This paper proposes an online robust self-learning terminal sliding mode control (RS-TSMC) with stability guarantee for balancing control of reaction wheel bicycle robots (RWBR) under model uncertainties and disturbances, which improves the balancing control performance of RWBR by optimising the constrained output of TSMC. The TSMC is designed for a second-order mathematical model of RWBR. Then robust adaptive dynamic programming based on an actor-critic algorithm is used to optimise the TSMC only by data sampled online. The system closed-loop stability and convergence of the neural network weights are guaranteed based on the Lyapunov analysis. The effectiveness of the proposed algorithm is demonstrated through simulations and experiments.

In this paper, a novel fast nonsingular integral terminal sliding mode controller based on an adaptive neural network (ANN-FNITSMC) is proposed for the trajectory tracking control of cable-driven continuum robots (CDCRs) in complex underwater environments with uncertainties. First, a novel fast nonsingular integral terminal sliding mode control (FNITSMC) is designed to solve the chattering and singularity problems of the conventional terminal sliding mode control (TSMC). Second, an adaptive neural network (ANN) based on a radial basis function (RBF) is established to derive the uncertainties and compensate for the control input of CDCRs, enabling high-stable accuracy and strong robustness trajectory tracking in complex underwater environments. Simulation results are presented to demonstrate the high accuracy and strong robustness of the ANN-FNITSMC. Finally, the high accuracy, high stability, and strong robustness of the proposed trajectory tracking strategy are verified through an underwater experiment platform.

In order to improve the global convergence performance of the super-twisting sliding mode control (STSMC) for the uncertain hybrid mechanism, especially in the high-speed scenario, and enhance the robustness of hybrid mechanism system to the uncertainties with a wide range of changes, an intelligent fixed-time super-twisting sliding mode control (IFTSTSMC) is proposed. Firstly, a fixed-time super-twisting sliding mode control (FTSTSMC) algorithm is designed by adding the exponential power terms with the fixed-time performance parameters in sliding variables and the transcendental function of the super-twisting algorithm in order to enhance the global convergence performance of the STSMC. Secondly, the existence condition of FTSTSMC for the uncertain hybrid mechanism is analyzed. The IFTSTSMC is designed by introducing RBF neural network to break through the limited range of uncertainties in FTSTSMC and enhance the robustness of hybrid mechanism system to the uncertainties with a wide range of changes. Then, the Lyapunov stability of the proposed method and the global fixed-time convergence of the system are proved theoretically. Finally, the effectiveness and superiority of the proposed control method are verified by the simulation and the automobile electro-coating conveying prototype experiment comparing with two classical finite-time sliding mode control methods.

This paper presents a compliant variable admittance adaptive fixed-time sliding mode control (SMC) algorithm for trajectory tracking of robotic manipulators. Specifically, a compliant variable admittance algorithm and an adaptive fixed-time SMC algorithm are combined to construct a double-loop control structure. In the outer loop, the variable admittance algorithm is developed to adjust admittance parameters during a collision to minimize the collision time, which gives the robot compliance property and reduce the rigid collision influence. Then, by employing the Lyapunov theory and the fixed-time stability theory, a new nonsingular sliding mode manifold is proposed and an adaptive fixed-time SMC algorithm is presented in the inner loop. More precisely, this approach enables rapid convergence, enhanced steady-state tracking precision, and a settling time that is independent of system initial states. As a result, the effectiveness and improved performance of the proposed algorithm are demonstrated through extensive simulations and experimental results.

Closed-loop kinematics of a dual-arm robot (DAR) often induces motion conflict. Control formulation is increasingly difficult in face of actuator failures. This article presents a new approach for fault-tolerant control of DARs based on advanced sliding mode control. A comprehensive fractional-order model is proposed taking nonlinear viscous and viscoelastic friction at the joints into account. Using integral fast terminal sliding mode control and fractional calculus, we develop two robust controllers for robots subject to motor faults, parametric uncertainties, and disturbances. Their merits rest with their strong robustness, speedy finite-time convergence, shortened reaching phase, and flexible selection of derivative orders. To avoid the need for full knowledge of faults, robot parameters, and disturbances, two versions of the proposed approach, namely adaptive integral fractional-order fast terminal sliding mode control, are developed. Here, an adaptation mechanism is equipped for estimating a common representative of individual uncertainties. Simulation and experiment are provided along with an extensive comparison with existing approaches. The results demonstrate the superiority of the proposed control technique. The robot performs well the tasks with better responses (e.g., with settling time reduced by at least 16%).

This paper proposes a fixed-time anti-saturation (FT-AS) control scheme with a simple control loop for the 6-Degree-of-Freedom tracking (6-DOF) control problem of spacecraft with parameter uncertainties, external disturbances and input saturation. Considering the external disturbance and parameter uncertainties, the dynamical model of the tracking error is established. The traditional methods of handling input saturation usually add anti-saturation subsystems in the control system to suppress the impact of input overshoot. However, this paper directly inputs the input overshoot into the tracking error model, thus constructing a modified lumped disturbance term that includes the influence of input overshoot. Then, a novel fixed-time disturbance observer (FT-DO) is designed to estimate and compensate for this modified lumped disturbance. Therefore, there is no need to add the anti-saturation structures in the control loop, significantly reducing the complexity of the system. Finally, an observer-based fixed-time non-singular terminal sliding mode (FT-NTSM) controller is designed to guarantee the fixed-time stability of the whole system. In this way, the convergence time of the proposed scheme does not depend on the system’s initial conditions. Simulation results illustrate that the proposed method keeps the control input within the limit while achieving high-precision tracking control of attitude and position.

This paper investigates the issue of tracking control for a free-floating space manipulator with prescribed performance constraints, considering the inertia uncertainties, internal disturbances and input saturation. An inherently continuous adaptive controller is proposed by incorporating non-singular fixed-time sliding mode control, prescribed performance control (PPC), and auxiliary compensation. First, a modified non-singular fast fixed-time terminal sliding surface is constructed, which has a shorten convergence time than the conventional fixed-time sliding surface. Unlike the existing complicated PPCs, a simple structure controller is developed to satisfy prescribed performance constraints through a unique tangent-type PPC technique. The input saturation is then compensated adaptively by an auxiliary mechanism. The Lyapunov theory thoroughly validates the stability and fixed-time convergence of the closed-loop tracking system. With the suggested control scheme, the system states can converge quickly to a small neighbourhood around the origin within a preassigned time, while the position tracking error can be confined within a prescribed performance bounds even in the presence of input saturation. Compared to the existing tracking methods, the suggested control approach has the advantages of faster transient convergence, higher steady-state tracking precision, and stronger robustness. Simulation comparisons demonstrate the effectiveness and superiority of the proposed controller.

Achieving the high-precision control of cable-driven parallel robots (CDPRs) is complex because of their structural properties. In this paper, a quintessential redundant CDPR is designed as the research subject, and a continuous switching sliding mode controller based on workspace vision is implemented to enhance the accuracy and stability of trajectory tracking. In addition, a virtual prototype of the CDPR with uncertainties is created in the simulation analysis software ADAMS, and co-simulation is performed with the control system designed in Simulink to validate the effectiveness of the proposed control strategy. Furthermore, a CDPR platform is established for trajectory tracking experiments using the visual-based position feedback method. The trajectory tracking performance with the three control schemes is then evaluated. The experimental results show that the continuous switching sliding mode control algorithm can significantly decrease trajectory tracking errors and exhibit superior trajectory tracking performance compared to the other control strategies.

In the present work, a folding wing system (FWS) was developed for guided ammunitions, so that the swept-back angle could be adjusted during both gliding and diving phases. Unlike previous designs, the FWS does not have any fixing mechanisms or brake elements, and it provides folding functionality to reduce the drag force during the terminal phase. We conducted mechanism design, manufactured the FWS, performed system identification and designed various controllers including linear quadratic regulator (LQR), linear quadratic integrator (LQI), sliding mode control (SMC) and second-order sliding mode control (SOSMC) to adjust and hold the desired swept-back angles. Then, the performance of the FWS was tested experimentally under two different flight scenarios, with and without aerodynamic loads. While all controllers operated with almost zero steady-state error (SSE) in the absence of aerodynamic loads, the SOSMC was the most effective controller under aerodynamic loads, considering SSE, delay, chattering, and energy consumption.

This paper proposes a robust generalised dynamic inversion (GDI) control system design with adaptive neural network (NN) estimation for spacecraft attitude tracking under the absence of knowledge of the spacecraft inertia parameters. The robust GDI control system works to enforce attitude tracking, and the adaptive NN augmentation compensates for the lack of knowledge of the spacecraft inertia parameters. The baseline GDI control law consists of a particular part and an auxiliary part. The particular part of the GDI control law works to realise a desired attitude dynamics of the spacecraft, and the auxiliary part works for finite-time stabilisation of the spacecraft angular velocity. Robustness against modeling uncertainties and external disturbances is provided by augmenting a siding mode control element within the particular part of the GDI control law. The singularity that accompanies GDI control is avoided by modifying the Moore-Penrose generalised inverse by means of a dynamic scaling factor. The NN weighting matrices are updated adaptively through a control Lyapunov function. A detailed stability analysis shows that the closed loop system is semi-global practically stable. For performance assessment, a spacecraft model is developed, and GDI-NN control is investigated for its attitude control problem through numerical simulations. Simulation results reveal the efficacy, robustness and adaptive attributes of proposed GDI-NN control for its application to spacecraft attitude control.

This paper presents a novel cable-driven parallel robot with the aim of rehabilitating/exercising young and disabled children in drawing and writing tasks. The pyramidal topology was identified as providing the required three active translational Degrees of Freedom with a redundant set of five cables in a compact portable shape. In addition, this robot was designed with new features, as it is inexpensive, easy to control, easy to move to any place at home or school as it is fitting a home desk or classroom table. In this paper, we present the main steps for designing the proposed cable-driven pyramidal parallel robot. Specific attention is addressed to the design of the structure and the end effector as well as to establishing proper simulation models. Several simulations are proposed by implementing both kinematic and dynamic models to demonstrate the feasibility of multiple writing/drawing tasks. A specific user interface is proposed allowing both continuous and intermittent trajectories. A sliding mode control is implemented to achieve suitable tracking accuracy on a desired path. Finally, an experimental validation is successfully carried out by considering multiple trajectories for demonstrating the engineering feasibility and effectiveness of the proposed design and simulation models.

This paper studied the back-stepping adaptive sliding mode control (SMC) attitude problem of quaternion aircraft model based on radial basis function (RBF) network approximation. Firstly, a sliding mode controller is designed based on the back-stepping method (BSM) for the nonlinear aircraft model. Secondly, a RBF network algorithm is designed to compensate for the unknown and uncertain parts of the aircraft system. RBF network has simple network structure and good generalisation ability, avoids lengthy and unnecessary calculations, realises adaptive approximation of unknown parts in the aircraft model, and through the adjustment of adaptive weights, the convergence and stability of the entire closed-loop system (CLS) are guaranteed. Finally, the anti-interference performance of the controller is verified by simulation of the actuator fault model. Our proposed method has all-right control performance indicated by the simulation results.

For diagnostic and therapeutic applications in spacious spots of the gastrointestinal (GI) tract, the single rigid body capsule clinically applied is difficult to realize the fix-point posture adjustment function manipulated by the external permanent magnet system using the static balance control because the posture alignment and the locomotion interfere with each other. To realize this function easily, the dual hemisphere capsule robot (DHCR) is proposed, based on tracking effect—the axis of DHCR keeps tracking the normal orientation of the spatial universal rotating magnetic vector (SURMV). Since tracking effect employs dynamic balance control, dynamic stability of the DHCR system affects posture alignment performance. This paper focuses on posture alignment dynamic modeling and the influence of the magnetic flux density and the angular velocity of the SURMV, along with the damping coefficient of the GI tract surface on stability, obtaining the stability domains of parameters. Furthermore, to reduce error due to the uncertainties in complex GI tract environment, the sliding mode controller based on nominal model is proposed to achieve more accurate dynamic tracking, and Lyapunov theorem is employed to assess stability of controller. Finally, the tracking effect is verified through simulations and experiments, indicating that the fix-point posture adjustment can be realized with higher accuracy and efficiency.

Early-stage rehabilitation therapy for post-stroke patients consists of intensive and accurate training sessions. During these sessions, the therapist moves the patient’s joint within its range of motion repetitively. Patients, at this stage, often cannot control their muscles, and neurological disorders may occur and lead to undesirable movements. Thus, the therapist should train the joint gently to handle any sudden involuntary movements. Otherwise, the joint may undergo excessive torques, which may injure it. In this paper, we address this case and develop a clinical rehabilitation robotic system for training the knee joint taking into account the occurrence of these undesirable movements. The developed system has an innovative mechanism to measure interaction torques exerted by involuntary movements. Then, we introduce a new control approach consisting of an admittance controller and a proportional-derivative controller augmented by a radial basis function (PD-RBF) neural network. The PD-RBF guides the robot joint along a predefined trajectory, while the admittance part tracks any sudden interaction torques and updates the predefined trajectory accordingly. Thus, the robot trains the knee joint and once an undesirable movement occurs the robot gets along with this movement smoothly, then it gets back to the predefined trajectory. To validate the performance of the proposed admittance PD-RBF controller, we consider two controllers, an admittance adaptive sliding mode control and an admittance conventional PD one. Then, a compatarive study is conducted on these controllers via real-world experiments. The obtained results verify the efficiency of the admittance PD-RBF and prove its superiority over the other aforementioned controllers.

The principal objective of the paper is to show the importance of the Hamiltonian in control theory. Instead of using the Lagrangian formulation of electromechanical or robotic systems, our work is focused on robot dynamics by its Hamiltonian. Using the iterative Newton–Euler, we generate the local Hamiltonians and the derivative of the moments at each joint of the robot manipulator. Thus, we can apply decentralized controllers at each joint. We compare and discuss the efficiency of the controllers. We show that the performance of the sliding modes controller is more robust than that of the PD or Bang–Bang controllers.

In this paper, control of a suspended cable-driven parallel robot has been experimentally investigated based on the dynamic model of the robot for object tracking purpose. In order to improve the tracking ability of the robot, three control approaches, namely kinematic PID, dynamic PD, and a kinematic sliding mode control (SMC), have been implemented, both on the Simscape and on the robot constructed at the Human and Robot Interaction Laboratory. Neural network controller and dynamic SMC have been implemented on the Simscape model. Afterward, the effectiveness of each approach has been investigated by employing the root mean square error (RMSE) index. Simulation and experimental results reveal the ability of each controller for precise and smooth control. For precise and real-time object tracking, YOLOv5-s and YOLOv4-tiny model are trained. By comparing the obtained index values, the kinematic PID demonstrates the best performance with the maximum RMSE value of 0.018 compared to other methods.

Pneumatic muscle actuator (PMA) similar to biological muscle is a new type of pneumatic actuator. The flexible manipulator based on PMAs was constructed to simulate the actual movement of the human upper arm. Considering the model errors and external disturbances, the fuzzing sliding mode control based on the saturation function was proposed. Compared with other fuzzy control methods, fuzzy control and saturation function are used to adjust the robust terms to improve the tracking accuracy and reduce the high-frequency chattering.

Unmanned Aerial Vehicles (UAVs) have been used extensively in many applications, such as surveillance, medical transportation and delivery tasks. These applications usually involve an attachment system to connect a payload. However, when connected to such a coupled system, the UAV exhibits lower flight performance mainly due to gust loads. Mathematical modeling is used in this paper to illustrate the comparison between an UAV with a coupled system and a conventional UAV. Proportional Derivative (PD) and Sliding Mode Control (SMC) approaches are used to evaluate the vibrations of the payload, and the UAV trajectory subjected to Dryden gust. This work therefore uses these methods to investigate the dynamic response of a quadrotor-type UAV equipped with a payload and a flexible attachment system.

In dynamically switched systems with unknown switching signal, the control system is conventionally designed based on the worst switching scenario to ensure system stability. Such conservative design demands excessive control effort in less critical switching configurations. In the case of continuum mechanics systems, such excessive control inputs result in increased structural deformations and resultant modeling uncertainties. These deformations alter differential equations of motion which cripple the task of control. In this paper, a new approach for tracking control of uncertain continuum mechanics multivariable systems undergoing switching dynamics and unknown time delay has been proposed. Control algorithm is constructed based on the mathematical rigid model of the plant and a Common Lyapunov Function (CLF) is proposed upon sliding hyperplane regarding all switching configurations. Considering the model-based nature of sliding mode control (SMC) and inevitable uncertainties induced from modeling simplifications of continuum system or parameter evaluation errors, Finite Element Analysis (FEA) is utilized to approximate total model uncertainties. To obtain robust stability, instead of conventional switching functions in the construction of control law, the control inputs are selected such that system dynamics reside within stability bounds which are calculated based on the Lyapunov asymptotic stability criterion. Therefore, the unwanted chattering issue caused by continuous switching is not observed in control input signals. Eventually, the accuracy of the proposed method has been verified through the student version of ANSYS® mechanical APDL-based simulations and its effectiveness has been demonstrated in multiple operating conditions.

In this research, a dynamic model is first established based on screw theory and the principle of virtual work for a bilaterally symmetrical hybrid robot. By combining a novel composite error (NCE) with second-order nonsingular fast terminal sliding mode (SONFTSM) control method, a NCE-based SONFTSM dynamic control method is further presented to guarantee better trajectory tracking performance and synchronization performance simultaneously. The asymptotic convergence of proposed errors and the stability of the proposed control method have been proved theoretically. Finally, the simulation and experiment are implemented to validate the effectiveness of the proposed control method.