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Published in Adedeji B. Badiru, Systems Engineering Models, 2019
Before proceeding it is critical to briefly review the vehicle dynamics concept of Sideslip, see Figure 9.1. Sideslip is the angular difference between a reference axis and the vehicle velocity vector at a specified point on the body. Sideslip is generally referenced relative to steering angle for the front axle, βf; or relative to vehicle centerline for the rear axle and center of gravity, βr and βcg respectively. Sideslip angle and sideslip rate are key driver cues for control of the vehicle and are primary factors in the subjective perception of transient performance [2].
Continuous- Time Kaiman Filter
Published in Frank L. Lewis, Lihua Xie, Dan Popa, Optimal and Robust Estimation, 2017
Frank L. Lewis, Lihua Xie, Dan Popa
(Sideslip is the angle in the horizontal plane between the relative wind and the aircraft longitudinal axis. It should be zero in straight and level flight to conserve fuel.) The pole at s = 0.004 corresponds to the unstable spiral mode. The complex-conjugate pole pair corresponds to the Dutch roll mode, which causes the nose of the aircraft to move in a damped figure-eight pattern with a period of 0.75 = 1/1.333s. The magnitude portion of the Bode plot for Equation 3.186 is given in Figure 3.11.
An efficient methodology for robust control of dynamic systems with interval matrix uncertainties
Published in International Journal of Systems Science, 2018
where a22 ∈ [ − 3.223, −2.637], a23 ∈ [ − 5.225, −4.275], a24 ∈ [0.702, 0.858], a31 ∈ [0.0774, 0.0946], a33 ∈ [ − 0.121, −0.099], a41 ∈ [0.006966, 0.010406], a42 ∈ [ − 0.0462, −0.0378], a43 ∈ [2.3276, 2.8522], a44 ∈ [ − 0.429, −0.351], b22 ∈ [ − 4.301, −3.519], b31 ∈ [0.0315, 0.0385], b41 ∈ [ − 2.7840, −2.2759] and b42 ∈ [0.279, 0.341] are all interval parameters with known lower and upper bounds. The inputs u1 and u2 represent the rudder and aileron deflections, while the state variables x1, x2, x3 and x4 are the bank angle, the derivative of the bank angle, the sideslip angle and the yaw rate, respectively.
An adaptive control architecture for cyber-physical system security in the face of sensor and actuator attacks and exogenous stochastic disturbances
Published in Cyber-Physical Systems, 2018
Xu Jin, Wassim M. Haddad, Tomohisa Hayakawa
The state vector , , contains the sideslip angle in deg, the roll rate in deg/sec and the yaw rate in deg/sec, respectively, and the control input , , contains the aileron command in deg and the rudder command in deg, respectively. Here, the state-dependent disturbance in (42) is used to capture perturbations in atmospheric drag [42]. Figure 2 shows a sample trajectory along with the standard deviation of the state trajectories x(t), , of the nominal system versus time for 30 sample paths. The mean control profile is also plotted in Figure 2.
Design of optimal sliding-mode control using partial eigenstructure assignment
Published in International Journal of Control, 2019
Ahmadreza Argha, Steven W. Su, Andrey Savkin, Branko Celler
Now we consider a two-input, two-output, fourth-order plant representing the motion of a Boeing B-747 aircraft obtained by linearisation around an operating condition of 20,000 ft. altitude with a speed of Mach 0.8 (Ishihara, Guo, & Takeda, 1992). The system matrices are as follows: and the system state, output and input vectors are where β(t), p(t), r(t), φ(t), δa(t) and δr(t) denote the sideslip angle, the roll rate, the yaw rate, the roll angle, the aileron deflection and the rudder deflection, respectively.