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The proposed controller is based on feedback linearization, and formation errors are guaranteed to be globally asymptotically stable.
By using the integration information of distributed formation errors, the control law improves the robustness of the formation.
This paper presents a fault-tolerant formation tracking (FTFT) scheme ensuring predesignated bounds of overshoot, convergence rate, and steady-state values of distributed formation errors for networked nonholonomic mobile robots in the presence of unexpected multiple actuator and system faults.
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The optimization study showed negligible error with experimentally obtained values at optimized conditions for maximizing NB reduction (error 3.37%) and aniline formation (error 3.93%).
This scheme gives a formation error representation that is independent of the number of agents in the formation and the resulting control algorithm is scalable.
A positive-definite function defined in terms of the formation error is then introduced and the Lyapunov stability theorem is used to obtain the cascade type guidance law.
Moreover, it is proven that under the designed event-triggered formation protocol, the multi-agent systems can achieve the desired time-varying formation which belongs to the feasible formation set with the bounded formation error and the closed systems do not exhibit Zeno behavior.
First, a full-order sliding mode surface in a decentralized manner is designed to incorporate both the individual position tracking error and the synchronized formation error while the UAV group is engaged in building a certain desired geometric pattern in three dimensional space.
These materials as well as possible patient movement during the examination could be responsible for the formation of errors in the resulting CBCT data.
The trajectory error is less than 2.5 km and the formation shape error is less than 2 km which meet the preset task request.
The trajectory error of each glider is less than 2.5 km, the formation shape error between each glider is less than 2 km, and the difference between actual energy consumption and the simulated energy consumption is less than 24% actual energy.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com