A Comparative Analysis of Fuzzy Based Hybrid Anfis Controller for Stabilization and Control of Nonlinear Systems

Kharola, Ashwani

doi:10.14810/ijscmc.2015.4402

Cited by 2 publications

(3 citation statements)

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“…The closed-loop system's transfer function is the reference model. This model captures the closed-loop system's desired behavior [18]. Figure 14 shows the Simulink model of PID-MRAC.…”

Section: Pid Based Model Reference Adaptive Control (Pid-mrac)mentioning

confidence: 99%

“…Increasing the value of 𝛾 causes plants to respond to changes more quickly. The adjusted control signal, u(t), is 𝑢 = 𝑢 𝑃𝐼𝐷 (𝑡)𝜃(𝑡), (18) where 𝑢 𝑃𝐼𝐷 (𝑡) = 𝐾 𝑝 𝑒(𝑡) + 𝐾 𝑖 ∫ 𝑒(𝜏) 𝑑𝜏…”

Section: Pid Based Model Reference Adaptive Control (Pid-mrac)mentioning

confidence: 99%

“…Adaptive approaches construct membership functions and rules automatically, which can be utilized to produce the optimal outcome from training data. The training was carried out using a hybrid learning technique that combines back-propagation with the least square method [18]. Figure 15 shows the ANFIS block diagram, this structure has the same components as the FIS except for the ANN block.…”

Section: Adaptive Neuro Fuzzy Inference Systemmentioning

confidence: 99%

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Performance of Anti-Lock Braking Systems Based on Adaptive and Intelligent Control Methodologies

Abougarair

Shashoa

Aburakhis³

2022

IJEEI

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Automobiles of today must constantly change their speeds in reaction to changing road and traffic circumstances as the pace and density of road traffic increases. In sophisticated automobiles, the Anti-lock Braking System (ABS) is a vehicle safety system that enhances the vehicle's stability and steering capabilities by varying the torque to maintain the slip ratio at a safe level. This paper analyzes the performance of classical control, model reference adaptive control (MRAC), and intelligent control for controlling the (ABS). The ABS controller's goal is to keep the wheel slip ratio, which includes nonlinearities, parametric uncertainties, and disturbances as close to an optimal slip value as possible. This will decrease the stopping distance and guarantee safe vehicle operation during braking. A Bang-bang controller, PID, PID based Model Reference Adaptive Control (PID-MRAD), Fuzzy Logic Control (FLC), and Adaptive Neuro-Fuzzy Inference System (ANFIS) controller are used to control the vehicle model. The car was tested on a dry asphalt and ice road with only straight-line braking. Based on slip ratio, vehicle speed, angular velocity, and stopping time, comparisons are performed between all control strategies. To analyze braking characteristics, the simulation changes the road surface condition, vehicle weight, and control methods. The simulation results revealed that our objectives were met. The simulation results clearly show that the ANFIS provides more flexibility and improves system-tracking precision in control action compared to the Bang-bang, PID, PID-MRAC, and FLC.

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“…The closed-loop system's transfer function is the reference model. This model captures the closed-loop system's desired behavior [18]. Figure 14 shows the Simulink model of PID-MRAC.…”

Section: Pid Based Model Reference Adaptive Control (Pid-mrac)mentioning

confidence: 99%

Section: Pid Based Model Reference Adaptive Control (Pid-mrac)mentioning

confidence: 99%

Section: Adaptive Neuro Fuzzy Inference Systemmentioning

confidence: 99%

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