Path following control of tracked vehicle using modified sup controller optimized with particle swarm optimization (PSO)

Subari, Muhammad Akhimullah; Hudha, Khisbullah; Kadir, Zulkiffli Abd; Dardin, S.; Amer, Noor Hafizah

doi:10.1007/s40435-021-00900-6

Cited by 6 publications

(6 citation statements)

References 24 publications

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“…The percentage errors are then calculated based on the difference in force produced as tabulated in Table 5. From the overall results, the percentage error obtained is less than 13 % where within the acceptable range of error [22]. Fig.…”

Section: Extension Casementioning

confidence: 52%

See 1 more Smart Citation

Impact Behaviour Modelling of Magnetorheological Elastomer Using a Non-Parametric Polynomial Model Optimized with Gravitational Search Algorithm

Zubir,

Hudha,

Abd. Kadir

et al. 2023

sv-jme

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This paper presents an approach to model the impact behaviour of a dual-acting magnetorheological elastomer (MRE) damper using 4th-order polynomial functions optimized with a gravitational search algorithm. MRE is a type of smart material that can change its mechanical properties in response to an injected current, making it well-suited for a wide range of applications such as vibration absorption, noise cancellation, and shock mitigation. The proposed model uses a combination of polynomial functions designed to predict the nonlinearity of MRE during compression and extension stages. The model is tuned and validated using experimental data from impact tests conducted on the MRE damper under various currents. The results indicate that the developed model can accurately track the impact behaviour of MRE with minimum error. Additionally, an interpolation model is proposed to estimate the appropriate forces for median currents. The interpolation model predicts the force between the upper and lower currents, demonstrating the model's ability to predict MRE behaviour accurately. The main contribution of this study is proposing a non-parametric model of MRE that is able to identify the hysteretic behaviour of the MRE based on specific current applied. In addition, an interpolation model is introduced in this study to cover not only the input current starting from 0 A to 2 A but also the intermediate current such as 0.3 A, 0.7 A, 1.3 A and 1.7 A.

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Section: Extension Casementioning

confidence: 52%

“…11, and their values are tabulated in Table 6. From the percentage of error, it can be seen that the error is less than 20 %, which is within an acceptable range of error [22].…”

mentioning

confidence: 57%

Impact Behaviour Modelling of Magnetorheological Elastomer Using a Non-Parametric Polynomial Model Optimized with Gravitational Search Algorithm

Zubir,

Hudha,

Abd. Kadir

et al. 2023

sv-jme

View full text Add to dashboard Cite

show abstract

“…The learning rate β was reduced to 0.002 for the critic network, and the discount factor γ was changed to 0.98. The number of obstacles k was set at two, with centers at [10,0] and [47, −10] around the waypoints of the reference path, and the common parameters r obs , r c , r e were set to 3.5 m, 5.5 m, and 7.5 m, respectively. The parameters φ rz and φ pz were set to 100 and 10, as scores for the reward and penalty, respectively.…”

Section: The Performance Of Path-following With Collision Avoidancementioning

confidence: 99%

“…A nonsingular terminal sliding mode control scheme was constructed to solve the problem of the omnidirectional mobile robot with mecanum wheels [2]. There are also numerous intelligent computing methods that have been widely used in the research field [10][11][12]. Moreover, with the boom in artificial intelligence technology in recent years, investigations based on machine learning are emerging in the control field, especially for path-following problems [13], etc.…”

Section: Introductionmentioning

confidence: 99%

Path-Following and Obstacle Avoidance Control of Nonholonomic Wheeled Mobile Robot Based on Deep Reinforcement Learning

et al. 2022

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In this paper, a novel path-following and obstacle avoidance control method is given for nonholonomic wheeled mobile robots (NWMRs), based on deep reinforcement learning. The model for path-following is investigated first, and then applied to the proposed reinforcement learning control strategy. The proposed control method can achieve path-following control through interacting with the environment of the set path. The path-following control method is mainly based on the design of the state and reward function in the training of the reinforcement learning. For extra obstacle avoidance problems in following, the state and reward function is redesigned by utilizing both distance and directional perspective aspects, and a minimum representative value is proposed to deal with the occurrence of multiple obstacles in the path-following environment. Through the reinforcement learning algorithm deep deterministic policy gradient (DDPG), the NWMR can gradually achieve the path it is required to follow and avoid the obstacles in simulation experiments, and the effectiveness of the proposed algorithm is verified.

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“…M.A. Subari [11] proposed a Sup-controller based on the kinematics model of the tracked vehicle, which combined the PID controller and skidding mode controller and used the particle swarm optimization algorithm. These improved kinematics model-based controllers usually apply complex algorithms, which can bring challenges to the performance and calculation speed of the control core board.…”

Section: Introductionmentioning

confidence: 99%

Path Tracking of Underground Mining Boom Roadheader Combining BP Neural Network and State Estimation

Teng

et al. 2022

Applied Sciences

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This paper proposes a path correction scheduling strategy for the underground mining boom roadheader by ably combining a back propagation (BP) neural network and state estimation. First, a pose deviation-based tracking model is designed for the roadheader, and it is then further studied and optimized by incorporating the benefits of BP neural networks into the model adaptation. Considering the fact that there is skidding between tracks on the ground and errors during the instant pose detection of the roadheader underground, singular value decomposition (SVD)–Unscented Kalman filtering is applied to estimate the real pose deviation, based on the summarized distribution regularities of the track skidding ratios and the pose detection errors, instead of complicated analysis mechanisms. The BP neural network and states estimation are well combined in structure, enabling this scheduling strategy to update the control law and revise the control instruction simultaneously in the procedure. The proposed path tracking model for the roadheader is simple and clear, without adding extra devices or massive algorithms, which is attractive in terms of industrial use. The path tracking simulations show that this proposed strategy achieves path tracking well in different scenarios and is of high adaptability when facing complex trajectory while still giving stable control instructions for the roadheader.

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Path following control of tracked vehicle using modified sup controller optimized with particle swarm optimization (PSO)

Cited by 6 publications

References 24 publications

Impact Behaviour Modelling of Magnetorheological Elastomer Using a Non-Parametric Polynomial Model Optimized with Gravitational Search Algorithm

Impact Behaviour Modelling of Magnetorheological Elastomer Using a Non-Parametric Polynomial Model Optimized with Gravitational Search Algorithm

Path-Following and Obstacle Avoidance Control of Nonholonomic Wheeled Mobile Robot Based on Deep Reinforcement Learning

Path Tracking of Underground Mining Boom Roadheader Combining BP Neural Network and State Estimation

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