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Safety path planning with obstacle avoidance using particle swarm optimization for agv in manufacturing layout
(2019-02-01)
Praserttaweelap, Rawinun
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Kaitwanidvilai, Somyot
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Aoyama, Hisayuki
In robotic systems, path planning is the one of important processes for robot motion. The best path planning is required for shortest path searching that can make fast movement of robot. However, the real environment is not only the path from point to point but it has obstacles which are the one of constraints for best path searching. The obstacle avoidance is concerned to avoid the crashing between robot and obstacle under environment. In Hard Disk Drive manufacturing, the first priority is safety constraint for non-collision and second priority is shortest path for processing time saving. This research designed the algorithm for path planning and obstacle avoidance for AGV in Hard Disk Drive Manufacturing of Seagate Technology (Thailand) Ltd by using particle swarm optimization. The fitness function on particle swarm optimization process for particle searching has been integrated with obstacle avoidance function to find the best path for robot without collision and total distance to find the shortest path. This algorithm is applied to verifying the model performance. The simulation results of this research are done by MATLAB 2016b and illustrate the good performance on different cases with controlled parameter.
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PSO based adaptive force controller for 6 DOF robot manipulators
(2017-01-01)
Thunyajarern, Sutthipong
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Seeboonruang, Uma
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Kaitwanidvilai, Somyot
Force control in robot arm has been used in many industrial applications especially end-effector contacting with environment. When environment is change, the performance of non-adaptive controller may be decreased. This paper presents adaptive force controller for 6 DOF (degree of freedom) Robot Manipulators that do not require identifying the environment before controlling. Particle swarm optimization (PSO) has been employed to solve this problem. In simulation, the end-effector was moved and touched different environments. The simulation results were compared with typical non-adaptive control. The result shows that when the environment is changed, the performance of non-adaptive force controller decreased. On the other hand, the performance of PSO based adaptive force controller remained the same.
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Specified order - H∞ loop shaping control for hard disk drive servo using pso
(2016-08-01)
Konghuayrob, Poom
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Kaitwanidvilai, Somyot
The demand of data storage density has been increasing continuously, but the data access time and track pitch need to be decreased. The high precision servo control system is required to achieve the predicted goal that will be crossing 10 TB/in<sup>2</sup> area, I density before 2020. Due to the fact that the precision of read/write head is sensitive to disturbance and noise, it is the challenging problem for the practical control. To overcome this problem, this paper proposes a new servo controller designed, specified order - H<inf>∞</inf> loop shaping (SOHLS) which adapts the particle swarm optimization (PSO) for searching the optimal controller parameters. The proposed technique can solve the problem of high order that was caused from the conventional H<inf>∞</inf> loop shaping (HLS), in addition, it can guarantee the robustness of the servo system,. Based on simulation results, the performances of the proposed SOHLS approach are investigated in comparison with that of the conventional H<inf>∞</inf> robust loop shaping, fixed-structure H<inf>∞</inf> control based on nonsmooth algorithm including reduced order controller by Hangkel norm model reduction technique. The proper repeatable runout (RRO) and non-repea table runout (NRRO) were applied, to the system to verify the robustness of all designed, controllers. The results demonstrate the advantages of the proposed SOHLS which gains better performance and more robustness than the other conventional controllers.
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PSO based decentralized robust power system stabilizer for a multi-machine power system
(2012-06-12)
Kaitwanidvilai, Somyot
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Ussawapusitgul, Chanchai
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Limcharoen, Worrakan
This paper presents a new technique to design a robust Power System Stabilizer (PSS) in multi-machine power system. The Particle Swarm Optimization (PSO) is adopted to find the optimal parameters of the predefined structure decentralized controller. The main objectives of this research are to reduce the oscillation and to increase the overall robust performance of the system. The proposed technique adopts the stability margin (ε) as the index of robustness and performance of the controlled system. In addition, the optimal weight selection, which is a difficult process of the robust loop shaping design, can be achieved by the proposed technique. In this paper, the performance of the proposed controller is investigated in the standard IEEE four generators system in comparison with the classical robust loop shaping control. The results of rotor speed deviation of generators by the proposed PSS and the conventional PSS verify the effectiveness of the proposed algorithm.
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Structure specified robust H∞ loop shaping control of a MIMO electro-hydraulic servo system using particle swarm optimization
(2011-07-26)
Olranthichachat, Piyapong
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Kaitwanidvilai, Somyot
A fixed-structure controller based on robust H∞ loop shaping control is proposed in this paper. It can be used to guarantee the robust performance under a structure-specified controller. In this proposed technique, Particle Swarm Optimization (PSO) is applied in the design controller, and the inverse of infinity norm from disturbances to states is formulated as the objective function in searching the optimal controller. Simulation results of MIMO electro-hydraulic servo system show that the proposed controller has simpler structure than that of the conventional H<inf>∞</inf> loop shaping controller and its stability margin is near the H<inf>∞</inf> loop shaping controllers.
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PSO based automatic weight selection and fixed structure robust loop shaping control for power system control applications
(2011-04-01)
Kaitwanidvilai, Somyot
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Olranthichachart, Piyapong
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Ngamroo, Issarachai
This paper proposes a new technique to design a fixed-structure robust loop shaping controller for the power system control applications. The proposed technique uses Particle Swarm Optimization (PSO) to find the optimal controller parameters so that the stability margin of controlled system is maximized. Infinity norm from disturbances to states is formulated as the cost function in our optimization. In addition, weighting function, which is normally difficult to obtain, is automatically determined by PSO. In this paper, the proposed technique is adopted to design the robust controllers for both two areas interconnected power system and VAR compensator connected electric power system. The performance of the proposed controller is investigated in comparison with conventional H∞ loop shaping controller, robust controller designed by LMI method and reduced order robust controller by Hankel norm model reduction method. As results indicated, the stability margin of the proposed controller is better than that of the controller designed by LMI method and the reduced order robust controller. In addition, the order of the proposed controller is much lower than that of the conventional robust loop shaping controller, making it easy to implement in practical works. ICIC International © 2011.
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Robust loop shaping-fuzzy gain scheduling control of a servo-pneumatic system using particle swarm optimization approach
(2011-01-01)
Kaitwanidvilai, Somyot
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Olranthichachat, Piyapong
In this paper, a new technique called robust loop shaping-fuzzy gain scheduled control (RLS-FGS) is proposed to design an effective nonlinear controller for a long stroke pneumatic servo system. In our technique, a nonlinear dynamic model of a long stroke pneumatic servo plant is identified by the fuzzy identification method and is used as the plant for our design. The structure of local controllers is selected as PID control which is proven by many research works that this type of control has many advantages such as simple structure, well understanding, and high performance. The proposed technique uses particle swarm optimization (PSO) to find the optimal local controllers which maximize the average stability margin. In addition, performance weighting function which is normally difficult to obtain is automatically determined by PSO. By the proposed technique, the RLS-FGS controller can be designed, and the structure of local controllers is still not complicated. As seen in the simulation and experimental results, our proposed technique is better than the classical gain scheduled PID controller tuned by pole placement and the conventional fuzzy PID controller tuned by ISE method in terms of robust performance. © 2010 Elsevier Ltd. All rights reserved.
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Harmonic reduction technique in PWM AC voltage controller using Particle Swarm Optimization and artificial neural network
(2010-12-01)
Piyarungsan, Pairoj
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Kaitwanidvilai, Somyot
This paper proposes a novel harmonic reduction technique for designing a Pulse Width Modulation (PWM) AC voltage controller. In the proposed technique, Total Current Harmonic Distortion (THD<inf>i</inf>), subjected to be minimized, is formulated in a cost function in an optimization problem; the optimal turn on and turn off angles in PWM waveform for a given output voltage are evaluated by Particle Swarm Optimization (PSO) technique. To apply our proposed technique for all output voltages, artificial neural network (ANN) is investigated to approximate the switching angles from sets of optimal angles evolved by PSO. Simulation results show that the proposed technique is suited for designing and gains a better performance compared to the conventional technique.
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Static VAR compensator using fixed structure static output feedback robust loop shaping control
(2010-12-01)
Kaitwanidvilai, Somyot
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Koisap, Chamnan
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Ngamroo, Issarachai
This paper proposes a new technique for designing a fixed-structure static output feedback robust loop shaping controller for a power system with VAR compensator. The proposed technique uses Particle Swarm Optimization (PSO) to evaluate the final solution. Infinity norm from disturbances to states is formulated as the cost function in our optimization. The performance of the designed system is investigated in comparison with the conventional H infinity loop shaping controller, the robust controller designed by LMI method and the reduced order robust controller by Hankel norm model reduction method. As results indicated, stability margin of our proposed controller is better than that of the others. In addition, the order of the proposed controller is much lower than that of the conventional robust loop shaping controller.
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Structured robust control for a Pmdc motor speed controller using swarm optimization and mixed sensitivity approach
(2010-12-01)
Kaitwanidvilai, Somyot
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Ngamroo, Issarachai
This paper proposes a new technique for designing a robust DC motor speed controller based on the concepts of fixed-structure robust controller and a mixed sensitivity method. Performance is specified by selecting the closed-loop objective weight, and uncertainties caused by the parameter changes of motor resistance, motor inductance and load are used to formulate the multiplicative uncertainty weight. Particle Swarm Optimization (PSO) is adopted to solve the optimization problem and find the optimal structured controller. The proposed technique can solve the problem of complicated and high order controller of conventional full order H <inf>∞</inf> controller and also retains the robust performance of conventional H <inf>∞</inf> optimal control. The performance and robustness of the proposed speed controller are investigated in a Permanent Magnet DC (PMDC) motor in comparison with the controllers designed by conventional H <inf>∞</inf> optimal control and conventional ISE method. Results of simulations demonstrate the advantages of the proposed controller in terms of simple structure and robustness against plant perturbations and disturbances. Experiments are performed to verify the effectiveness of the proposed technique. © Springer Science+Business Media B.V. 2010.

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