Frame Micro-Optoelectromechanical Angular Velocity Transducer with Optical Readout Units Based on the Optical Tunneling Effect

Бусурин, В. И.; Kazaryan, A. V.; Shtek, S. G.; Zheglov, M. A.; Vasetskiy, S. O.; Kyi, Phay L.

doi:10.1007/s11018-022-02088-3

Cited by 4 publications

(7 citation statements)

References 9 publications

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“…Under these conditions, the differentiator variables (18) generate the achievable trajectory in signal form and its first and second derivatives. These variables are new reference influences and are used to generate control influences u 1 , u 2 in the tracking system of the mobile robot.…”

Section: Designing a Three-block Tracking Differentiatormentioning

confidence: 99%

“…To design a tracking differentiator (18) with the indicated features by means of nongenerated replacement of variables, we proceed to a new coordinate frame, which is formed by the tracking errors and sigmoidal local connections:…”

Section: Designing a Three-block Tracking Differentiatormentioning

confidence: 99%

“…Let us first formulate sufficient conditions of the boundedness of solutions of the system (22) without considering (19). To simplify the setting, let us establish the following initial conditions in the tracking differentiator (18):…”

Section: Designing a Three-block Tracking Differentiatormentioning

confidence: 99%

“…In Equation (28) are sufficient conditions under which inequalities (25) will be fulfilled at t ∈ [0; T]. Now, let us introduce constraints on the state variables of the tracking differentiator (18), its corrective influences, and the rate of their change. Considering (25) and the inverse Equation (20), namely z 2 = e 2 − p 1 σ(l 1 e 1 ), z 3 = e 3 − p 2 σ(l 2 e 2 ), we obtain a system of dual inequalities.…”

Section: Designing a Three-block Tracking Differentiatormentioning

confidence: 99%

“…In traditional applications, this computational algorithm is used to process the sensor signal of a state variable of the control plant and recover its first derivative. This allows no installation of velocity or acceleration sensors (they usually produce noisy signals [18]).…”

Section: Introductionmentioning

confidence: 99%

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Generation of Achievable Three-Dimensional Trajectories for Autonomous Wheeled Vehicles via Tracking Differentiators

Krasnova,

Kokunko,

Kochetkov

et al. 2023

Algorithms

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Planning an achievable trajectory for a mobile robot usually consists of two steps: (i) finding a path in the form of a sequence of discrete waypoints and (ii) transforming this sequence into a continuous and smooth curve. To solve the second problem, this paper proposes algorithms for automatic dynamic smoothing of the primary path using a tracking differentiator with sigmoid corrective actions. Algorithms for setting the gains of the differentiator are developed, considering a set of design constraints on velocity, acceleration, and jerk for various mobile robots. When tracking a non-smooth primary path, the output variables of the differentiator generate smooth trajectories implemented by a mechanical plant. It is shown that the tracking differentiator with a different number of blocks also generates derivatives of the smoothed trajectory of any required order, taking into account the given constraints. Unlike standard analytical methods of polynomial smoothing, the proposed algorithm has a low computational load. It is easily implemented in real time on the on-board computer. In addition, simple methods for modeling a safety corridor are proposed, taking into account the dimensions of the vehicle when planning a polygon with stationary obstacles. Confirming results of numerical simulation of the developed algorithms are presented.

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Section: Designing a Three-block Tracking Differentiatormentioning

confidence: 99%

Section: Designing a Three-block Tracking Differentiatormentioning

confidence: 99%