Over the past several decades, there has been a constant increase in the use of unmanned aircraft systems (UASs). Hence, there has also been a growth in the number of control algorithms to service many applications embodied by these vehicles. Initially, UASs were made popular by the military for reconnaissance, intelligence, surveillance, and target acquisition (RISTA) applications. Nowadays, UASs are used for everything from crop surveys to tourism. Nowhere is this more evident than with a multi-rotor unmanned aerial vehicle (UAV). This paper presents a survey of control methods for multi-rotor systems, namely quadrotors. In doing so, we hope to visualize a clear path to what additional capabilities might be needed in the future. In our examination, we review many of the notable research organizations and their efforts to expand the utility of multi-rotor aircraft. We also summarize the basic literature definitions and control strategies for autonomous quadrotors.

For linear and well-defined estimation problems with Gaussian white noise, the Kalman filter (KF) yields the best result in terms of estimation accuracy. However, the KF performance degrades and can fail in cases involving large uncertainties such as modeling errors in the estimation process. The smooth variable structure filter (SVSF) is a relatively new estimation strategy based on sliding mode theory and has been shown to be robust to modeling uncertainties. The SVSF makes use of an existence subspace and of a smoothing boundary layer to keep the estimates bounded within a region of the true state trajectory. Currently, the width of the smoothing boundary layer is chosen based on designer knowledge of the upper bound of modeling uncertainties, such as maximum noise levels and parametric errors. This is a conservative choice, as a more well-defined smoothing boundary layer will yield more accurate results. In this paper, the state error covariance matrix of the SVSF is used for the derivation of an optimal time-varying smoothing boundary layer. The robustness and accuracy of the new form of the SVSF was validated and compared with the KF and the standard SVSF by testing it on a linear electrohydrostatic actuator (EHA).

In this paper, the model predictive control (MPC) strategy is utilized in smart homes to handle the optimal operation of controllable electrical loads of residential end-users. In the proposed model, active consumers reduce their daily electricity bills by installing photovoltaic (PV) panels and battery electrical energy storage (BEES) units. The optimal control strategy will be determined by the home energy management system (HEMS), benefiting from the meteorological and electricity market data stream during the operation horizon. In this case, the optimal scheduling of home appliances is managed using the shrinking horizon MPC (SH-MPC) and the main objective is to minimize the electricity cost. To this end, the HEMS is augmented by the SH-MPC, while maintaining the desired operation time slots of controllable loads for each day. The HEMS is cast as a standard mixed-integer linear programming (MILP) model that is incorporated into the SH-MPC framework. The functionality of the proposed method is investigated under different scenarios applied to a benchmark system while both time-of-use (TOU) and real-time pricing (RTP) mechanisms have been adopted in this study. The problem is solved using six case studies. In this regard, the impact of the TOU tariff was assessed in Scenarios 1–3 while Scenarios 4–6 evaluate the problem with the RTP mechanism. By adopting the TOU tariff and without any load shifting program, the cost is $\$ $ 1.2274 while by using the load shifting program without the PV and BEES system, the cost would reduce to $\$ $ 0.8709. Furthermore, by using the SH-MPC model, PV system and the BEES system, the cost would reduce to $\$ $ -0.282713 with the TOU tariff. This issue shows that the prosumer would be able to make a profit. By adopting the RTP tariff and without any load shifting program, the cost would be $\$ $ 1.22093 without any PV and BEES systems. By using the SH-MPC model, the cost would reduce to $\$ $ 1.08383. Besides, by adopting the SH-MPC, and the PV and BEES systems, the cost would reduce to $\$ $ 0.05251 with the RTP tariff, showing the significant role of load shifting programs, local power generation, and storage systems.