Presence Of Dynamic Uncertainties Research Articles

Event‐Triggered Adaptive Neural Network Backstepping Sliding Fault‐Tolerant Control of Spacecraft Formation Flying With Input Saturation

This study explores the challenge of tracking control for spacecraft formation flying (SFF) in the presence of dynamic uncertainties and external perturbations. Firstly, sliding mode control combined with backstepping control is used to address saturation issues. Then, neural networks, minimal parameter learning, and adaptive control are integrated to handle dynamic uncertainties and actuator failures. To alleviate the communication load, an event‐triggered mechanism is ultimately implemented, which leads to the development of an adaptive sliding mode fault‐tolerant control algorithm based on an event‐triggered neural network. This control architecture achieves significant advancements over traditional techniques: (1) ensuring system robustness and adaptability in complex scenarios with uncertain system dynamics and external disturbances, effectively counteracting actuator failures and input saturation issues; (2) significantly reducing transmission and computational burdens in resource‐limited networked systems through the adoption of event‐triggered control (ETC) mechanisms; (3) achieving high‐precision tracking performance for SFF without relying on prior knowledge of the system’s inherent dynamics, environmental disturbances, or potential actuator deficiencies. The Lyapunov approach is utilized to confirm the closed‐loop system’s boundedness. Finally, the proposed method’s efficacy is confirmed via simulations with a two‐satellite formation.

A Passivity-Based Framework for Safe Physical Human–Robot Interaction

In this paper, the problem of making a safe compliant contact between a human and an assistive robot is considered. Users with disabilities have a need to utilize their assistive robots for physical human–robot interaction (PHRI) during certain activities of daily living (ADLs). Specifically, we propose a hybrid force/velocity/attitude control for a PHRI system based on measurements from a six-axis force/torque sensor mounted on the robot wrist. While automatically aligning the end-effector surface with the unknown environmental (human) surface, a desired commanded force is applied in the normal direction while following desired velocity commands in the tangential directions. A Lyapunov-based stability analysis is provided to prove both the convergence as well as passivity of the interaction to ensure both performance and safety. Simulation as well as experimental results verify the performance and robustness of the proposed hybrid controller in the presence of dynamic uncertainties as well as safe physical human–robot interactions for a kinematically redundant robotic manipulator.

On the Development of a Data-Driven-Based Fractional-Order Controller for Unmanned Aerial Vehicles

Proper control is necessary for ensuring that UAVs successfully navigate their surroundings and accomplish their intended tasks. Undoubtedly, a perfect control technique can significantly improve the performance and reliability of UAVs in a wide range of applications. Motivated by this, in the current paper, a new data-driven-based fractional-order control technique is proposed to address this issue and enable UAVs to track desired trajectories despite the presence of external disturbances and uncertainties. The control approach combines a deep neural network with a robust fractional-order controller to estimate uncertainties and minimize the impact of unknown disturbances. The design procedure for the controller is outlined in the paper. To evaluate the proposed technique, numerical simulations are performed for two different desired paths. The results show that the control method performs well in the presence of dynamic uncertainties and control input constraints, making it a promising approach for enabling UAVs to track desired trajectories in challenging environments.

Force Distribution and Estimation for Cooperative Transportation Control on Multiple Unmanned Ground Vehicles.

This article presents an effective design of omnidirectional four-mecanum-wheeled vehicles to transport an object and track a predefined trajectory cooperatively. Furthermore, a novel design of the rotary platform is presented for multiple unmanned ground vehicles (m-UGVs) to load objects and provide better maneuverability in confined spaces during cooperative transportation. The number of unmanned ground vehicles (UGVs) is adjustable according to the object's weight and size in the proposed framework because transportation is accomplished without physical grippers. Moreover, to minimize the complexity in dealing with the interactive force between the object and UGVs, no force/torque sensor is used in the design of the control algorithm. Instead, an adaptive sliding-mode controller is formulated to cope with the dynamic uncertainties and smoothly transport an object along a desired trajectory. Thus, three external force analyses-gradient projection method, adaptive force estimation, and radial basis function neural network force estimation-are proposed for m-UGVs. In addition, the stability and the performance tracking of the m-UGV system in the presence of dynamic uncertainties using the proposed force estimation are investigated by employing the Lyapunov theory. Finally, experiments on cooperative transportation are presented to demonstrate the efficiency and efficacy of the m-UGV system.

Cooperative Manipulation of Deformable Objects by Single-Leader–Dual-Follower Teleoperation

This article proposes a method for single-leader–dual-follower teleoperation, where one robot (direct-follower robot, DFR) is directly teleoperated and the other robot (assisting-follower robot, AFR) can autonomously cooperate with the DFR to hold and move a deformable object, with the contact force regulated to a desired value. Since the AFR does not know its partner’s movement, first, it achieves position alignment with the DFR by using the contact force. Second, we develop an adaptive movement estimation algorithm according to the Lyapunov theory, such that DFR’s position is estimated in the presence of dynamic uncertainties. Finally, we adopt the impedance control to generate a reference trajectory for the AFR to track, in order to maintain the desired contact force. Several simulations are conducted on a platform with two three-degrees-of-freedom robots. Experiments are performed with a dual-arm robot (Baxter), results of which demonstrate the feasibility and effectiveness of the proposed method.

Adaptive Control of Wire-Borne Underactuated Brachiating Robots Using Control Lyapunov and Barrier Functions

Executing safe brachiation maneuvers with a cable-suspended underactuated robot is a challenging problem due to the complications induced by the cable dynamics. We present and experimentally implement an online adaptive controller for a wire-borne brachiating robot swinging on a vibrating cable. An adaptive function approximation approach is proposed to estimate the unknown dynamics of the flexible cable as an external force applied to the robot. Robust control Lyapunov and barrier functions are designed and incorporated into quadratic programs to synthesize a unified adaptive control framework, which formally guarantees the stability and safety of the brachiating robot in the presence of dynamic uncertainties, actuator constraints, and obstacles in the environment. The stability analysis and derivation of the adaptation law are carried out through a Lyapunov analysis. We demonstrate and validate the proposed control framework using extensive hardware experiments with an underactuated brachiating robot operating on a flexible cable. Simulation results, hardware experiments, and comparisons with a baseline controller show that the proposed quadratic programming-based controller achieves reliable tracking performance and disturbance estimation in the presence of model uncertainties, actuator limits, and safety constraints.

Adaptive Proportional-Integral Sliding Mode-Based Fault Tolerant Control for Autonomous Underwater Vehicles with Thrusters Saturation and Potential Failure

This paper focuses on the fault tolerant control of autonomous underwater vehicles (AUVs) in the presence of dynamic uncertainties and potential thruster failure issues. For this, an adaptive proportional-integral sliding mode-based fault tolerant control (APISM-FTC) is proposed to drive the AUV to follow the desired trajectory, in the event of unknown thrusters failure and thrusters saturation. Radial basis function neural network (RBFNN) and an adaptive approach are used to evaluate the dynamics uncertainty during the construction of the APISM-FTC controller. To guarantee that all tracking errors asymptotically converge to zero, a comprehensive theoretical analysis and mathematical proof based on Lyapunov stability analysis are implemented. The simulation experiments on two fault conditions are carried out, respectively, and the control effects under normal conditions are compared. It can be shown that the designed APISM-FTC method can make the system reach a stable state quickly, and can still have a good control performance in the case of the failure of the thruster.

RBFNN and projection operator based robust adaptive control with input saturation of a quadrotor equipped with two manipulators constrained to a high voltage line

The aim of this paper is to propose a robust adaptive control for a quadrotor equipped with two manipulators constrained along a high voltage line (HVL). Due to the urgent need of the HVL to inspect, the quadrotor compared to humans has many advantages such as cost and less time, higher accuracy, access to impassable points, and most importantly, reducing human victims. In this paper, two manipulators are used to hang the quadrotor from the HVL and move along it such that the quadrotor can perform monitoring operations more accurately. The motion equations are derived for the constrained quadrotor by the Lagrangian method. A control law is derived based on inverse dynamics of reduced-form equations. A robust adaptive controller is proposed for control of the constrained quadrotor in the presence of dynamic uncertainties and external disturbances. The nonlinear terms in the dynamic model are approximated by radial basis function neural network (RBFNN) and adaptive laws based on the projection operator. Moreover, saturation functions are considered for inputs in the controller by an auxiliary system. Stability analysis of the closed-loop system is performed by the Lyapunov theory. In order to confirm the effectiveness of the proposed method, some numerical simulations are carried out. The simulation results show the quadrotor can move along the HVL in the presence of the dynamic model uncertainties and the external disturbances and satisfy the considered constraints.

Active Disturbance Rejection Terminal Sliding Mode Control for Tele-Aiming Robot System Using Multiple-Model Kalman Observers

This study proposes a tele-aiming control strategy for the ground reconnaissance robot to track the maneuvering target rapidly in the presence of dynamic uncertainties, sensory measurement noises, and time-varying external disturbances. First, the tele-aiming control trajectory generated by human–computer interaction (HCI) device is filtered with a tracking differentiator and a recursive average filter. Second, the inertial impact force disturbance generated by maneuvering tele-aiming control jointly with the other uncertainties (e.g., internal friction, modeling error, etc.) is considered as a lumped disturbance, and then a novel multiple-model augmented-state extended Kalman observer (MEKO) is designed, capable of filtering out the joint measurement noises and estimating the lumped disturbance simultaneously. Lastly, a nonsingular terminal sliding mode controller is applied to eliminate the lumped disturbance and control the joints to track the corresponding desired joint trajectory. To verify the tele-aiming control performance, the random trajectory tracking experiments are designed to simulate the tele-aiming tracking control of maneuvering targets. As indicated from the experimental results, the proposed control strategy is capable of significantly suppressing the effect of inertial impact force disturbance and joint measurement noises, and achieving fast and stable tele-aiming control.

Extended-state-observer-based adaptive control of flexible-joint space manipulators with system uncertainties

In this paper, an extended-state-observer-based adaptive controller is proposed for flexible-joint space manipulators (FJSM) to accurately track trajectories while stabilizing bases in the presence of dynamic uncertainties and joint stiffness uncertainties. The dynamic model of a FJSM is established, and its state-spaced representation is obtained by introducing an error vector and a sliding mode surface vector as state variables. Besides, an extended state observer (ESO) is designed to guarantee the precise estimation of the manipulator’s velocity states as well as the joint stiffness uncertainties. Based on the ESO and the state-spaced representation, an adaptive controller is generated by implementing backstepping method, where the dynamic uncertainties are compensated by a Radial Basis Function neural network (RBFNN) and the joint stiffness uncertainties are eliminated by the estimation of the ESO. The stabilities of the ESO-based adaptive controller are validated by Lyapunov theory. Several numerical simulations were conducted, and the simulation results verifies the effectiveness of the proposed controller.

Initial configurations‐independent predefined performance control of robotic manipulator with input saturation

AbstractThis article develops a guaranteed predefined performance control scheme for a class of robotic manipulators with input saturation and unknown uncertainties. The main advantage is that it can provide convenient and quantifiable predefined performance control, by integrating the ‐class performance function in the prescribed performance control framework and designing an adaptive anti‐saturation compensator. The maximum overshoot, the settling time, and the steady‐state error can be specified independently and are not limited by the initial configurations of the robotic system, where the introduction of ‐class performance function simplifies the control design. By introducing the adaptive neural network estimator and anti‐saturation compensator, the proposed robust controller is also available in the presence of dynamic uncertainties, external disturbance, and actuator saturation. The proposed adaptive anti‐saturation compensator can be easily incorporated into the guaranteed predefined performance control, which simplifies the stability analysis. Numerical results based on simulations demonstrate the effectiveness of the proposed method.

Finite-time adaptive control for the dual-arm space robots with uncertain kinematics, dynamics and deadzone nonlinearities

Dual-arm space robots are capable of load transporting and coordinated manipulation for on-orbit servicing. However, achieving the accurate trajectory tracking performance is a big challenge for dual-arm robots, especially when mechanical system uncertainties exist. This paper proposes an adaptive control scheme for the dual-arm space robots with grasped targets to accurately follow trajectories while stabilizing base’s attitude in the presence of dynamic uncertainties, kinematic uncertainties and deadzone nonlinearities. An approximate Jacobian matrix is utilized to compensate the kinematic uncertainties, while a radial basis function neural network (RBFNN) with feature decomposition technique is employed to approximate the unknown dynamics. Besides, a smooth deadzone inverse is introduced to reduce the effects from deadzone nonlinearities. The adaption laws for the parameters of the approximate Jacobian matrix, RBFNN and the deadzone inverse are designed with the consideration of the finite-time convergence of trajectory tracking errors as well as the parameters estimation. The stability of the control scheme is validated by a defined Lyapunov function. Several simulations were conducted, and the simulation results verified the effectiveness of the proposed control scheme.

Nonlinear disturbance observer-based fault-tolerant control for flexible teleoperation systems with actuator constraints and varying time delay

In this paper, designing a control law for teleoperation systems with flexible-link slave robots in the presence of dynamic uncertainties, disturbances, actuator faults and actuator constraints with time-varying communication delays is addressed. This study proposes a simple anti-saturation nonlinear fault-tolerant controller incorporating a disturbance observer. The attractive features of the proposed controller include the ability to cope with disturbances, avoiding actuators exceeding their usual bounds, and compensating for the actuator faults. Besides which, the controller has a simple structure, does not need a fault detection mechanism, and coordinates the master’s motion speed with the slave’s actuator. A Lyapunov–Krasovskii functional is used to prove the stability and tracking performance of the teleoperation system. The feasibility and efficiency of the proposed controller are corroborated through simulation results.

An intermittent controller for robotic manipulator with uncertain dynamics in task space

An intermittent controller for robotic manipulator in the presence of dynamic uncertainties was developed in this paper. The adaptation law is designed to deal with the dynamic uncertainties. In task space, for given a desired position, the robot end-effector is able to reach the desired position under the designed intermittent controller. Different from most of the existing works on control of robotic manipulator, the designed controller only needs to receive the information of the desired position in some interval time, but not continuously. In addition, the intermittent control of robotic manipulator is discussed in task space instead of joint space. Based on an extended Barbalat’s Lemma, some simple control gains are obtained. As a direct application, we implement the proposed controller on a two-link robotic manipulator. Numerical simulations demonstrate the effectiveness of the proposed control strategy.

An adaptive robust nonsingular fast terminal sliding mode controller based on wavelet neural network for a 2-DOF robotic arm

The paper proposes a new robust adaptive control method for a two-link robot manipulator to enhance high tracking control performance despite the presence of dynamic uncertainties and unknown disturbances. The paper examines a Non-Singular Fast Time Sliding Mode (NFTSM) controller based on Wavelet Neural Network (WNN). The Wavelet Network is employed to approximate the upper bound of uncertainties and disturbances. In addition, a compensation term is added to the NFTSM control to attenuate the effect of uncertainties, including unavoidable approximation errors and unknown disturbances. Therefore, to ensure high tracking accuracy, chattering phenomenon reduction, and fast response against approximation errors and uncertainties. The parameters of the controller are tuned online by an adaptive learning algorithm, and online adaptive control laws are determined by the Lyapunov stability theorem. Due to these techniques, the suggested control drives the system to the desired performance where tracking errors converge to zero within a finite time. Simulations performed on a two-link robotic arm demonstrate a higher performance of the proposed adaptive robust NFTSMC based on WNN methodology compared to some advanced control strategies.

Robust adaptive neural trajectory tracking control of surface vessels under input and output constraints

In this paper, a novel robust adaptive control scheme is developed for the trajectory tracking of surface vessels in the presence of dynamic uncertainties, unknown time-varying disturbances and input and output constraints. Firstly, the output constraint problem is transformed into the constraint problem of trajectory tracking error by coordinate transformations. Then, a nonlinear transformation is introduced to transform the tracking error into the transformed variable, with the aid of which the output constraint problem of surface vessel trajectory tracking is transformed into the boundedness problem of transformed variable, such that the various types of output constraint boundaries including constant, time-varying, symmetry and asymmetry ones can be handled in a unified framework. An auxiliary dynamic system (ADS) is applied to handle the input saturation effect. Incorporating the nonlinear transformation, the radial basis function neural networks and the ADS into dynamic surface control technique, a novel robust adaptive neural trajectory tracking control law is designed. It is theoretically proved that all signals in the closed-loop trajectory tracking control system of surface vessels are locally uniformly ultimately bounded and the actual positions and heading of surface vessels are guaranteed to lie in the constrained region. Simulation results on a scale model vessel illustrate the effectiveness of the proposed control scheme.

A new multi-stable fractional-order four-dimensional system with self-excited and hidden chaotic attractors: Dynamic analysis and adaptive synchronization using a novel fuzzy adaptive sliding mode control method

Four-dimensional chaotic systems are a very interesting topic for researchers, given their special features. This paper presents a novel fractional-order four-dimensional chaotic system with self-excited and hidden attractors, which includes only one constant term. The proposed system presents the phenomenon of multi-stability, which means that two or more different dynamics are generated from different initial conditions. It is one of few published works in the last five years belonging to the aforementioned category. Using Lyapunov exponents, the chaotic behavior of the dynamical system is characterized, and the sensitivity of the system to initial conditions is determined. Also, systematic studies of the hidden chaotic behavior in the proposed system are performed using phase portraits and bifurcation transition diagrams. Moreover, a design technique of a new fuzzy adaptive sliding mode control (FASMC) for synchronization of the fractional-order systems has been offered. This control technique combines an adaptive regulation scheme and a fuzzy logic controller with conventional sliding mode control for the synchronization of fractional-order systems. Applying Lyapunov stability theorem, the proposed control technique ensures that the master and slave chaotic systems are synchronized in the presence of dynamic uncertainties and external disturbances. The proposed control technique not only provides high performance in the presence of the dynamic uncertainties and external disturbances, but also avoids the phenomenon of chattering. Simulation results have been presented to illustrate the effectiveness of the presented control scheme.

Distributed adaptive three‐dimension formation control based on improved RBF neural network for non‐linear multi‐agent time‐delay systems

Based on the improved radial basis function (RBF) neural networks, the distributed three-dimension formation control scheme in the presence of dynamic uncertainties is studied for non-linear multi-agent systems with time delay. A virtual leader which tracks the desired signal is followed by all agents adaptively. Linear reduced-order observers are designed on the basis of absolute and local state errors of each agent. The local state error and absolute state error are generated between neighbouring agents and each individual agent in formation, respectively. The time delay for each agent in the formation can be offset by designing a Lyapunov function, which can simplify the controller design. To deal with non-linear dynamic uncertainties and unavoidable disturbance, improved RBF neural networks are employed. In comparison with traditional RBF neural networks, improved RBF neural networks can provide better convergence performance. Subsequently, the formation controller is designed and the stability of the systems is validated by using a new Lyapunov function. Numerical simulation is conducted to demonstrate the effectiveness of the proposed method for non-linear multi-agent time-delay systems.

Data-driven global robust optimal output regulation of uncertain partially linear systems

In this paper, a data-driven control approach is developed by reinforcement learning ( RL ) to solve the global robust optimal output regulation problem ( GROORP ) of partially linear systems with both static uncertainties and nonlinear dynamic uncertainties. By developing a proper feedforward controller, the GROORP is converted into a global robust optimal stabilization problem. A robust optimal feedback controller is designed which is able to stabilize the system in the presence of dynamic uncertainties. The closed-loop system is assured to be input-to-output stable regarding the static uncertainty as the external input. This robust optimal controller is numerically approximated via RL. Nonlinear small-gain theory is applied to show the input-to-output stability for the closed-loop system and thus solves the original GROORP. Simulation results validates the efficacy of the proposed methodology.

Neural-network-based adaptive control for bilateral teleoperation with multiple slaves under Round-Robin scheduling protocol

A neural-network-based adaptive control scheme is developed for bilateral teleoperation systems with single-master-multiple-slaves in the presence of dynamic uncertainties and communication constraint. Discrete-time data transmitting communication network with bandwidth limitation and time-varying communication delays is considered and the Round-Robin scheduling protocol is used to orchestrate the data transmission from multiple slaves to the master. Stability criteria of the closed-loop system are established by constructing appropriate Lyapunov-Krasovskii functionals. Simulation studies are performed to illustrate the effectiveness of the proposed control algorithm.