The comparison of optimization for active steering control on vehicle using PID controller based on artificial intelligence techniques

Kusuma, Dwi Hendra; Ali, Machrus; Sutantra, Nyoman

doi:10.1109/isemantic.2016.7873803

Cited by 23 publications

(13 citation statements)

References 13 publications

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“…e development of the proposed mechanism is used in the steering control of autonomous vehicles. Furthermore, the work in [111] has provided a detailed comparison for the active steering control of an autonomous vehicle using a PID controller. ey have tuned the PID controller using five types of SI techniques which include PSO, firefly algorithm (FA), ant colony optimization (ACO), imperialist competitive algorithm (ICA), and bat algorithm (BA).…”

Section: Kinematics and Dynamic Parameters Simulationmentioning

confidence: 99%

Are Self-Driving Vehicles Ready to Launch? An Insight into Steering Control in Autonomous Self-Driving Vehicles

Rasib

Butt

Khalid

et al. 2021

Mathematical Problems in Engineering

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In the last couple of years, academia-industry collaboration has demonstrated rapid advancements in the development of self-driving vehicles. Since it is evident that self-driving vehicles are going to reshape the traditional transportation systems in near future through enhancement in safe and smart mobility, motion control in self-driving vehicles while performing driving tasks in a dynamic road environment is still a challenging task. In this regard, we present a comprehensive study considering the evolution of steering control methods for self-driving vehicles. Initially, we discussed an insight into the traditional steering systems of the vehicles. To the best of our knowledge, currently, there is no taxonomy available, which elaborates steering control methods for self-driving vehicles. In this paper, we present a novel taxonomy including different steering control methods which are categorized into deterministic and heuristic steering control methods. Concurrently, the abovementioned techniques are critically reviewed elaborating their strengths and limitations. Based on the analysis, key challenges/research gaps in existing steering control methods along with the possible solutions have been briefly discussed to improve the effectiveness of the steering system of self-driving vehicles.

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Section: Kinematics and Dynamic Parameters Simulationmentioning

confidence: 99%

Are Self-Driving Vehicles Ready to Launch? An Insight into Steering Control in Autonomous Self-Driving Vehicles

Rasib

Butt

Khalid

et al. 2021

Mathematical Problems in Engineering

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“…Currently, Artificial Intelligence (AI) is often used to develop various sciences including the Imperialist Competitive Algorithm (ICA) [8] [9], Firefly Algorithm (FA) [10], Ant Colony Optimization (ACO) [11], and Particle Swarm Optimization (PSO), Artificial Salmon Tracking Algorithm (ASTA) [12], and ANFIS [13]. In addition, many applied cases have used these algorithms, such as a wind turbine blade control [14], as a DC motor speed control [15], as wind-diesel control [16], a vehicle steer control [8] [17], reconfiguration of the distribution network [18] [19]. In this work, the ANFIS and proportional integral derivative (PID) controller which is tuned with FA artificial intelligence to control water discharge at the water level in the tank is discussed clearly.…”

Section: Introductionmentioning

confidence: 99%

Design of Water Level Control Systems Using Pid and Anfis Based on Firefly Algorithm

Ali

Afandi

Parwati

et al. 2019

JEEMECS (Journal of Electrical Engineering, Mechatronic and Computer Science)

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At present, a controlling system of the fluid flow measurement is needed in industrial processes. Determination of the water quantity is also used to reduce the volume of water usage in the holding tank varies. The effort to overcome this problem has made using a system for regulating performances while a water flow based on the tank volume controlled using a PID system. This system uses a flow sensor to detect the water speed, an electric ball valve as an actuator. In principles, this tool is used to stabilize the output water speed per minute at a predetermined set point. This study is focused in five designed method comparison in associated with a water level without control, standard PID method, PID-Auto tuning method Matlab, PID-FA method, and ANFIS-PID-FA method. The results of the PID-FA training data are used as an input from ANFIS. From the simulation, it is also found that five control models have different performances while the ANFIS-PID-FA model is performed in the best control model. The ANFIS-PID-FA model has also the smallest overshot and undershot on the water level and output flow of 0.0021 pu, 0.0093 pu, 0.0072 pu, and 0.0096 pu.

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“…All three controllers are tuned using artificial intelligence, namely the method of Particle Swarm Optimization (PSO) and Firefly Algorithm (FA). Because, Artificial Intelligent (AI) is often used to develop various disciplines such as vehicle steering controllers [4] [5], as micro-hydro control [6], and others. In this study used the method Firefly Algorithm (FA) [7].…”

Section: Introductionmentioning

confidence: 99%

Comparison of LFC Optimization on Micro-hydro using PID, CES, and SMES based Firefly Algorithm

Kadaryono¹,

Rukslin²,

Ali³

et al. 2018

EECSI

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Micro-hydro gets potential energy from water flow that has a certain height difference. Potential energy is strongly influenced by high water fall. Potential energy through pipes, incoming turbines converted into kinetic energy. The kinetic energy of the turbine coupled with the generator is converted into electrical energy. Some components used for micro-hydro power generation, among others; intake, settling basin, headrace, penstock, turbine, draft tube, generator, and control panel. Water flows through the pipe into the turbine house so it can rotate the turbine blades. Turbine rotation is used to rotate a generator at the micro hydro generator. The most common problem with micro-hydro generating systems is inconsistent generator rotation caused by changes in connected loads. Load changes can cause system frequency fluctuations and may cause damage to electrical equipment. Artificial Intelligence (AI) is used to obtain the right constants to obtain the best optimization.

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The comparison of optimization for active steering control on vehicle using PID controller based on artificial intelligence techniques

Cited by 23 publications

References 13 publications

Are Self-Driving Vehicles Ready to Launch? An Insight into Steering Control in Autonomous Self-Driving Vehicles

Are Self-Driving Vehicles Ready to Launch? An Insight into Steering Control in Autonomous Self-Driving Vehicles

Design of Water Level Control Systems Using Pid and Anfis Based on Firefly Algorithm

Comparison of LFC Optimization on Micro-hydro using PID, CES, and SMES based Firefly Algorithm

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