**4.4 Intelligent adaptive control (artificial-neural-networks and fuzzy-logic controller)**

Two simulation scenarios were evaluated, with the CA cruising at varying speeds throughout the course. The airspeed and yaw angle convergence was seen in both circumstances. In, adaptable sliding mode control was compared to feedbacklinearization. The feedback controller proved very vulnerable to sensor noise but not robust, even with simplified dynamics. Under noisy conditions, the SMC operated effectively, and adaptability was able to anticipate uncertainty including ground effect [45]. As a result, nonlinear feedback-linearization control has good tracking yet poor disturbance rejection [46]. However, when feed-back linearization is combined with another approach that is less sensitive to noise, good results are obtained. The use of a trial and error strategy to tune input variables was, however, a key shortcoming of this study. The strategy was shown to be more effective in terms of achieving the target attitude as well as reducing weight drift [47]. To learn the whole dynamics of the CA, including unmodeled dynamics, outputting feedback control been implemented on a CA employing NN for leader-follower CA generation. From four control inputs, a virtual NN control was used to govern all 6DoF. In the context of a sinusoidal disturbance, an adaptive neural network approach was used to stabilise CAs. Decreased error function and so no weight drifts were achieved using the proposed technique of two simultaneous single hidden layers (**Figures 5** and **6**).

**Figure 5.** *On the CA, a schematic representation of FLC.*

*Robust Control Algorithm for Drones DOI: http://dx.doi.org/10.5772/intechopen.105966*

#### **5. Matlab-simulink result and comparison**

In this chapter, we display the Mat lab-Simulink findings and discuss the divergence between the various controllers shown above. The step-response of the endogenous variable x, y, z and ψ is shown for each control, followed by the double circular or elliptical trajectories along the simulated outcomes.

With LQR control is utilised, some distinctive characteristics of the step-response are shown using **Table 4** (**Figures 7** and **8**).

**Table 5** depicts the exact linearization position and yaw response with no interfering control by dynamic-feedback to a step-input (**Figure 9**).

**Table 6** Exhibits some typical features from the step response, while using dynamic inversion using zero-dynamics stabilisation control.

#### **5.1 Comparison**

Whenever different Controllers are used, the step-response of the dependent variable x, y, z, and ψ is shown in the diagram below (**Figure 10**).

**Tables 7**-**10** demonstrate some of the step response's characteristic parameters, where D-FBL denotes Dynamic-Feedback-Linearization and S-FBL denotes Static-Feedback-Linearization.


**Table 4.** *Distinctive characteristics of the step-response.*

**Figure 7.** *The LQR's Yaw and position for a step-input response.*

We can deduce the following from the information shown in these tables:

