Trajectory tracking of multi-legged robot based on model predictive and sliding mode control

Gao, Yong; Wu, Wei; Wang, Xinmei; Wang, Dongliang; Li, Yanjie; Yu, Qiuda

doi:10.1016/j.ins.2022.05.069

Cited by 28 publications

(5 citation statements)

References 41 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These include high computational demands, limitations in intricate environments, dependence on training data quality, and specialized designs that compromise versatility [88], [99], [100]. To address these challenges, proposed solutions involve integrating multiple control strategies like MPC, optimal trajectory planning, and reinforcement learning [101]- [104]. Hierarchical control approaches have also emerged to improve robot arm collaboration, enabling efficient multitasking while preventing collisions [105].…”

Section: Stability and Locomotion In Bipedal Wheel-legged Robotsmentioning

confidence: 99%

“…Notably, these advancements' primary focus is enhancing control strategies, allowing robots to manage intricate transitions and navigate unforeseen environments seamlessly [51], [74], [75], [181], [182]. Central to this progression is integrating predictive control, trajectory planning, and reinforcement learning, all contributing synergistically to optimize robotic performance [90], [91], [103], [104]. Furthermore, by factoring in the intricacies of diverse terrains and varied operational conditions, the practicality and versatility of these robots in real-world settings become increasingly evident [78], [80], [92], [93], [99], [136], [138], [183]- [185].…”

Section: Future Directions Of Control S and Design In Bipedal Wheel-l...mentioning

confidence: 99%

See 1 more Smart Citation

Evolution, Design, and Future Trajectories on Bipedal Wheel-legged Robot: A Comprehensive Review

Mansor,

Irawan,

Abas

2023

IJRCS

View full text Add to dashboard Cite

This comprehensive review delves into the realm of bipedal wheel-legged robots, focusing on their design, control, and applications in assistive technology and disaster mitigation. Drawing insights from various fields such as robotics, computer science, and biomechanics, it offers a holistic understanding of these robots' stability, adaptability, and efficiency. The analysis encompasses optimization techniques, sensor integration, machine learning, and adaptive control methods, evaluating their impact on robot capabilities. Emphasizing the need for adaptable, terrain-aware control algorithms, the review explores the untapped potential of machine learning and soft robotics in enhancing performance across diverse operational scenarios. It highlights the advantages of hybrid models combining legged and wheeled mobility while stressing the importance of refining control frameworks, trajectory planning, and human-robot interactions. The concept of integrating soft and compliant mechanisms for improved adaptability and resilience is introduced. Identifying gaps in current research, the review suggests future directions for investigation in the fields of robotics and control engineering, addressing the evolution and terrain adaptability of bipedal wheel-legged robots, control, stability, and locomotion, as well as integrated sensory and perception systems, microcontrollers, cutting-edge technology, and future design and control directions.

show abstract

Section: Stability and Locomotion In Bipedal Wheel-legged Robotsmentioning

confidence: 99%

Section: Future Directions Of Control S and Design In Bipedal Wheel-l...mentioning

confidence: 99%

Evolution, Design, and Future Trajectories on Bipedal Wheel-legged Robot: A Comprehensive Review

Mansor,

Irawan,

Abas

2023

IJRCS

View full text Add to dashboard Cite

show abstract

“…Their approach enhanced performance in the presence of uncertainties, disturbances, and without the need for time-consuming tuning and analyzed the stability using the Lyapunov direct method. Gao et al [22] proposed a control strategy to achieve accurate tracking control of multi-legged robots by decomposing trajectory tracking into body-level and limb-level sub-control systems, utilizing a MPC strategy for the body-level subsystem and a robust adaptive terminal SMC for the limb-level subsystem, which greatly improves tracking accuracy and demonstrates omnidirectional mobility in various scenarios. Ma et al [23] presented a new reaching law-based control approach combining a sliding-mode disturbance observer and an enhanced two-vector model predictive current control enhances the dynamic performance and robustness of the permanent magnet synchronous motor to system disturbances.…”

Section: -Introductionmentioning

confidence: 99%

“…) Referring to the equations(22) and(23), we can conclude: 𝐼(𝑘 + 1) = 𝑎𝐼(𝑘) + 𝑇 𝑠 𝑢(𝑘)(24) The tracking error of the reference current in the proposed MPC controller is defined as follows:𝑒 𝐼 (𝑘) = 𝐼 𝑟 (𝑘) − 𝐼(𝑘)(25) where 𝑒 𝐼 (𝑘) is the tracking error of the MPC controller. Considering the equations (24) and (25), we can predict the flow tracking error in the upcoming moments: { 𝑒 𝐼 (𝑘) = 𝐼 𝑟 (𝑘) − 𝐼(𝑘) 𝑒 𝐼 (𝑘 + 1) = 𝐼 𝑟 (𝑘 + 1) − 𝐼(𝑘 + 1) = 𝐼 𝑟 (𝑘 + 1) − 𝑎𝐼(𝑘) + 𝑇 𝑠 𝑢(𝑘) 𝑒 𝐼 (𝑘 + 2) = 𝐼 𝑟 (𝑘 + 2) − 𝑎 2 𝐼(𝑘) − 𝑎𝑇 𝑠 𝑢(𝑘) − 𝑇 𝑠 𝑢(𝑘 + 1) ⋮ 𝑒 𝐼 (𝑘 + 𝑁) = 𝐼 𝑟 (𝑘 + 𝑁) − 𝑎 𝑁 𝐼(𝑘) − 𝑇 𝑠 ∑ 𝑎 𝑁−1−𝑖 𝑁−1 𝑖=0𝑢(𝑘 + 𝑖)…”

mentioning

confidence: 99%

Model Predictive Sliding Mode Control of Direct Current Motors

Eghbal,

Seyedeh-Solmaz

2024

Preprint

View full text Add to dashboard Cite

In this paper, a cascade controller is designed for controlling the speed of a direct current motor, which includes a combination of a model predictive controller and a sliding mode controller. The main goal is to precisely control the speed and angle of the motor shaft by adjusting the input voltage, taking into account the voltage limitations. We design a model predictive sliding mode controller in such a way that in the inner loop, the model predictive controller controls the voltage to follow the reference current, and in the outer loop, the sliding mode controller generates the reference current to follow the desired speed. The sliding mode controller, with its resistance to external disturbances, reduces their effects and the uncertainties of modeling. By extracting the reference current based on the speed tracking error and the presence of uncertainty, the optimal terminal voltage of the motor is predicted in the inner loop, leading to the tracking of the desired rotational speed by the motor. This paper also proves the stability of the system in the presence of the motor terminal input voltage constraint, and the simulation results confirm the effectiveness of the proposed controller in dealing with uncertainty and external disturbances.

show abstract

“…In recent years, robotic manipulation has also expanded to a much broader scope, including manipulation in artistic applications [2] and anthropic environments [3], micro-and nanoscales manipulation [4] and swarm manipulation [5]. With the increasing demand for rapid response and high-precision control of robot manipulators, the improvement of tracking performance remains an attractive and open problem [6,7].…”

Section: Introductionmentioning

confidence: 99%

Model-free adaptive trajectory tracking control of robotic manipulators with practical prescribed-time performance

Huang,

Dong,

Yang

et al. 2023

Nonlinear Dyn

View full text Add to dashboard Cite

Considering accurate model information of robotic manipulators is hard to obtain in real applications, this article proposes a model-free trajectory tracking control scheme for the robotic system with input saturation, where a pre-defined tracking precision within prescribed time under any initial conditions is ensured. Firstly, to avoid a sharp corner when control input exceeds constraint, a new smooth function is used to approximate the saturated control torque and then a new robotic system model is built. Based on the rebuilt system, a time-delay estimation (TDE) method is used to estimate the lumped uncertainty containing unknown dynamics and external disturbance. Furthermore, a model-free adaptive controller is developed to ensure the tracking error converge to a preset small residual within given time, where adaptive law and auxiliary system are used to cope with the TED estimation error and control input saturation, respectively. With the developed practical prescribed-time function, there is no constraint on the initial value of the tracking error, thus global stability is guaranteed. Finally, a planar two-link robotic manipulator is simulated to show the effectiveness of the developed control scheme.

show abstract

Trajectory tracking of multi-legged robot based on model predictive and sliding mode control

Cited by 28 publications

References 41 publications

Evolution, Design, and Future Trajectories on Bipedal Wheel-legged Robot: A Comprehensive Review

Evolution, Design, and Future Trajectories on Bipedal Wheel-legged Robot: A Comprehensive Review

Model Predictive Sliding Mode Control of Direct Current Motors

Model-free adaptive trajectory tracking control of robotic manipulators with practical prescribed-time performance

Contact Info

Product

Resources

About