Rule-Base Reduction Fuzzy Control for Path Tracking Variable Linear Speed Differential Steer Vehicles

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