Path following problem for patrolbot solved with fuzzy control

Serbencu, Adriana; Cernega, Daniela; Şerbencu, Adrian; Șușnea, Ioan

doi:10.1109/ical.2009.5262635

Cited by 2 publications

(2 citation statements)

References 4 publications

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“…Secondly, the stage from initial state (i.e. reaching stage or non-sliding stage) to sliding state system is only a feedback controller and hence robustness of the system is weakened to a great extend [8].…”

Section: Introductionmentioning

confidence: 99%

Improved PSO tuned Classical Controllers (PID and SMC) for Robotic Manipulator

Kapoor¹,

Ohri²

2015

IJMECS

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Due to simplicity and robustness, classical PID and SMC have been still widely used in practical applications. Performance of these controllers (PID and SMC) depends upon the value of some of the constant controller parameters. To avoid the most commonly used tedious trial and error method, this paper proposes an improved PSO based method for getting the optimized value of these parameters. For validation purpose these improved PSO tuned Proportional Integral Derivative (PID) and Sliding Mode (SMC) classical controllers have been applied for the motion control problem of the robotic manipulator. The chattering problem of SMC has been handled by using pseudo sliding function. Further results have been analyzed by comparing them with the basic conventional controllers. Results and conclusions are based on simulation results.

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Section: Introductionmentioning

confidence: 99%

Improved PSO tuned Classical Controllers (PID and SMC) for Robotic Manipulator

Kapoor¹,

Ohri²

2015

IJMECS

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“…Therefore neural network can be used to reduce chattering by study the uncertain upper bound adaptively [4,5]. Particle swarm optimization algorithm to optimize the parameters of sliding mode controller is superior to conventional experience to select the parameters, which can further enhance the performance of the sliding mode control system [6,7]. This paper introduces neural network and particle swarm optimization into sliding mode control, presents a new intelligence sliding mode control scheme for stabilized platform of rotary steering drilling tool.…”

Section: Introductionmentioning

confidence: 99%

An Intelligent Sliding Mode Control Scheme for Stabilized Platform of Rotary Steering Drilling Tool

Zhang

Jiang

2012

AMM

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Considering the nonlinear and uncertain influencing factors on stabilized platform of rotary steering drilling tool under work condition, a new intelligent sliding mode control strategy is presented for stabilized platform. The system robustness is ensured by sliding mode control. The upper bound of overall uncertainty is nonlinear approximated by RBF neural network, which can make the uncertain upper bound adjust adaptively. The chattering is reduced by quasi-sliding mode method. Finally, particle swarm optimization algorithm is applied to search the optimal controller parameters, including boundary layer thickness, switching function coefficient and adaptive parameter of neural network weight, Simulation results show that this control scheme can make stabilized platform get a good control performance and robustness.

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Path following problem for patrolbot solved with fuzzy control

Cited by 2 publications

References 4 publications

Improved PSO tuned Classical Controllers (PID and SMC) for Robotic Manipulator

Improved PSO tuned Classical Controllers (PID and SMC) for Robotic Manipulator

An Intelligent Sliding Mode Control Scheme for Stabilized Platform of Rotary Steering Drilling Tool

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