The significance of Bodes Ideal Transfer Function (BITF) in control theory has motivated researchers to explore new ways to harness its beneficial properties. In this work, we present a novel control strategy that integrates Input-Output Feedback Linearization (IOFL) with a Fractional Integral Controller (FIC) to achieve enhanced system performance. The proposed approach is applied to a Two-Input Two-Output (TITO) nonlinear system, where IOFL is first utilized to linearize and decouple the system into two independent pure integrator subsystems. Subsequently, a newly designed fractional-order integral controller, based on BITF, is implemented to ensure precise reference tracking. To validate the effectiveness of this method, we apply it to a TITO coupled tank (TITOCT) process and compare its performance with Nonlinear Continuous-Time Generalized Predictive Control (NCGPC).

In this paper, feedback linearization and linear observer with a new analytical fractional-order proportional integral controller (FPIC) design method for controlling a double-integrating system is proposed. This method is based on Bode’s Ideal Transfer Function (BITF) and frequency domain specifications. For the design of the FPIC, the open loop transfer function associated the double integrating system and the FPIC is referenced to Bode’s ideal transfer function to have a constant phase margin. This property ensures the stability of the closed-loop system and robustness against gain variations. The proposed FPIC approach is successfully applied to the two-tank process. The effectiveness and tracking performance of the control law is demonstrated through simulation and experimental tests.

This paper presents an adaptive model-free control strategy designed for trajectory tracking of a 2-DOF Quanser Aero 2 helicopter. The approach employs a fuzzy logic system to approximate a model-based control law, effectively addressing system uncertainties and specific disturbances. The stability analysis of the closed-loop system is carried out using Lyapunov's theorem, guiding the adaptive law to adjust the parameters of the fuzzy system. It is proven that all signals within the closed-loop system remain bounded, and the tracking error converges to a small region near the origin. The proposed adaptive approach is successfully applied to control pitch and yaw angles. Through simulation and experimental tests, the effectiveness and tracking performance of the control law are demonstrated.

Currently, a major obstacle faced by multirotor unmanned aerial vehicles (UAVs) is the limitation of embedded energy. The research community has been exploring this issue through the analysis of design, control, and planning methods. Most of these strategies rely on comprehending the interrelated sub-dynamics of the DAV system, encompassing factors like propeller aerodynamics, electro-mechanical dynamics of the motor, and battery dynamic. This paper proposes an estimation algorithm based on a power consumption multiphysical model, which involves all these components and their coupling. Initially, we build sub-models for each component dynamics and then incorporate them to draw up a multiphysical model that can predict with some degree of precision the consumed power during the UAV flight operation.

The study of control techniques for exoskeleton robots used in upper-extremity rehabilitation is gaining popularity. These robots are connected to the human upper limb at multiple points, allowing for smooth and independent joint movements. Therefore, providing a robust, precise, and safe control system is necessary. Sliding control-based approaches are well reputed for their robustness to parameter uncertainties, modeling errors, and external disturbances. Nevertheless, their major disadvantage is chattering. This paper describes two controllers based on sliding mode theory to reduce this undesirable effect: the generalized variable structure control and the higher-order finite-time sliding mode control. The former incorporates the derivative of the torque input in the model, while the latter is based on homogeneity and higher-order sliding modes to diminish chattering. A comparison between the two controllers is achieved by simulating passive rehabilitation mode.

Quadrotor unmanned aerial vehicles have a limited quantity of embedded energy. To preserve and guaranty the success of the UAV mission, we should manage energy consumption during the mission. In this study, we introduce an optimization algorithm to minimize the consumed energy in the flying vehicle mission under windy conditions. In order to calculate the energy consumed by the quadrotor, we present a power loss model, where the energy is formulated as a function of rotor speed and acceleration. Then, we formulate the energy minimization problem as an optimal control problem and solve this problem in order to calculate minimum energy for a point-to-point quadrotor mission under windy conditions. In order to highlight the proposed optimization approach, we compare energy consumption obtained by optimization algorithm with an adaptive control approach in simulation experiment.

In this paper, a disturbance observer based robust adaptive fuzzy tracking control algorithm is developed. The problem of trajectory tracking and wind disturbance rejection for a quadrotor unmanned aerial vehicle are investigated. An adaptive fuzzy controller is used to provide good tracking performances for the quadrotor vehicle. While, a state and disturbance observer is employed in order to estimate the immeasurable states and the unknown wind disturbances. The stability analysis of the global controller/observer system is proved using Lyapunov theory. It is shown that all signals in the closed-loop system are uniformly ultimately bounded (UUB). The proposed design can guarantees the desired tracking performances and external wind disturbance rejection. Simulation studies are presented to highlight the efficiency of the proposed control scheme.

In this paper we deals with the limitation of embedded energy for quadrotor unmanned aerial vehicles. Quadrotor UAVs are flying machines that use lift generated by several rotors, and because of this, a large proportion of their available energy is consumed by rotors in order to maintain the vehicle in the air. In this concept, two optimal control problems are formulated and solved. For the first problem, minimum-energy control effort is computed for desired initial and final configurations with respect to the angular velocity of rotors. Where in the second, minimum-time control effort is computed for a desired energy. The proposed method is illustrated by simulation experiment for a quadrotor UAV.