The antlion optimizer (ALO) algorithm is used in this article for maximum power point tracking (MPPT) of a solar array. The solar array consists of a single module, while there are 20 cells in the module. The voltage and current ratings of each cell are 2 V and 2.5 A, making a 100 W array in ideal condition. However, the voltage and current characteristics of the PV cell are unable to achieve maximum power. Therefore, the ALO was used for MPPT. The results of the ALO are compared with the traditional metaheuristic approaches, perturb and observe ($P\&O$) and flower pollination (FP) algorithms. Comparison of the ALO with the stated algorithms is conducted for two cases: when solar irradiance is 1000 W/m${}^2$ and when it drops to 200 W/m${}^2$ at first then reaches 1000 W/m${}^2$. The change of irradiance is performed to simulate the partial shading condition. The simulation results depict that maximum power for the first case using the ALO reaches 91.3 W in just 0.05 s, while the $P\&O$ and PFA reach 90 W after 0.64 and 2 s, respectively. For the case of partial shading, maximum power using the ALO drops to 55 W when irradiance decreases to 200 W/m${}^2$ and then increases with the increase in irradiance reaching 91.3 W which clearly shows that the ALO outperforms the P&O and FPA.

The robotic airship can provide a promising aerostatic platform for many potential applications. These applications require a precise autonomous trajectory tracking control for airship. Airship has a nonlinear and uncertain dynamics. It is prone to wind disturbances that offer a challenge for a trajectory tracking control design. This paper addresses the airship trajectory tracking problem having time varying reference path. A lumped parameter estimation approach under model uncertainties and wind disturbances is opted against distributed parameters. It uses extended Kalman filter (EKF) for uncertainty and disturbance estimation. The estimated parameters are used by sliding mode controller (SMC) for ultimate control of airship trajectory tracking. This comprehensive algorithm, EKF based SMC (ESMC), is used as a robust solution to track airship trajectory. The proposed estimator provides the estimates of wind disturbances as well as model uncertainty due to the mass matrix variations and aerodynamic model inaccuracies. The stability and convergence of the proposed method are investigated using the Lyapunov stability analysis. The simulation results show that the proposed method efficiently tracks the desired trajectory. The method solves the stability, convergence, and chattering problem of SMC under model uncertainties and wind disturbances.

This paper presents an LPV output feedback control strategy for a 4-DOF control moment gyroscope (CMG). The LPV model constructed by linearizing the nonlinear model around moving operating points contains fifteen scheduling variables causing a massive computational complexity involved in controller synthesis. A practical iterative approach based on kernel principal component analysis (kernel-PCA) is proposed for reduction of scheduling variables and LPV controller synthesis. LPV controller designed for a fixed rotor speed is applied in feedback to a full nonlinear model of the CMG at different rotor speeds. The simulation results show that the controller obtained through the proposed method ensures improved performance in terms of increased operating range for CMG with reduced control effort while maintaining the transient performance and tracking accuracy.

Two wheeled Self-Balancing Segway, which works on the principle of inverted pendulum, is naturally unstable, nonlinear, and under actuated system. Self-Balancing means the capability of the Segway to balance itself on two wheels without falling. Therefore, the system has to be controlled to reach stability in this unstable state. The two wheeled Segway is considered important due to its applications in daily life. In this paper, a hybrid control system is proposed for upward stabilization and control of two wheeled Segway. The control design approaches proposed in the literature for Segway result in a large control effort which requires high torque causing the saturation of actuator field and ultimately failure of the controller. Controller designed using the proposed approach is able to reduce the control effort by 64% compared to the ones available in literature. Moreover, controller designed through the proposed approach is able to improve disturbance rejection for both pitch and yaw angles of the Segway. Simulation results illustrate the effectiveness of the proposed approach over the ones available in the literature.