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Advance Modeling of a Skid-Steering Mobile Robot for Remote ...

Advance Modeling of a Skid-Steering Mobile Robot for Remote ...

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= −p ax A z ω 2 n ze −ξ zω nz sin( √ 1 − ξ 2 z ω nz t + B z + Φ ) (5.8)5.2 Identification <strong>of</strong> the Tire Dynamic Parameters 71a x = g sin(ψ) ≈ gψa y = −g sin(θ) ≈ −gθa y = −p ax ¨φ= −p ax A z ω 2 n ze −ξ zω nz[(2ξ 2 z − 1) sin ( √ 1 − ξ 2 z ω nz t + B z)− 2ξz√1 − ξ 2 z cos ( √ 1 − ξ 2 z ω nz t + B z) ]where g is the gravitational acceleration and the first equality come from the linear property<strong>of</strong> trigonometric functions such that: a sin x + b cos x = √ a 2 + b 2 sin(x + Φ), wheretan Φ = b . The approximations come from the first order Taylor expansion <strong>of</strong> the sin() func-ation, which makes sense <strong>for</strong> ¨small¨ values <strong>of</strong> θ, ψ.By looking at the relations in (5.8), we can claim, with a good approximation, that the signalsmeasured from the accelerometers differ from the free responses in (5.7) only by their amplitudesand eventually phases. Thereby. the Roll, Pitch and Yaw angular natural frequencyω inand damping ratio ξ i can be estimated by looking at the frequency <strong>of</strong> the oscillationsmeasured from the accelerometers and the rate at which they decay. To this purpose, thedata from the accelerometers are acquired eight times per each motion (Roll, Pitch and Yaw)and analyzed by using the Matlab functions defined in Appendix A. In particular, the datacontain 2 12 samples and are acquired with a sample frequency <strong>of</strong> 1 kHz, but only the first 2 11samples are analyzed (tRange = ′ beginnig ′ ), in order to cut <strong>of</strong>f possible disturbs present inthe last part <strong>of</strong> the data where the system has already reached the equilibrium. Moreover, thedata are lowpass-filtered at 20 Hz (fRange = [1, 20]) by using the function filterData()and the peak frequency is found by using the function findPeakFreq().Figure 5.4, 5.5, 5.6 depict three examples <strong>of</strong> the acquired data and their FFT respectively <strong>for</strong>the Roll, Pitch and Yaw motion.Tab.5.2 shows all the peak frequencies found <strong>for</strong> each recorded data. As the standarddeviation is relatively small in all the three cases ( std( f peak)mean( f peak )

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