Robot Motion Planning and Control
Robot Motion Planning and Control
Fuzzy Control Systems Design and Analysis: A Linear Matrix Inequality Approach
Fuzzy Control Systems Design and Analysis: A Linear Matrix Inequality Approach
Tracking-error model-based predictive control for mobile robots in real time
Robotics and Autonomous Systems
Conditions of output stabilization for nonlinear models in the Takagi--Sugeno's form
Fuzzy Sets and Systems
Parameterized linear matrix inequality techniques in fuzzy control system design
IEEE Transactions on Fuzzy Systems
Automatica (Journal of IFAC)
New approaches to H∞ controller designs based on fuzzy observers for T-S fuzzy systems via LMI
Automatica (Journal of IFAC)
Multiple DNN identifier for uncertain nonlinear systems based on Takagi-Sugeno inference
Fuzzy Sets and Systems
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The aim of this work is to include the navigation step for the waypoint-based guidance of a UAV system and to illustrate aspects such as tracking of the reference trajectory under wind presence, while conserving total energy requirements. The mission is represented utilising graph theory tools. The mathematical modelling of an aircraft controlled by an actuator surface is presented in terms of simple analytic relationships in order to simulate the actual horizontal motion of the vehicle. Its equivalence with a Tagaki-Sugeno (T-S) fuzzy system is illustrated that can aid the control methodology involved. Additionally, the advantages of utilising such an analysis is also stressed. The model formulated is an error posture model, that depends on current and reference posture. The control law is designed through parallel distributed compensation (PDC) and the gains are computed with the help of linear matrix inequalities (LMIs). Hence stability for the system is also guaranteed provided that the state variables are bounded in a priori known compact space. Moreover the energy requirements are described. This article is contributing towards energy enhancing a UAV mission and generating safely-flyable trajectories to meet mission objectives. The control law used is calculated in the pre-flight planning and can be used in real time for any trajectory to be tracked under any environmental conditions. Provided that angular and linear velocities are bounded, the latter is feasible under the assumption that the magnitude of air speed is small compared to the ground velocity of the aerial vehicle. The methodology offers an improved visualisation to aid an analyst with the representation of a UAV mission through graph theory tools utilising energy requirements for the mission and fast computational schema using matrix analysis. A simulation example of a UAV deployed from a source to reach a destination node under windy conditions is included to illustrate the analysis. The reference trajectory used is a piecewise Bezier-Bernstein curve referred to as the Dubins path.