In tandem with the fast-growing demand for Unmanned Aerial Vehicles (UAVs) for surveillance and reconnaissance, advanced controllers for these critical systems are needed. This paper proposes a flight dynamics controller design that considers various uncertainties for the Hydra Technologies UAS-S4 Ehécatl. In order to be realistic, in addition to flight dynamics nonlinearities, three main sources of uncertainties are considered, as those caused by unknown controller’s parameters, modeling errors, and external disturbances. A Robust adaptive fuzzy logic controller is designed, in charge of nonlinear flight dynamics in presence of a variety of uncertainties. The nonlinear flight dynamics is modeled based on the Takagi-Sugeno method relying on the soft association of local linear models. Since this controller is model-based, an optimal reference model is defined, which is stabilised by the Linear Quadratic Regulator procedure. A fuzzy logic controller is then designed for the nonlinear model. Lastly, with the aim to handle the uncertainties, the gains of the fuzzy controller are reconfigured, and are continuously adjusted by Lyapunov-based robust adaptive laws. The performance of the UAS-S4 Robust adaptive fuzzy logic controller is evaluated in terms of lateral and longitudinal flight dynamics stabilisation, and the reference model state variables tracking under various uncertainties.

For robot manipulators, there are two types of disturbances. One is model parametric uncertainty; the other is unmodelled parameters such as joint friction forces and external disturbances. Unmodelled joint frictions and external disturbances reduce performance in terms of positioning accuracy and repeatability. In order to compensate for unmodelled parameters, the design of a new controller is considered. First, the modelled and unmodelled parameters are included in a dynamic model. Then, based on the dynamic model, a new Lyapunov function is developed. After that, new nonlinear joint friction and external disturbance estimation laws are derived as an analytic solution from the Lyapunov function; thus, the stability of the closed system is guaranteed. Better values of the adaptive dynamic compensators can be extracted by fuzzy rules according to the tracking error. Limitations and knowledge about friction and external disturbances are not required for the design of the controller. The controller compensates for all possible model parameter uncertainties, all possible unknown joint frictions and external disturbances.

Thisarticle presents an observer-based output tracking control method for electrically actuated cooperative multiple manipulators using Bernstein-type operators as a universal approximator. This efficient mathematical tool represents lumped uncertainty, including external perturbations and unmodeled dynamics. Then, adaptive laws are derived through the stability analysis to tune the polynomial coefficients. It is confirmed that all the position and force tracking errors are uniformly ultimately bounded using the Lyapunov stability theorem. The theoretical achievements are validated by applying the proposed observer-based controller to a cooperative robotic system comprised of two manipulators transporting a rigid object. The outcomes of the introduced method are also compared to RBFNN, which is a powerful state-of-the-art approximator. The results demonstrate the efficacy of the introduced adaptive control approach in controlling the system even in the presence of disturbances and uncertainties.

The purpose of this paper is to design a stabilizing controller for a car with n connected trailers. The proposed control algorithm is constructed on the Lyapunov theory. In this paper, the purpose of navigating the system toward the desired point considering the slip phenomenon as a main source of uncertainty is analyzed. First mathematical models are presented. Then, a stabilizing control approach based on the Lyapunov theory is presented. Subsequently, an uncertainty estimator is taken into account to overcome the wheel slip effects. Obtained results show the convergence properties of the proposed control algorithm against the slip phenomenon.

This paper addresses the tracking problem for uncertain nonlinear sandwich systems that consist of two nonlinear subsystems and saturation nonlinearity, which is sandwiched between the subsystems. The considered sandwich system is also subject to a nonsymmetric input saturation constraint. Due to the nonsmooth characteristics of sandwiched saturation nonlinearity and also the input saturation function, the design procedure deals with hard challenges. To overcome these difficulties, a recursive approach is suggested that consists of two phases. For the implementation of the proposed approach, a tracking problem is solved in each phase. In the first phase, the second subsystem with sandwiched saturation nonlinearity is considered and the output tracking problem of the desired time-varying reference signal is solved using backstepping method. The outcome of the first phase is the desired reference signal that should be tracked by the first subsystem in the next phase. In the second phase, the robust control input is designed for the first subsystem by employing adaptive sliding mode technique such that, despite the nonsymmetric input saturation constraint, model uncertainty and external disturbances, the output of the first subsystem follows the desired signal that is obtained in the previous phase. The simulation results for a mechanical sandwich system are illustrated to verify the effectiveness of the proposed control method.

Course keeping for ships is vital for automatic navigation in marine transportation. To improve the control effect and reduce the energy output of the controller, this article proposes an improved concise backstepping controller based on a Lyapunov candidate function by introducing a nonlinear function of course error to replace the course error itself in the feedback loop. The procedure of nonlinear controller design has been reduced from two steps to one step using information from controlled plant to construct the Lyapunov candidate function. Compared with the pure backstepping control, the proposed improved algorithm reserves the nonlinear item of the system, and possesses a strong disturbance rejection ability and robustness to the mathematical model uncertainty. The algorithm given here has advantages of simplified construction method, satisfactory control effect, robustness and energy saving.

Let $M$ and $N$ be admissible Hausdorff topological spaces endowed with admissible families of open coverings. Assume that $\mathcal{S}$ is a semigroup acting on both $M$ and $N$. In this paper we study the behavior of limit sets, prolongations, prolongational limit sets, attracting sets, attractors, and Lyapunov stable sets (all concepts defined for the action of the semigroup $\mathcal{S}$) under equivariant maps and semiconjugations from $M$ to $N$.

This work is concerned with trajectory tracking of robots when the electrical dynamics of the brushless DC motor actuators is considered. It is shown that proportional-derivative (PD) control with feedforward compensation, plus some additional terms to cope with the electrical dynamics, ensures state boundedness. Furthermore, tracking error converges to zero from arbitrarily large initial conditions if controller gains are correctly chosen. Under mild assumptions, this controller reduces to the well-known PD control with feedforward compensation when implemented according to torque control, a successful industrial practice. Thus, it is explained, for the first time, why this strategy works well in applications.

This study offers the solution of the end-effector trajectory tracking problem subject to state constraints, suitably transformed into control-dependent ones, for mobile manipulators. Based on the Lyapunov stability theory, a class of controllers fulfilling the above constraints and generating the mobile manipulator trajectory with (instantaneous) minimal energy, is proposed. The problem of manipulability enforcement is solved here based on an exterior penalty function approach which results in continuous mobile manipulator controls even near boundaries of state constraints. The numerical simulation results carried out for a mobile manipulator consisting of a non-holonomic unicycle and a holonomic manipulator of two revolute kinematic pairs, operating in a two-dimensional task space, illustrate the performance of the proposed controllers.

In this paper we are concerned with control of rigid robots equipped with brushless DC-motors (BLDC) when the electric dynamics of these actuators is taken into account. We show for the first time that a saturated PD controller suffices to achieve global asymptotic stability. Our controller is the simplest controller proposed until now to solve this problem: it only requires position measurements and linear feedback of electric current.

Two distributed stochastic search algorithms are proposed for motion planning of multi-robot systems: (i) distributed gradient, (ii) swarm intelligence theory. Distributed gradient consists of multiple stochastic search algorithms that start from different points in the solutions space and interact with each other while moving toward the goal position. Swarm intelligence theory is a derivative-free approach to the problem of multi-robot cooperation which works by searching iteratively in regions defined by each robot's best previous move and the best previous move of its neighbors. The performance of both approaches is evaluated through simulation tests.

This study addresses the problem of adaptive controlling of both a nonredundant and a redundant robotic manipulator with state-dependent constraints. The task of the robot is to follow a prescribed geometric path given in the task space, by the end-effector. The aforementioned robot task has been solved on the basis of the Lyapunov stability theory, which is used to derive the control scheme. A new adaptive Jacobian controller is proposed in the paper for the path following of the robot, with both uncertain kinematics and dynamics. The numerical simulation results carried out for a planar redundant three-DOF (three degrees of freedom) manipulator whose end-effector follows a prescribed geometric path given in a two-dimensional (2D) task space, illustrate the trajectory performance of the proposed control scheme.

The paper presents a control scheme for simultaneous control of position and force of robot manipulator in contact with an elastodynamic environment. The control makes the assumption that interaction force between the robot and environment is adequately modeled by a second-order linear model with constant coefficients, and its implementation requires the knowledge of only boundary values of the environment parameters. It is shown that, provided that robot dynamics is exactly modeled, the scheme ensures asymptotic convergence of errors along nominal trajectories characterized by constant prescribed interaction forces and constant prescribed velocities along the contact surface.

The purpose of this paper is to show that the method of controlledLagrangians and its Hamiltonian counterpart (based on the notionof passivity) are equivalent under rather general hypotheses. Westudy the particular case of simple mechanical control systems(where the underlying Lagrangian is kinetic minus potentialenergy) subject to controls and external forces in some detail.The equivalence makes use of almost Poisson structures (Poissonbrackets that may fail to satisfy the Jacobi identity) on theHamiltonian side, which is the Hamiltonian counterpart of a classof gyroscopic forces on the Lagrangian side.