In this paper, we propose a novel adaptive feedforward super-twisting sliding mode control algorithm to resolve the tracking control problem of parallel manipulators. The proposed control scheme includes three main terms, (i) the standard super-twisting algorithm, (ii) an adaptive feedforward dynamic model, and (iii) a feedback term to ensure stability. The proposed controller provides robustness towards uncertainties and disturbances, less sensitive to measurement noise, and allows dynamic parameters adaptation of the manipulator while executing a certain task. Real-time experiments are conducted on a 3-DOF non-redundant Delta parallel robot, including two main scenarios, (i) nominal case, and (ii) robustness towards operating acceleration changes. The relevance of the proposed controller is verified experimentally in both scenarios and compared with two other controllers from the literature, including the standard and the feedforward super-twisting sliding mode control algorithms.

This article deals with the development and implementation of a novel feedforward super-twisting sliding mode controller for robotic manipulators. A full stability analysis based on a Lyapunov candidate is established showing a local asymptotic finite-time convergence of the proposed controller in the presence of upper bounded disturbances. Its robustness toward parametric uncertainties and system disturbances, thanks to the super-twisting approach, is pointed out. In addition, the feedforward dynamic term of the proposed controller that can compensate for the model nonlinearities is not sensitive to measurement noise. Real-time experiments have been conducted on two parallel manipulators: a 5-DOF SPIDER4 PKM and a 3-DOF Delta PKM. The effectiveness of the proposed controller is validated in different scenarios, including the nominal case and robustness toward parametric variations (payload) and speed changes

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In this paper, we present a robust nonlinear de-centralized control scheme for an islanded DC microgrid (MG) where the main control goals are to achieve a sustained stability for the DC bus voltage at a certain desired value, to maintain the power balance in the system and to insure robustness against disturbances and perturbations. The proposed control strategy uses the Model Free Super-Twisting Sliding Mode (MFSMC) as it needs no accurate representation of the environment in order to be effective which makes it a suitable choice for system with nonlinear model prone to parameters variation. The studied microgrid is composed of a solar photo-voltaic (PV) unit and a hybrid energy storage system including a battery and a supercapacitor (SC) along with DC loads. To attain the intended objectives, a hierarchical cascaded control strategy is designed with two levels: a high-level control that stabilizes the DC bus voltage at a reference value by generating a current reference to be tracked, and a low-level control composed of an energy management system EMS based on a passive filtration to distribute the reference current between the storage system units according to their dynamic specifications. Simulations on MATLAB/Simulink are carried out to evaluate the effectiveness and robustness of the proposed control scheme under various operating conditions created by random variations of power generation and consumption. Noise sensitivity test is also carried out for this controller and for a model-based one that is the Feedback linearization control technique (FL).

This paper presents a new Energy Management System (EMS) strategy for the Energy Storage System (ESS) of a Microgrid (MG) based on the proper time scale of each ESS component. The theoretical framework allows to achieve more efficient use of the different ESS components’. While almost all existing methods rely on filtering load profile signals to achieve power separation, our proposed approach achieves a better performance on components’ ageing rate with less processing complexity using a simple limiter in algorithm. The proposed method is first evaluated as an EMS only and then extended to the whole MG. Results show an ageing rate reduction of 93% for fast component at the price of 24% of ageing rate increase for slow component.

In this paper, a new robust model-based super-twisting algorithm is proposed as a control solution for parallel kinematic manipulators (PKMs). The conventional super-twisting algorithm for robot manipulators has the structure of a computed-torque control which can be sensitive to measurement noise. This issue may deteriorate the dynamic performance of the manipulator and reduce its robustness towards changes in the operating conditions. The proposed approach, relying on the desired trajectory, is more computationally efficient and more robust. It includes a feedforward dynamic compensator, the super-twisting feedback control, and a feedback stabilizing term. As a validation, real-time experiments have been conducted on a 5-DOF redundantly actuated PKM. Several scenarios have been tested including nominal case and the robustness towards speed variations. The relevance of the proposed control solution is proved through the improvement of the tracking performance at different dynamic operating conditions.

In this paper, we propose a novel time-varying feedback control strategy based on the Robust Integral of the Sign of the Error (RISE). The main motivation is to enhance the tracking performance of RISE controller at high dynamic operating conditions. RISE control law ensures a semi-global asymptotic tracking without introducing severe restrictions on the uncertain and nonlinearly parametrized systems. More nonlinearities are added to the original RISE control law by replacing the static feedback gains with nonlinear ones which depend on the system state variables. The proposed contribution is implemented in real-time experiments on a non-redundant three-degrees-of-freedom parallel manipulator named Delta. Comparing to the standard RISE controller, experimental results show better tracking performances of the proposed time-varying feedback RISE controller.

In this paper, we focused on the yaw motion dynamics for light electric vehicles. The objective is to track the yaw rate reference in order to enhance lateral stability. Two controllers were designed, a simple PID controller and a third generation CRONE controller. The design methodology considers the total mass of the vehicle, the longitudinal speed, and the road adhesion as uncertain parameters. Later, we analyze the effect of vertical dynamics, on a controlled full vehicle system, where the variation of the normal wheel-contact forces was taken into consideration. A double lane change maneuver is simulated, and the results show the robustness of the CRONE controller against the uncertain parameters and its effectiveness to lessens the influence of load transfer variations.

This paper deals with a new dynamic formulation of parallel manipulators incorporating the actuator and friction dynamics to be utilized in control. A model-based controller, PD with computed feedforward, is implemented for a parallel robot taking into consideration the formulated dynamics. The motivation behind this contribution is to enhance the control performance by compensating the unfavourable nonlinearities abundant extensively in PKMs. Those nonlinearities may increase considerably when operating at high-speed motions. The proposed feedforward part relies on the reference trajectories instead of the measured ones improving the control performance and the computational efforts. To validate our contribution, real-time experiments are conducted on a four degree-of-freedom parallel robot named VELOCE in different operating conditions.

This paper presents a fault tolerant approach for a coaxial octorotor regarding rotor failures. A complete architecture including error detection, fault isolation and system recovery is presented. The diagnosis system is designed with a nonlinear observer to generate residuals and an inference model to evaluate them and isolate the faulty motor. Once the motor failure is diagnosed, a recovery algorithm is applied. It uses the built-in hardware redundancy of the octorotor and compensates the loss of the failing motor by controlling its dual to keep a stable flight that allows the multirotor to continue its mission. This architecture is validated on real flights.

This paper presents a fault tolerant control strategy based on an offline control mixing for an octorotor unmanned aerial vehicle (UAV) regarding several rotor failures. This strategy consists of a set of explicit laws, computed offline, each one dedicated to a fault situation. The corresponding law is selected according to the output of a fault detection and isolation (FDI) module. This module is designed with a non-linear sliding mode observer. The main advantage of this architecture is the deterministic character of the solution, its fast operation and the low computational load. The effectiveness of this approach is illustrated through real experimental application to a coaxial octorotor, where up to four motor failures are considered.

The autoregressive (AR) model is a well-known technique to analyze time series. The Yule-Walker equations provide a straightforward connection between the AR model parameters and the covariance function of the process. In this paper, we propose a nonlinear extension of the AR model using kernel machines. To this end, we explore the Yule-Walker equations in the feature space, and show that the model parameters can be estimated using the concept of expected kernels. Finally, in order to predict once the model identified, we solve a pre-image problem by getting back from the feature space to the input space. We also give new insights into the convexity of the pre-image problem. The relevance of the proposed method is evaluated on several time series.