UKF-Based Neural Training for Nonlinear Systems Identification and Control Improvement

Guerra, Juan; Garcia-Hernandez, Ramon; Llama, Miguel A.; Santibáñez, Víctor

doi:10.1109/access.2022.3217816

Cited by 2 publications

(3 citation statements)

References 21 publications

(32 reference statements)

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“…Table 1 explains the implementation step-by-step and the comparison of the EKF and UKF. More extensive details and the stability analysis of the UKF training algorithm for neural identification are explained in [31].…”

Section: Unscented Kalman Filtermentioning

confidence: 99%

Neural Sliding Mode Control of a Buck-Boost Converter Applied to a Regenerative Braking System for Electric Vehicles

Ruz-Hernandez,

Garcia-Hernandez,

Ruz Canul

et al. 2024

WEVJ

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This paper presents the design and simulation of a neural sliding mode controller (NSMC) for a regenerative braking system in an electric vehicle (EV). The NSMC regulates the required current and voltage of the bidirectional DC-DC buck–boost converter, an element of the auxiliary energy system (AES), to improve the state of charge (SOC) of the battery of the EV. The controller is based on a recurrent high-order neural network (RHONN) trained using the extended Kalman filter (EKF) and the unscented Kalman filter (UKF) as the tools to train the neural networks to obtain a higher SOC in the battery. The performance of the controller with the two training algorithms is compared with a proportional integral (PI) controller illustrating the differences and improvements obtained with the EKF and the UKF. Furthermore, robustness tests considering Gaussian noise and varying of parameters have demonstrated the outcome of the NSMC over a PI controller. The proposed controller is a new strategy with better results than the PI controller applied to the same buck–boost converter circuit, which can be used for the main energy system (MES) efficiency in an EV architecture.

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Section: Unscented Kalman Filtermentioning

confidence: 99%

Neural Sliding Mode Control of a Buck-Boost Converter Applied to a Regenerative Braking System for Electric Vehicles

Ruz-Hernandez,

Garcia-Hernandez,

Ruz Canul

et al. 2024

WEVJ

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“…This section shows the proposed SI optimization approach for UKF learning of decentralized neural block control (DNBC-UKF) [16] applied to a 2-DOF robot manipulator.…”

Section: Decentralized Neural Block Control (Dnbc-ukf)mentioning

confidence: 99%

“…The robot actuators act as torque sources and receive analog voltage as a torque reference signal. Joint positions are obtained using incremental encoders that send information to a DAQ [16].…”

Section: Prototype Descriptionmentioning

confidence: 99%

A Comparative Study of Swarm Intelligence Metaheuristics in UKF-Based Neural Training Applied to the Identification and Control of Robotic Manipulator

Guerra,

Garcia-Hernandez,

Llama

et al. 2023

Algorithms

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This work presents a comprehensive comparative analysis of four prominent swarm intelligence (SI) optimization algorithms: Ant Lion Optimizer (ALO), Bat Algorithm (BA), Grey Wolf Optimizer (GWO), and Moth Flame Optimization (MFO). When compared under the same conditions with other SI algorithms, the Particle Swarm Optimization (PSO) stands out. First, the Unscented Kalman Filter (UKF) parameters to be optimized are selected, and then each SI optimization algorithm is executed within an off-line simulation. Once the UKF initialization parameters P0, Q0, and R0 are obtained, they are applied in real-time in the decentralized neural block control (DNBC) scheme for the trajectory tracking task of a 2-DOF robot manipulator. Finally, the results are compared according to the criteria performance evaluation using each algorithm, along with CPU cost.

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UKF-Based Neural Training for Nonlinear Systems Identification and Control Improvement

Cited by 2 publications

References 21 publications

Neural Sliding Mode Control of a Buck-Boost Converter Applied to a Regenerative Braking System for Electric Vehicles

Neural Sliding Mode Control of a Buck-Boost Converter Applied to a Regenerative Braking System for Electric Vehicles

A Comparative Study of Swarm Intelligence Metaheuristics in UKF-Based Neural Training Applied to the Identification and Control of Robotic Manipulator

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