Narrow Results

Flexible and robust Time-Varying Formation (TVF) tracking of Unmanned Ground Vehicles (UGVs) guided by an Unmanned Aerial Vehicle (UAV) is considered in this paper. The UAV–UGVs system control model is based on leader-follower approach, where the control scheme consists of two consecutive tasks, namely, deployment task and TVF tracking. Accordingly, two novel nonlinear controllers are proposed for controlling the UGVs formation. First, unlike the classical frameworks on UGVs formation tracking, for which only particular shapes are handled (e.g. circle, square, ellipse), we propose a UGVs deployment-controller ensuring to reach free-formation shapes. The key feature is in using the estimated implicit representation of the desired formation shape as a potential function to generate the UGVs reference trajectory. Second, in the TVF tracking task, a robust cascaded velocity/torque controller for UGVs is proposed based on kinematic and dynamic models. Differently from the classical backstepping framework, the key idea is in introducing an auxiliary control input, in such a way that the overall UGV dynamics is converted into a simpler and modular control structure. As such, the auxiliary input is used to control indirectly the actual UGVs velocity vector. A signum term is added to the torque-input to compensate for the unknown external disturbances and unmodeled dynamics. Numerical simulation shows the effectiveness of the proposed formation controllers compared with the case when the perfect velocity-tracking assumption holds. Experimental results are further provided using three festos Robtino robots to show the validity of the proposed TVF tracking velocity-control scheme.

The paper proposes a nonlinear optimal control approach for the model of the vertical take-off and landing (VTOL) aircraft. This aerial drone receives as control input a directed thrust, as well as forces acting on its wing tips. The latter forces are not perpendicular to the body axis of the drone but are tilted by a small angle. The dynamic model of the VTOL undergoes approximate linearization with the use of Taylor series expansion around a temporary operating point which is recomputed at each iteration of the control method. For the approximately linearized model, an H-infinity feedback controller is designed. The linearization procedure relies on the computation of the Jacobian matrices of the state-space model of the VTOL aircraft. The proposed control method stands for the solution of the optimal control problem for the nonlinear and multivariable dynamics of the aerial drone, under model uncertainties and external perturbations. For the computation of the controller’s feedback gains, an algebraic Riccati equation is solved at each time-step of the control method. The new nonlinear optimal control approach achieves fast and accurate tracking for all state variables of the VTOL aircraft, under moderate variations of the control inputs. The stability properties of the control scheme are proven through Lyapunov analysis.

A robust feedback Nash equilibrium is defined and solved analytically in a differential climate model with N regions based on an approach of IPCC 2001 scientific report for calculating radiative forcing due to anthropogenic CO₂ emissions. In addition, uncertainty is introduced by perturbing the climate change dynamics such that future radiative forcing and global mean temperature will have unknown outcomes and probability distributions. There are n asymmetric investors, each investing in a portfolio containing N regional capital stocks used in production that generates CO₂ emissions. In each region there is one policy maker, acting as a regional social planner, that chooses regionally optimal abatement policies. Dynamic maximin decision criteria are applied for the policy makers in a robust feedback Nash equilibrium for N policy makers' abatement strategies and n investors' investment strategies.

This paper deals with the problems of stability analysis and control synthesis with performance for the master-slave synchronization of Lur'e systems using a time-delay feedback control. The proposed approach consists in obtaining a new improved robust stability criteria as well as a control law based on linear matrix inequalities (LMIs) via discretized Lyapunov–Krasovskii functional combined with an alternative strategy based on the introduction of slack variables to allow the decoupling of the system matrices from the Lyapunov matrices. Using the problem of synchronizing coupled Chua's circuits, two examples are presented to illustrate the effectiveness of the proposed methodology.

The dynamical model for the spacecraft with multiple solar panels and the cooperative controller for such spacecraft are studied in this paper. The spacecraft consists of a rigid platform and two groups of flexible solar panels, where solar panels could be driven to rotate by the connecting shaft. The flexible solar panel involves the use of the orthogonal polynomial in two directions to describe its elastic deformation. By using the Rayleigh–Ritz method, the characteristic equation is derived to obtain natural frequencies and modal shapes of the whole spacecraft. Then the discrete rigid-flexible coupled dynamical equation of the spacecraft is obtained by using the Hamiltonian principle. The equation involves the coupling of the attitude maneuver, solar panels’ driving and vibration suppression. These dynamical behaviors are addressed by the rigid-flexible coupled mode for the first time in this paper. Based on the dynamical equation, the cooperative control scheme is designed by combing the proportional-differential and robust control method. Numerical results show the accuracy of the present modelling method and the validation of the control strategy. The modal analysis implies the complex rigid-flexible coupled characteristic between the central platform and flexible solar panels. The proposed control scheme can maintain the attitude stability while solar panels are being driven, as well as the vibration suppression of flexible solar panels.

This paper presents the design and implementation detail of an advanced mini quadrotor system, including the low cost commercial-off-the-shelf (COTS) electronics and advanced control algorithm. The proposed quadrotor has a gross takeoff weight of 758g and 360mm frame diagonal size. It is capable of semi-autonomous maneuver in GPS denied environments, solely relying on onboard sensors and computers. A globally defined quadrotor model is formularized, and a nonlinear velocity tracking controller is implemented on the special Euclidean group SE(3). An optical flow and ultrasonic-based onboard downward-facing camera is used as the primary sensor to provide velocity and altitude measurement feedback for the controller. The control and sensor fusion algorithm is developed under Arduino compatible open source electronics.

Controlling nonlinear systems is an important and serious task, especially in the presence of external disturbances and uncertain parameters. The main aim of this paper is to design a robust controller that ensures good trajectory tracking of a new quadrotor with rotating arms subjected to external disturbances. Before proceeding to the quadrotor control, it is necessary to first develop a dynamic model of the studied system that takes into account the variation of: the Center of Gravity (CoG), the inertia, and the allocation matrix. Then, based on the finite time Lyapunov stability theory, the theoretical basis of backstepping control integrated with disturbance observer is explained. The observer estimates online the external disturbances and compensates them in the internal loop that contains the attitude and the position backstepping controllers. Numerical simulations are performed to illustrate the efficiency of the proposed control technique, where the controller’s parameters are tuned using a Genetic Algorithm (GA). Finally, qualitative and quantitative comparison of the suggested controller with the conventional backstepping controller is carried out. Overall, the findings show that the proposed control technique outperforms in terms of accuracy and robustness.

We first investigate the fundamental properties of the mechanical system as related to the control design. Then a new robust control is proposed for mechanical systems with fuzzy uncertainty. Fuzzy set theory is used to describe the uncertainty in the mechanical system. The desirable system performance is deterministic. The proposed control is deterministic and is not the usual if-then rules-based. The resulting controlled system is uniformly bounded and uniformly ultimately bounded proved via the Lyapunov minimax approach. The resulting control design is systematic and is able to render the deterministic performance. A mechanical system is chosen for demonstration.

This paper presents a control scheme for the humanoid robot TEO’s elbow joint based on a novel tuning method for fractional-order PD and PI controllers. Due to the graphical nature of the proposed method, a few basic operations are enough to tune the controllers, offering very competitive results compared to classic methods. The experiments show a robust performance of the system to mass changes at the tip of the humanoid arm.

This paper proposes a nonlinear optimal control approach for mulitple degrees of freedom (DOF) brachiation robots, which are often used in inspection and maintenance tasks of the electric power grid. Because of the nonlinear and multivariable structure of the related state-space model, as well as because of underactuation, the control problem of these robots is nontrivial. The dynamic model of the brachiation robots undergoes first approximate linearization with the use of Taylor series expansion around a temporary operating point which is recomputed at each iteration of the control method. For the approximately linearized model, an H-infinity feedback controller is designed. The linearization procedure relies on the Jacobian matrices of the brachiation robots’ state-space model. The proposed control method stands for the solution of the optimal control problem for the nonlinear and multivariable dynamics of the brachiation robots, under model uncertainties and external perturbations. For the computation of the controller’s feedback gains an algebraic Riccati equation is solved at each time-step of the control method. The global stability properties of the control scheme are proven through Lyapunov analysis. The new nonlinear optimal control approach achieves fast and accurate tracking for all state variables of the brachiation robots, under moderate variations of the control inputs.

This paper proposes a Finite-Time Disturbance Observer-based new Fast Terminal Sliding Mode Control (FTDO–FTSMC) for systems under disturbances and parametric uncertainties with input saturation. Our approach begins with the design of a finite-time observer to estimate disturbances, in which we show that the errors in disturbance estimation converge to zero in finite time. Then, a new fast terminal sliding surface is proposed for the fast and finite-time convergence of the tracking errors. The closed-loop system under the proposed control scheme has been proved to be asymptotically stable by using the Lyapunov theory. Finally, the proposed FTDO–FTSMC framework is applied to the attitude control of a three-DOF hover quadrotor under time-varying disturbances and parametric uncertainties. Simulation results show that the proposed control strategy achieves excellent performance in terms of trajectory tracking, disturbance rejection and robustness, even in the presence of external disturbances and model uncertainties, compared with the existing control approaches.

This paper focuses on trajectory tracking, robustness and stabilization of a golf swing robot which has been recently developed to simulate the ultra-high-speed swing motions of a golfer. The proposed control strategies are based on the Lyapunov stability theory and include Backstepping and Sliding-Mode Control based techniques. To attenuate the chattering phenomena caused by a discontinuous switching function and improve the dynamic response of the manipulator, a fuzzy system is used in this research; a Backstepping Sliding-Mode Controller (BSMC), a Backstepping Fuzzy Sliding-Mode Controller (BFSMC) and a Super-twisting Backstepping Sliding-Mode Controller (STBSMC) are used to evaluate the proposed hybrid controller’s BFSMC performance. The Lyapunov stability theory is used to guarantee the stability of the proposed closed-loop robot technique. Numerical simulations show the effectiveness of the proposed strategy based on the fuzzy logic mechanism under different disturbances and uncertainties.