Indirect Adaptive Control Using Neural Network and Discrete Extended Kalman Filter for Wheeled Mobile Robot

Silaa, Mohammed Yousri; Bencherif, Aissa; Barambones, Oscar

doi:10.3390/act13020051

Cited by 3 publications

(1 citation statement)

References 47 publications

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“…WAF, MAF, and KF are suitable only for linear systems with Gaussian noise [26,27], while PF can solely mitigate the impact of Gaussian distribution on nonlinear systems [28,29]. EKF and UKF techniques are ineffective against non-Gaussian noise in nonlinear systems, potentially escalating computational complexity [30,31].…”

Section: Introductionmentioning

confidence: 99%

Enhancement of DDST-MFAC for tracking performance by using dynamic data reconciliation

Wang,

Syafiq Sadun,

et al. 2024

Meas. Sci. Technol.

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Model-free adaptive control (MFAC) stands out as an effective data-driven method for addressing nonlinear problems in industrial processes. To maintain good control performance, a data-driven set-point tuning (DDST) method is used to update the virtual set-point of the MFAC system. The DDST-based MFAC (i.e. DDST-MFAC) constantly approaches the target of the process through the nonlinear set-point tuning method. However, due to equipment errors and external interference, industrial sensors often suffer from measurement noise, which can have adverse effects on the control performance. In this article, an available dynamic data reconciliation (DDR) technique is adopted to improve the tracking performance of the DDST-MFAC, which suppresses the impact of process noise by using predicted information and measured data to achieve high-precision requirements for controlling nonlinear processes. Finally, considering both Gaussian and non-Gaussian distribution of measurement noise, the effectiveness of the proposed method was verified through the simulation of a nonlinear nonaffine plant. It is also applied to the steam-water heat exchange process, the control result is improved ultimately.

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Section: Introductionmentioning

confidence: 99%

Enhancement of DDST-MFAC for tracking performance by using dynamic data reconciliation

Wang,

Syafiq Sadun,

et al. 2024

Meas. Sci. Technol.

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Robust Adaptive Sliding Mode Control Using Stochastic Gradient Descent for Robot Arm Manipulator Trajectory Tracking

Silaa,

Barambones,

Bencherif

2024

Electronics

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This paper presents an innovative control strategy for robot arm manipulators, utilizing an adaptive sliding mode control with stochastic gradient descent (ASMCSGD). The ASMCSGD controller significant improvements in robustness, chattering elimination, and fast, precise trajectory tracking. Its performance is systematically compared with super twisting algorithm (STA) and conventional sliding mode control (SMC) controllers, all optimized using the grey wolf optimizer (GWO). Simulation results show that the ASMCSGD controller achieves root mean squared errors (RMSE) of 0.12758 for θ1 and 0.13387 for θ2. In comparison, the STA controller yields RMSE values of 0.1953 for θ1 and 0.1953 for θ2, while the SMC controller results in RMSE values of 0.24505 for θ1 and 0.29112 for θ2. Additionally, the ASMCSGD simplifies implementation, eliminates unwanted oscillations, and achieves superior tracking performance. These findings underscore the ASMCSGD’s effectiveness in enhancing trajectory tracking and reducing chattering, making it a promising approach for robust control in practical applications of robot arm manipulators.

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Closed-Form Continuous-Time Neural Networks for Sliding Mode Control with Neural Gravity Compensation

Urrea,

Garcia-Garcia,

Kern

2024

Robotics

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This study proposes the design of a robust controller based on a Sliding Mode Control (SMC) structure. The proposed controller, called Sliding Mode Control based on Closed-Form Continuous-Time Neural Networks with Gravity Compensation (SMC-CfC-G), includes the development of an inverse model of the UR5 industrial robot, which is widely used in various fields. It also includes the development of a gravity vector using neural networks, which outperforms the gravity vector obtained through traditional robot modeling. To develop a gravity compensator, a feedforward Multi-Layer Perceptron (MLP) neural network was implemented. The use of Closed-Form Continuous-Time (CfC) neural networks for the development of a robot’s inverse model was introduced, allowing efficient modeling of the robot. The behavior of the proposed controller was verified under load and torque disturbances at the end effector, demonstrating its robustness against disturbances and variations in operating conditions. The adaptability and ability of the proposed controller to maintain superior performance in dynamic industrial environments are highlighted, outperforming the classic SMC, Proportional-Integral-Derivative (PID), and Neural controllers. Consequently, a high-precision controller with a maximum error rate of approximately 1.57 mm was obtained, making it useful for applications requiring high accuracy.

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Indirect Adaptive Control Using Neural Network and Discrete Extended Kalman Filter for Wheeled Mobile Robot

Cited by 3 publications

References 47 publications

Enhancement of DDST-MFAC for tracking performance by using dynamic data reconciliation

Enhancement of DDST-MFAC for tracking performance by using dynamic data reconciliation

Robust Adaptive Sliding Mode Control Using Stochastic Gradient Descent for Robot Arm Manipulator Trajectory Tracking

Closed-Form Continuous-Time Neural Networks for Sliding Mode Control with Neural Gravity Compensation

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