Tracking control of an autonomous tethered underwater vehicle (ATUV) for a successful marine operation is a challenging task due to the complex and nonlinear dynamics of the vehicle characterized by parametric uncertainties. Besides these issues, the vehicle mainly operates in an uncertain and unpredictable environment. To deal with the ATUV control tracking problem, this article proposes a new tracking control approach that will be named saturation-based adaptive computed torque+ (SACT+). The proposed SACT+ is designed using a variable saturation function, a computed torque structure, a saturation-based dynamic feedback, and an adaptive mechanism. Then, several arguments, based on the well-known Lyapunov techniques, are proposed to prove the stability behavior of the final closed-loop dynamics. This ensures the convergence (theoretically) of the vehicle tracking error to the origin, leading to stable and safe operations. However, this tracking error (experimentally) only stays around the origin due to many factors, such as the measurement noise from the vehicle’s sensors, the inherent uncertainties of the vehicle combined with external disturbances from the marine environment, etc. Different tests are conducted in real-time using our underwater vehicle Leonard prototype to validate the proposed SACT+. The obtained experimental results show the effectiveness and robustness of the proposed SACT+ approach in real-life cases. Finally, the performance and energy consumption indices, as well as comparative experimental studies with two well-established controllers (from the literature), confirm the relevance of the proposed approach for controlling small-sized and/or low-cost underwater vehicles.

This paper proposes a novel adaptive feedback sliding mode control for parallel kinematic manipulators (PKMs), built on the conventional model-based sliding mode control. This structure was chosen for its robustness towards uncertainties and external disturbances. The contribution of this research is the inclusion of a feedforward term based on the dynamic model of the PKM in the control design. This feedforward term compensates for high nonlinear dynamics, as well as avoids measurement noise in control inputs. Additionally, the fixed feedback gains of the sliding mode controller are redesigned as adaptive gains, which provide better correction actions for larger tracking errors. A stability analysis of the proposed control solution, based on Lyapunov’s method, is provided. The effectiveness of the proposed controller is demonstrated through its application to a Gough-Stewart platform (MISTRAL parallel robot) in various real-time experimental scenarios. The proposed approach is compared with various controllers demonstrating its superiority. It ensures nominal root mean square tracking errors of (i) about$22\,\mu m$in joint space, and (ii) about$27\,\mu m$in traveling plate Cartesian position. Note to Practitioners—This paper was motivated by the problem of robustness towards uncertainties and operating conditions for PKMs and especially in industry. The literature contains some control solutions to deal with these issues by designing robust or adaptive nonlinear controllers. Indeed, despite their success, for the first type, large uncertainties may degrade the tracking performance and the robustness is not always guaranteed. Adaptive controllers can be more robust, but their tuning can be difficult and especially in the case of real-time estimation of dynamic parameters. In this paper, a new robust-based sliding mode control with adaptive feedback gains is proposed to ensure the robustness towards uncertainties and external disturbances. The idea is to design adaptive feedback gains for a model-based sliding mode control, resulting in an improved robustness, while ensuring an easy tuning of its parameters. The proposed solution is validated through real-time experiments on an industrial parallel robot, and compared with some model-based controllers. The obtained results show clearly the benefits of the proposed control design and its effectiveness and robustness. In future work, the proposed controller can be endowed with a real-time estimation of the dynamic parameters, as well as, a redesign based on the super-twisting algorithm.

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

This article proposes a nonlinear model predictive control (NMPC) strategy for the position tracking of cable-driven parallel robots (CDPRs). The NMPC formulation handles explicitly the cable tensions and their limits. Accordingly, the cable tension distribution is performed as an integral part of the NMPC feedback control strategy, which notably allows the CDPR to operate on the wrench-feasible workspace boundaries without failure. In order to integrate the cable tension minimization within the NMPC formulation, the concept of wrench equivalent optimality (WEO) is introduced. The WEO is a nonnegative measure able to evaluate if the wrench generated by a given cable tension vector can be generated by an alternative tension vector with smaller 2-norm. The redundancy resolution performed by means of the minimization of the WEO enables the stability of the closed-loop system to be proved. More precisely, sufficient conditions for the uniform asymptotic stability are deduced using results from the analysis of NMPC schemes without terminal constraints and costs. Furthermore, the proposed NMPC strategy is validated experimentally on a fully constrained 6 degree-of-freedom CDPR.

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.

This paper introduces a Model Predictive Control (MPC) strategy for fully-constrained Cable-Driven Parallel Robots. The main advantage of the proposed scheme lies in its ability to explicitly handle cable tension limits. Indeed, the cable tension distribution is performed as an integral part of the main control architecture. This characteristic significantly improves the safety of the system. Experimental results demonstrate this advantage addressing a typical pick-and-place task with two different scenarios: nominal cable tension limits and reduced maximum tension. Satisfactory tracking errors were obtained in the first scenario. In the second scenario, the desired trajectory escapes from the workspace defined by the new set of tension limits. The proposed MPC scheme is able to minimize the tracking errors without violating the tension limits. Satisfying results were also obtained regarding robustness against uncertainties on the payload mass.

In this paper a PD controller with intelligent compensation is used to solve the problem of tracking trajectories for a Delta Parallel Robot with three degrees of freedom. This controller uses an artificial B-Spline neural network as a feedforward compensation term. To evaluate the proposed controller performance some numerical simulations under two different scenarios have been carried out in order to know its effectiveness respect to a simple PD controller.

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.

This paper deals with the dynamic modeling and identification of an electrically driven underwater robot manipulator. The proposed study includes the dynamic modeling of the actuators of the arm as well as the identification of the parameters of the model. The proposed method deals with the specific case of heterogeneously actuated arms, namely arms with actuators behaving differently for each joint, being considered at the kinematic level. Indeed, we show how to estimate the arms parameters when some of their revolute joints are directly actuated by geared motors, while the others are actuated by linear actuators. A minimum set of identifiable parameters is determined, and adequate excitation trajectories are generated and used in the identification procedure. Realtime experimental validation on the manipulator arms of Ifremer's HROV (Hybrid Remotely Operated Vehicle) Ariane underwater vehicle demonstrates that the proposed method improves the estimation of the dynamic model.

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 deals with the design of a new robust adaptive controller for parallel manipulators based on sliding mode and modelbased adaptive control. More precisely, the proposed controller relies on continuous finite-time terminal sliding mode (TSM) control and the linear-in-the-parameters property of the inverse dynamics of the manipulator. The main motivation behind the proposed scheme is to improve the tracking performance of fast and accurate parallel manipulators while guaranteeing the closed-loop system's robustness. Based on the linear-in-the-parameters property of the inverse dynamics of the manipulator, an adaptive law is proposed in order to estimate in real-time the dynamic parameters of the manipulator. The proposed controller has the advantage of relying on the desired reference trajectories instead of measured ones which can improve its robustness and efficiency. To demonstrate the effectiveness of the proposed controller, real-time experiments are conducted on a four-degree-of-freedom parallel manipulator called Veloce.

Control of underwater vehicles is a thoroughly investigated subject but still an open problem, because of the environmental disturbances, the highly nonlinear behaviour of vehicles, the complexity of the vehicle hydrodynamics, etc. In this paper, we are interested in depth control of a bioinspired U-CAT underwater AUV in real operating conditions. Two depth control schemes are proposed, including a PID controller and a nonlinear RISE feedback controller. The proposed controllers are implemented on the robot, then tested in an open water environment. The obtained results are presented and discussed through different experimental scenarios to illustrate the efficiency of the proposed controllers, not only to successfully control the depth, but also to be robust towards external disturbances and parameters uncertainties. we conclude that RISE controller is more robust towards environmental disturbances and outperforms the PID controller when the robot is tested in real operating condition.

This paper deals with the control of a lower limb exoskeleton acting at the knee joint level. Classical −1 adaptive control law is proposed to ensure assistance-as-needed and resistive rehabilitation following a desired trajectory that is defined by a therapeutic doctor. This control law introduces a time lag within the desired trajectory tracking due to the presence of a filter in its structure. In order to mitigate this drawback, the classical −1 adaptive control is augmented by a nonlinear proportional control. The classical and augmented −1 adaptive control laws are tested in real-time using the Exoskeleton Intelligently COmmunicating and Sensitive to Intention (EICOSI) of LISSI-lab. Real-time experimental results highlight the utility of these control laws in assistance-as-needed and resistive rehabilitation paradigms.

This paper demonstrates a novel motion control approach for biomimetic underwater vehicles with pitching fins. Even though these vehicles are highly maneuverable, the actuation of their different degrees of freedom (DOFs) is strongly coupled. To address this problem, we propose to use smooth DOF prioritization depending on which maneuver the vehicle is about to do. DOF prioritization has allowed us to develop a modular, easily applicable and extendable motion control architecture for U-CAT vehicle, which is meant for archaeological shipwreck penetration. We demonstrate the benefits of this architecture by developing an remotely operated vehicle autopilot for depth and yaw using a nonlinear state feedback controller. We also show the extensibility of the approach by controlling 3 DOFs of a fully autonomous U-CAT. The real-time experimental results show high position tracking precision (depth RMS error: 1.9 cm; yaw RMS error: 2.5°) Comparative experiments justify the use of DOF prioritization.

In this paper, the recently developed ℒ1adaptive control strategy is experimentally validated for the first time on a parallel kinematic manipulator. The ℒ1adaptive controller is known for its decoupled estimation and control loops which enables fast adaptation while guaranteeing robustness of the closed-loop system. The control scheme is experimentally implemented on a 4-DOFs parallel kinematic manipulator. Based on the obtained experimental results, a comparative study shows that the proposed ℒ1adaptive controller outperforms the PD controller in terms of tracking performance thanks to the compensation of the nonlinearities in the adaptive controller.

In this paper, a new control scheme based on the desired compensation adaptive control strategy for mechanical manipulators is developed. In order to estimate the unknown parameters, the adaptation law is formulated based on the inverse dynamic model and the desired trajectories instead of the actual ones. To further improve the tracking performance and the disturbance rejection ability of the original controller, the static feedback gains are replaced by nonlinear varying ones. The computed control inputs are then projected using a kinematics based projector in order to remove the internal efforts in redundantly actuated parallel kinematic manipulators that may damage the mechanical structure of the manipulator. To demonstrate its effectiveness, the proposed controller is validated through real-time experiments on Dual-V; a 3-DOFs redundantly actuated parallel kinematic manipulator. The obtained results show that the proposed controller outperforms the original one in terms of tracking errors and energy consumption.

A novel adaptive controller based on the Robust Integral of the Sign of the Error (RISE) is proposed. The RISE feedback strategy yields semi-global asymptotic tracking despite the presence of unstructured additive disturbances provided some limited assumptions on the system. To achieve better tracking performance, the RISE controller is extended with a model-based adaptive feedforward term. The addition of the feedforward term compensates for the structured uncertainties yielding reduced tracking errors and reduced control effort. The proposed controller is experimentally implemented on a 3-DOFs redundantly actuated parallel manipulator. The computed control inputs are projected using a kinematics based projector in order to remove the internal efforts that may damage the mechanical structure of the manipulator. Experimental results show a better performance of the proposed adaptive controller compared to the basic RISE controller in terms of tracking accuracy and energy consumption.

Cable-driven parallel robots (CDPR) are efficient manipulators able to carry heavy payloads across large workspaces. Therefore, the dynamic parameters such as the mobile platform mass and center of mass location may considerably vary. Without any adaption, the erroneous parametric estimate results in mismatch terms added to the closed-loop system, which may decrease the robot performances. In this paper, we introduce an adaptive dual-space motion control scheme for CDPR. The proposed method aims at increasing the robot tracking performances, while keeping all the cable tensed despite uncertainties and changes in the robot dynamic parameters. Reel-time experimental tests, performed on a large redundantly actuated CDPR prototype, validate the efficiency of the proposed control scheme. These results are compared to those obtained with a non-adaptive dual-space feedforward control scheme.

In this paper, a Nonlinear Model Predictive Control (NMPC) is proposed to control a single stage Voice-Coil-Motor (VCM) of a Hard-Disk-Drive (HDD). Due to its fast settling time and its robustness, this controller is suggested to be applied for the first time to control a R/W head of an HDD. To highlight the good performance and characteristics of the NMPC, a comparative study with a standard PID control is presented. The two control methodologies were evaluated in nominal conditions as well as in other situations such as disturbances and uncertainties on the plant model parameters. In all cases, NMPC presents much better simulation results in term of speed and robustness in the presence of unexpected perturbations and parameters' change.

This paper deals with the dual-space adaptive control of R4 redundantly actuated parallel manipulator for applications with very high accelerations. This controller is compared experimentally with a dual-space feedforward controller (which may have good performances for specific cases, but has crucial losses of performance when there is any operational change (such as a change of load)), for a pick-and-place task with accelerations of 30G (without payload) and 20G (with a payload of 200g). The objective of this paper is to show that the proposed dual-space adaptive controller not only keeps a very good performance independently of the operational case, but also has a better performance than the dual-space feedforward controller even when this last one is best configured to the given case.

This paper presents the design and experimental implementation of an ℒ1adaptive control on a tethered underwater vehicle. This controller, well known for its fast adaptation and its robustness, is proposed to be applied for the first time in the field of underwater vehicles control. This paper summarizes the implementation and experimental results obtained on a modified version of the AC-ROV underwater vehicle. Various experimental scenarios are presented to illustrate the ability of the ℒ1adaptive law not only to successfully control pitch and depth (even with strong modeling uncertainties), but also to be efficient towards disturbances like waves or buoyancy changes.

The control of dynamically stable hopping robots has made great progress in the last decades. This paper deals with modeling and control of Kangaroo hopping robot. It is a one-leg planar hopping robot which imitates the mode of displacement of kangaroos. Lagrangian dynamic model of the hopping robot is computed on the different phases of the jumping cycle. A new control scheme is proposed to control the leg thrust during stance phase for planar hopping. A nonlinear model predictive control has been combined with Raibert's approach which has improved significantly its performances. A simulator has been developed to simulate the behavior of the controlled robot. The proposed control approach is validated in simulation and is compared with Raibert's approach.

This paper deals with the problem of unavailability of velocity measurements for the control of parallel manipulators for pick-and-place applications. As most controllers require the measurement of the joint positions as well as joint velocities, the latter need to be estimated. Three observers have been implemented while keeping the same nonlinear controller: a Lead-lag filter based observer, an Alpha-beta-gamma observer and a High-gain observer. The resulting performances obtained in Real-time experiments by each observer have been detailed and compared.

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This paper deals with constant disturbances rejection in limit cycle tracking for an under-actuated mechanical system. The feedback controller presented in the work of Andary et al. (2008) is enhanced to handle constant disturbances by using online iterative estimation of an equivalent disturbance which is easily compensated by adding the estimated value to the output of the system. The effectiveness of the proposed method is demonstrated through real-time experiments on an inertia wheel inverted pendulum. Constant disturbances are introduced either as a weight asymmetrically fixed to the pendulum body, or by the use of a bad calibrated sensor.

This paper deals with a discret-time control approach, proposed for the control of a five-link, four-actuator planar biped walker. The approach is based on the choice of a particular class of walk configurations that enables a full state controllability avoiding the need to use Poincare-like argumentation in the proof of motion's reproducibility (stability of limit cycles). Simulation results attest the efficiency of the proposed control approach.

This paper deals with nonlinear dual mode adaptive control of a redundant manipulator for a pick-and-place scenario with high acceleration (20 G). For performance comparisons, a conventional Proportional-Derivative (PD) controller has also been implemented. In this context, the experimental testbed is not equipped with velocity sensors. Therefore, a high-gain observer has been implemented to estimate the articular velocities. Real-time experiments show the performance improvements obtained by the proposed control approach in comparison to the conventional one.

This paper deals with a control approach dedicated to stable limit cycle generation for underactuated mechanical systems. The proposed approach is based on partial nonlinear feedback linearization and dynamic control for optimal periodic reference trajectories tracking. Simulation results and experiments show the efficiency of the proposed approach.

In this paper, a new nonlinear predictive control scheme is proposed for a planar under-actuated walking robot. The basic feature in the proposed strategy is to use online optimization in order to update the tracked trajectories in the completely controlled variables in order to enhance the stability of the remaining indirectly controlled ones. The stability issue is discussed using the Poincare's section tool leading to a computable criterion that enables the stability of the overall scheme to be investigated as well as the computation of a candidate region of attraction. The whole framework is illustrated through a simulation case-study.