Speed control design for a vehicle system using fuzzy logic and PID controller

Olivier, M.; Munyazikwiye, Bernard B.; Karimi, Hamid Reza

doi:10.1109/ifuzzy.2015.7391894

Cited by 12 publications

(8 citation statements)

References 9 publications

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“…Before carrying out the hardware/software design of the proposed fuzzy controller, we have tested it by applying a car plant [26] according to our car dimensions, shown in Figure 5. It is a discrete transfer function given in (4).…”

Section: System Modelmentioning

confidence: 99%

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FPGA based co-design of a speed fuzzy logic controller applied to an autonomous car

Aridhi

Popescu²,

Mami

2021

IJRES

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This paper invests in FPGA technology to control the speed of an autonomous car using fuzzy logic. For that purpose, we propose a co-design based on a novel fuzzy controller IP. It was developed using the hardware language VHDL and driven by the Zynq processor through an SDK software design written in C. The proposed IP acts according to the ambient temperature and the presence or absence of an obstacle and its distance from the car. The partitioning of the co-design tasks divides them into hardware and software parts. The simulation results of the fuzzy IP and those of the complete co-design implementation on a Xilinx Zynq board showed the effectiveness of the proposed controller to meet the target constraints and generate suitable PWM signals. The proposed hardware architecture based on 6-LUT blocks uses 11 times fewer logic resources than other previous similar designs. Also, it can be easily updated when new constraints on the system are to be considered, which makes it suitable for many related applications. Fuzzy computing was accelerated thanks to the use of digital signal processing blocks that ensure parallel processing. Indeed, a complete execution cycle takes only 7 us.

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Section: System Modelmentioning

confidence: 99%

“…𝛥𝑣 𝛥𝑢 = 0.0007198z 2 +0.00217z+0.0003955 𝑧 3 -2.18z+1.487z-0.3009 (4) where u  is the control signal that is considered as throttle control and used to increase or decrease the engine driver force [26] and v  is the car speed in cm/s. Figure 4 presents the system model developed in MATLAB/Simulink environment.…”

Section: 𝐻(𝑧) =mentioning

confidence: 99%

FPGA based co-design of a speed fuzzy logic controller applied to an autonomous car

Aridhi

Popescu²,

Mami

2021

IJRES

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“…Obtaining an admissible mathematical model is viable for a simple system, but this is not always possible for complex cases. To bridge this gap, non-model based control-strategies like neural and fuzzy techniques were later developed [5] and implemented in a number of control applications like, cruise control [6][7][8][9], industrial processes [10][11][12], robotics [13][14] and ball and beam/plate systems [15][16][17][18][19], etc.…”

Section: Introductionmentioning

confidence: 99%

“…The results showed improved performance with the neuro-fuzzy controller (Adaptive Neuro-Fuzzy Inference System, ANFIS) compared with manually tuned control (typically used in the industries). Munyaneza et al [7] concluded that PD control produces small rise time (in cruise-control system), yet it creates a high percentage of overshoot, resulting in overall poor performance as compared with fuzzy control. Similarly, Dawood et al [6] have compared PID, fuzzy logic, and genetic-algorithm for the cruise control application.…”

Section: Introductionmentioning

confidence: 99%

Design and implementation of adaptive neuro-fuzzy inference system for the control of an uncertain ball and beam apparatus

Saleem

Shahid

Nouman

et al. 2022

murjet

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Controlling an uncertain mechatronic system is challenging and crucial for its automation. In this regard, several control-strategies are developed to handle such systems. However, these control-strategies are complex to design, and require in-depth knowledge of the system and its dynamics. In this study, we are testing the performance of a rather simple control-strategy (Adaptive Neuro- Fuzzy Inference System) using an uncertain Ball and Beam System. The custom- designed apparatus utilizes image processing technique to acquire the position of the ball on the beam. Then, desired position is achieved by controlling the beam angle using Adaptive Neuro-Fuzzy and PID control. We are showing that adaptive neuro-fuzzy control can effectively handle the system uncertainties, which traditional controllers (i.e., PID) cannot handle.

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“…Many kinds of research concentrated on speed track. Proportional-integral-derivative (PID) based controller is applied in [12] which can realize speed tracking. To further improve the response speed, linear quadratic regulator (LQR) is also studied in [13].…”

Section: Introductionmentioning

confidence: 99%

Hierarchical speed control for autonomous electric vehicle through deep reinforcement learning and robust control

He²,

Chen

et al. 2021

IET Control Theory & Appl

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For the speed control system of autonomous electric vehicle (AEV), challenge happens with how to determine an appropriate driving speed to satisfy the dynamic environment while resisting uncertainty and disturbance. Therefore, this paper proposes a robust optimal speed control approach based on hierarchical architecture for AEV through combining deep reinforcement learning (DRL) and robust control. In decision-making layer, a deep maximum entropy proximal policy optimization (DMEPPO) algorithm is presented to obtain an optimal speed via dynamic environment information, heuristic target entropy and adaptive entropy constraint. In motion control layer, to track the learned optimal speed while resisting uncertainty and disturbance, a robust speed controller is designed by the linear matrix inequality (LMI). Finally, simulation experiment results show that the proposed robust optimal speed control scheme based on hierarchical architecture for AEV is feasible and effective.

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Speed control design for a vehicle system using fuzzy logic and PID controller

Cited by 12 publications

References 9 publications

FPGA based co-design of a speed fuzzy logic controller applied to an autonomous car

FPGA based co-design of a speed fuzzy logic controller applied to an autonomous car

Design and implementation of adaptive neuro-fuzzy inference system for the control of an uncertain ball and beam apparatus

Hierarchical speed control for autonomous electric vehicle through deep reinforcement learning and robust control

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