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Numerical simulation and experimental identification of the hydraulic servomechanisms
Published in Nicolae Vasiliu, Daniela Vasiliu, Constantin Călinoiu, Radu Puhalschi, Simulation of Fluid Power Systems with Simcenter Amesim, 2018
Nicolae Vasiliu, Daniela Vasiliu, Constantin Călinoiu, Radu Puhalschi
The utility of the mathematical models developed for linear control systems is limited by a lot of nonlinearities existing in the design, manufacturing process, and operation in real conditions [10,11]. In practice, any linear analysis is followed by a long series of numerical simulations performed by different dedicated software. This chapter is devoted to an example of using Amesim for accomplishing the following phases of the design of a moving body servomechanism: Find the evolution of the main dynamic parameters during a transient generated by sine and step input signals.Study the influence of the main geometrical, hydraulic, and mechanical parameters on the dynamic performances.Identify the influence of the main design parameters on the frequency response applied at the input.
Control of Electromechanical Systems
Published in Sergey Edward Lyshevski, Mechatronics and Control of Electromechanical Systems, 2017
Device physics and dynamics of electromechanical motion devices, amplifiers, converters, and other components are modeled by nonlinear differential equations. For a few electromechanical devices, under assumptions and simplifications, linear differential equations were obtained from which transfer functions can be found. Linear systems can be described in the s and z domains using transfer functions Gsys(s) and Gsys(z). The Laplace operator s = d/dt and Laplace transforms are used for continuous-time systems. The transfer functions can be used to design control laws. For nonlinear systems, one cannot effectively apply linear control theory. However, many nonlinear electromechanical systems can be controlled using PID control laws that ensure adequate near-optimal performance.
Continuous-Time Systems
Published in Harold Klee, Randal Allen, Simulation of Dynamic Systems with MATLAB® and Simulink®, 2018
Another distinguishing property of linear systems is the way they respond to sinusoidal inputs. At steady state, the output of a linear system forced by a sinusoidal input with radian frequency ω0 is itself a sinusoid at the same frequency. In general, the output is shifted in time (out of phase) with respect to the input, and the amplitude is either attenuated or amplified compared to the amplitude of the input. This property is the foundation of linear AC steady-state analysis and the design of linear control systems by the method of frequency response. In the case of nonlinear systems, the output includes harmonics (sinusoidal terms at frequencies nω0, n = 1, 2, 3, …).
Dynamic mode decomposition type algorithms for modeling and predicting queue lengths at signalized intersections with short lookback
Published in Journal of Intelligent Transportation Systems, 2023
Kazi Redwan Shabab, Shakib Mustavee, Shaurya Agarwal, Mohamed H. Zaki, Sajal K. Das
Uncovering complex traffic dynamics from high-fidelity data requires “interpretable” data-driven techniques. Dynamic mode decomposition (DMD) is a purely data-driven technique that estimates a locally linear representation of complex nonlinear dynamics. The critical point about the method is that it does not require any prior information about the system or its internal physics to capture its dynamics. Hence, it is comparable to gray box models of system identification. DMD and related algorithms provide approximate system identification, unlike purely data-driven statistical models, and machine learning algorithms. The identification of complex dynamical systems as approximate linear dynamics has several benefits. Among them is the simplicity in understanding the system, short-term prediction, pattern identification, and the applicability of linear control algorithms. There have been a few studies attempting to utilize DMD-based algorithms for applications in ITS. DMD-based algorithm was used in image-based traffic flow visualization (Schmid, 2011), short-term traffic flow prediction (Yu et al., 2021), highway traffic dynamics analysis (X. Wang & Sun, 2023), and estimation of traffic mobility pattern (Li & Yang, 2022).