Results 241 to 250 of about 3,412,246 (356)

Wearable exoskeleton robot control using radial basis function‐based fixed‐time terminal sliding mode with prescribed performance

open access: yesAsian Journal of Control, EarlyView.
Abstract This paper tackles the problem of robust and accurate fixed‐time tracking in human–robot interaction and deals with uncertainties. This work introduces a control approach for a wearable exoskeleton designed specifically for rehabilitation tasks.
Mahmoud Abdallah   +4 more
wiley   +1 more source

Mental construction in mathematical proof

open access: gold, 2019
I Wayan Eka Mahendra   +2 more
openalex   +1 more source

On qualitative analysis of an ecological dynamics with time delay

open access: yesAsian Journal of Control, EarlyView.
Abstract In this paper, we study a fractional‐order predator–prey system with time delay, where the dynamics are logistic with prey population commensurate to the carrying capacity. Mainly, by linearizing the system around the equilibrium point, we first analyze the stability and then prove the existence of Hopf bifurcation.
Canan Celik, Kubra Degerli
wiley   +1 more source

Fractional‐order controller tuning via minimization of integral of time‐weighted absolute error without multiple closed‐loop tests

open access: yesAsian Journal of Control, EarlyView.
Abstract This study presents a non‐iterative tuning technique for a linear fractional‐order (FO) controller, based on the integral of the time‐weighted absolute error (ITAE) criterion. Minimizing the ITAE is a traditional approach for tuning FO controllers. This technique reduces the over/undershoot and suppresses the steady‐state error. In contrast to
Ansei Yonezawa   +4 more
wiley   +1 more source

Go-or-grow models in biology: a monster on a leash. [PDF]

open access: yesJ Math Biol
Thiessen R   +3 more
europepmc   +1 more source

Risk‐aware safe reinforcement learning for control of stochastic linear systems

open access: yesAsian Journal of Control, EarlyView.
Abstract This paper presents a risk‐aware safe reinforcement learning (RL) control design for stochastic discrete‐time linear systems. Rather than using a safety certifier to myopically intervene with the RL controller, a risk‐informed safe controller is also learned besides the RL controller, and the RL and safe controllers are combined together ...
Babak Esmaeili   +2 more
wiley   +1 more source

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