Dynamic obstacles pose one of the most significant safety challenges for autonomous robots operating in cluttered environments. This paper aims to achieve collision-free obstacle avoidance without sacrificing the prescribed performance requirements in the formation control of wheeled humanoid robots (WHRs). A novel dynamic obstacle avoidance strategy is proposed by introducing a virtual barrier potential field incorporating the obstacle’s position, velocity, and the relative angle with respect to the robot. To balance the predefined performance constraints and safety/connectivity requirements, a finite-time flexible performance function is developed to ensure finite-time convergence of the formation tracking errors to predetermined regions, while the performance boundaries can adjust flexibly when the performance constraints conflict with the collision avoidance and/or connectivity maintenance requirements. Based on the dynamic obstacle avoidance strategy and the flexible performance control, a distributed finite-time flexible performance formation control strategy is constructed, which achieves finite-time convergence of the formation tracking errors, dynamic obstacle avoidance for all robots, and connectivity maintenance among initially connected robots. Finally, the effectiveness of the proposed formation control strategy is demonstrated by both simulation and experimental results.

In this paper, a fixed-time adaptive deferred constrained control strategy is proposed for a flexible single-link manipulator system with input saturation. Fuzzy Neural Networks are utilized to estimate the unknown dynamics of the flexible manipulator system as well as the errors caused by input saturation. To address output constraints imposed within a prescribed time period, a time-shift function and an adjusted barrier function are introduced. The system’s stability is rigorously proven using the direct Lyapunov method. Finally, numerical simulations and experimental results are presented to validate the effectiveness and superiority of the proposed control approach.

This study focuses on the reinforcement learning (RL)-based consensus tracking control of nonlinear multiagent robot systems (MARSs) with event triggering mechanism. Each agent of the MARSs is composed of a three-link rigid robot and a flexible payload, which can be assumed to be a Eulbernoulli beam. Based on the assumed mode method (AMM), the infinite distributed parameter model of the robot–payload system is approximated as a finite dimension model, and the dynamic performance of the robot system is controlled with the use of boundary control input. First, a RL control strategy based on actor–critic structure is adopted to maintain the consensus angles tracking of all agents while suppress the load vibration. Second, considering the communication bandwidth problem in practical applications, an event-triggered mechanism is utilized to reduce the transmission burden based on relative threshold strategy. Furthermore, the semi-global uniformly ultimately bounded (SGUUB) property of the closed-loop system is derived to guarantee the state errors can converge to the small neighborhoods of the origin. Finally, the effectiveness of the proposed control strategy is demonstrated by numerical simulations.

A neuro adaptive control for deploying a partial differential equation-based multi-agent system in 3D space with a non-collocated observer is proposed in this study. Since the full states of the system are unavailable in practice, an observer-based control is developed to ensure stability of the underlying closed-loop system. In addition, the system uncertainty is addressed by introducing a neural network control. By choosing appropriate system parameters, the desired control objectives can be achieved. The proposed strategy is simple to implement and its implementation condition is easily satisfied. Finally, the effectiveness of the designed method is verified by the simulation results. Note to Practitioners— In this paper, we introduce a neuro-adaptive control strategy for a multi-agent system based on partial differential equations in 3D space, using a non-collocated observer. This approach is particularly relevant for practitioners dealing with dynamic and uncertain environments in control systems. Neural networks are employed to manage system uncertainties, adapting to changing conditions, which is crucial in environments with variable system parameters. The non-collocated observer allows for state estimation, beneficial in situations where direct measurement is impractical. Ensuring the observer’s accuracy is key for effective control. Our strategy focuses on simplicity and ease of implementation, making it accessible for integration into existing systems. The observer-based control ensures the stability of the closed-loop system, a critical factor for consistent performance.

This paper addresses the problem of distributed formation-containment tracking control for uncertain multi-agent systems (MASs) with completely unknown system nonlinearities, denial-of-service (DOS) attacks, and switching communication topologies. To enhance the system robustness, neural networks (NNs) are utilized to identify the unknown nonlinear terms. Additionally, a novel distributed observer is designed to reconstruct the external unmeasured attack dynamics. To handle the switching topologies of MASs, a mechanism using piecewise continuous functions is proposed to counteract the unexpected controller actions during switching time instants. Furthermore, the clever design of barrier Lyapunov functions aids in achieving the required predefined performance. A fractional power nonlinear filter is introduced to tackle the problem of computational complexity. By applying the local neighborhood states information and the lyapunov stability theory, the presented control method evaluates the stability of the MASs and shows that the designed controller not only enables the system output to track a formation trajectory in the presence of external attacks but also converges the consensus errors into a predefined set. Finally, simulation results are provided to validate the effectiveness of the proposed control strategy.

In this study, a novel fault-tolerant broad fuzzy learning control scheme is proposed for a flexible single-link manipulator with input delay and output constraints. The broad fuzzy neural network is utilized to effectively approximate the time delay of the controller and compensate for the unknown nonlinear uncertainties. By applying the Lyapunov direct method and barrier Lyapunov function, the semi-global uniformly ultimately boundedness (SGUUB) of the system is demonstrated, which guarantees that all system states converge to zero within the specified limitation. Finally, the simulation and experiment results, compared with those of different neural networks, manifest the validity of the proposed control method.

This study proposes a neural-network (NN)-based adaptive fixed-time control method for a two-degree-of-freedom (2-DOF) nonlinear helicopter system with input quantization and output constraints. First, a hysteresis quantizer is employed to mitigate chattering during signal quantization, and adaptive variables are utilized to eliminate errors in the quantization process. Subsequently, the system uncertainties are approximated using a radial basis function NN. Simultaneously, a logarithmic barrier Lyapunov function (BLF) is constructed to prevent the system outputs from violating the constraint boundaries. Based on a rigorous Lyapunov stability analysis and the fixed-time stability criterion, the signals of the closed-loop system are proven to be bounded within a fixed time. Finally, numerical simulations and experiments verified the feasibility of the proposed method.

This study investigates a multitrajectory tracking control strategy for an unknown 2-DOF helicopter system, integrating deterministic learning (DL) and reinforcement learning (RL). Initially, DL theory is applied to identify the local unknown dynamics of a 2-DOF helicopter system using radial basis function neural networks (RBFNNs). Subsequently, the identified dynamic knowledge is expressed and stored using constant RBFNNs. To mitigate the issue of partial knowledge failure due to deviations between the actual and learned trajectories, we introduce a RL framework for dynamic compensation. Finally, a composite control strategy incorporating both nominal and auxiliary components is designed to achieve multitrajectory tracking control. The stability of the closed-loop system is analyzed and demonstrated using the Lyapunov direct method. The simulation and experimental results demonstrate the effectiveness of the proposed control strategy.

With the burgeoning growth of the maritime economy, marine risers have emerged as reliable and convenient conduits for the transport of oil and natural gas. However, these risers are vulnerable to vibrational disturbances, which can adversely impact system performance and induce fatigue damage. Therefore, effective vibration control strategies are required to address this issue. This study introduces an innovative adaptive quantized fault-tolerant control strategy designed to attenuate vibrations in a three-dimensional (3-D) riser-vessel system against the effects of actuator faults, unknown control direction, and external disturbances. Different from previous findings, the suggested controller can directly counteract the nonlinear component stemming from actuator faults and handle the nonlinear decomposition inherent to the quantizer, without the necessity for upper-limit estimation. Furthermore, to tackle the input saturation, control laws are formulated using the hyperbolic tangent operator. Finally, the proposed controller’s effectiveness and robustness are validated through thorough Lyapunov analysis and numerical simulations, affirming the system’s uniformly bounded stability.

While a deadlock control problem in complex resource allocation systems (RASs) has been extensively studied in the literature, the corresponding results that are applicable to assembly systems are quite limited, both, in terms of structural analysis of deadlocks and deadlock resolution. Taking Petri nets (PNs) as a modeling and analysis tool, this article focuses on the deadlock control problem for a forward-conflict free net (FCFN), which allows for batch assembly and multiple resource allocations. First, a new structural characterization of deadlocks in FCFN is proposed through two structural objects: 1) circuit and 2)$\omega $-structure. The starting point for this is motivated by the fact that deadlocks in assembly systems stem not only from the circular wait of resources but also the parts waiting for their assembly with other parts. Subsequently, based on these two objects, a necessary and sufficient condition about FCFNs liveness is obtained: 1) an FCFN is live if and only if no circuit and 2)$\omega $-structure are saturated at any reachable marking. Finally, in order to prevent each such object from inducing deadlocks, a hierarchical search algorithm appropriate for real-time implementation is developed to avoid its saturation. The proposed algorithm is proven to be capable of ensuring the deadlock-free operation of FCFNs. Moreover, several examples are provided to demonstrate its effectiveness.

Aiming to address the time-varying formation control problem of uncertain and networked multiagent systems, this article develops a novel robust constraint-following control approach, which is implemented in two steps. First, a kinematic model (i.e., kinematic constraint) for each agent is constructed by employing local tracking errors; this constraint mainly ensures formation maintenance and trajectory following. Second, based on the kinematic model, and to attenuate time-varying uncertainties, a robust constraint-following controller is meticulously designed. This controller not only ensures the strict satisfaction of kinematic constraints but also sufficiently suppresses time-varying uncertainties. Ultimately, an application to quadrotor swarm is provided to illustrate the proposed control scheme.

This study presents an adaptive event-triggered control methodology for flexible manipulator systems with prescribed performance and input backlash. To reduce the communication burden between the controllers and actuators, we consider a relative threshold event-triggered mechanism. Then, an adaptive inverse function is applied to eliminate the input backlash of the actuator, and a neural network is adopted to handle the system uncertainty. It is proven that the proposed control approach not only ensures the tracking error converges to a small region close to zero within the prescribed time but also significantly reduces overshoot by using Lyapunov’s direct method. Furthermore, the efficacy of the scheme proposed is demonstrated through numerical simulations and experiments.

This article presents a disturbance observer (DO)-based neural network (NN) control for a two-degree-of-freedom (2-DOF) helicopter system with input saturation, external disturbances, and output constraints. First, the uncertainties in the helicopter system are approximated using a radial basis function NN. Subsequently, a DO is used to approximate unknown compound disturbances, involving errors from NN estimation, input saturation, and external disturbances. To address the issue of output constraints imposed at a prescribed time period, a novel time-shift function and an adjusted barrier function are employed. Through the direct Lyapunov method, the boundedness of all control signals in the closed-loop system is verified. Finally, the effectiveness of the proposed control method is validated through numerical simulation results.

This study focuses on the novel reinforcement learning control strategy of a nonlinear two-degrees-of-freedom (2-DOF) helicopter system for tracking the desired trajectory while minimizing the tracking error. First, gradient descent algorithm is incorporated in the context of the reinforcement learning control scheme to obtain the adaptive laws. Subsequently, considering the uncertainties in the nonlinear system, radial basis function (RBF) neural networks (NNs) are exploited to approximate the unknown internal dynamics. In contrast to the previous studies, aiming at accelerating the convergence in reinforcement learning control, a barrier Lyapunov function is constructed to constrain the states to ensure that the tracking error rapidly converges to a neighborhood of zero. Under the proposed control strategy, the states of the closed-loop system are proven to be semi-globally uniformly ultimately bounded through rigorous Lyapunov analyses, and the state constraints are satisfied. Furthermore, the simulations and experiments conducted on a Quanser laboratory platform reveal that the proposed control functions are suitable and effective. Note to Practitioners—This paper is motivated by designing a reinforcement learning control strategy to enhance online learning capability and control performance of the controller for a nonlinear 2-DOF helicopter system. The control framework is divided into the design of the critic and actor NNs, responsible primarily for evaluating the control performance and approximating uncertainties in the system separately. Unlike the adaptive NN control, the actor NN weights are updated by combining information of states and inputs from the critic NN. In addition, aiming at accelerating the convergence, a barrier Lyapunov function is constructed to constrain the states to ensure that the tracking error rapidly converges to a neighborhood of zero. Finally, the proposed control strategy is validated in simulation and experiment on the Quanser laboratory platform.

We investigate the trajectory tracking problem of underactuated aerial vehicles with unknown mass in the presence of unknown non-vanishing disturbances using an event-triggered approach, while considering the constraint that the derivative of the reference trajectory is not available. In contrast to existing references where the derivative of the reference trajectory is needed, here we first introduce a high-gain observer to estimate the unknown derivative solely from the reference trajectory. A disturbance observer is designed to compensate for non-vanishing disturbances, such as wind, etc. Fuzzy logic systems are used to approximate the model uncertainty arising from the unknown mass of the vehicle, and then we derive a thrust command law that follows from a desired stabilizing force. Additionally, unlike traditional fixed and relative threshold strategies that rely solely on control signals, we develop a new time-varying eventtriggered mechanism linked to the performance of the controlled system, taking into account factors such as tracking errors, to develop angular velocity commands, enhancing tracking accuracy while efficiently conserving communication resources, especially in the absence of Zeno behavior. We present simulation results to demonstrate the efficacy of the proposed approach and validate the theoretical findings.

Limited computational resource is one of the features of the embedded system including unmanned marine surface vehicle (MSV). Under this practical constraint, we aim to develop a distributed formation control algorithm for a group of uncertain MSVs, whose computational burden is low. The uncertainty and disturbance estimator (UDE) is employed to compensate for the modeling uncertainties, the unknown environmental disturbances, and the unavailable derivative of the virtual control inputs, such that the formation errors converge asymptotically to zero. Meanwhile, the prescribed performance control technique is applied to ensure that the convergence rates of the formation errors are faster than predefined values. In addition, collision avoidance and connectivity preservation among the neighboring vehicles are guaranteed, while the obstacle avoidance is also ensured. As no parameter update law is required to calculate, the computational burden is reduced significantly. The effectiveness of the proposed UDE-based formation control algorithm is validated through comparative simulation.

This article proposes an adaptive inverse neural network (NN) control of an uncertain flexible single-link manipulator with input backlash and output constraint. First, an adaptive inverse function is applied to eliminate the input backlash of the actuator. Second, an NN is applied to approximate the system uncertainty. Third, a barrier Lyapunov function is used to guarantee that the system is maintained within the constraints. Subsequently, the system’s semi-globally uniformly ultimately bounded stability is proved by the Lyapunov direct method. Finally, the simulation and experimental results manifest the feasibility of the proposed controller.

For multiple flexible manipulator systems under an undirected switching topology graph with actuator faults, dead zones, and external disturbances, the proposed distributed control technique aims to attain joint angle consensus and vibration suppression. Therefore, in boundary controller design, the Nussbaum function is utilized to address the issue of uncertain control direction caused by fault tolerance and dead zones. Then, under the condition that the switching topology is connected, by using the backstepping technique, a new Lyapunov function is introduced so that the multiple flexible manipulators system can ensure angle cooperative control and vibration suppression while not knowing the control direction. Moreover, the adaptive law is designed to handle compound disturbances consisting of the unknown dead zones, additive faults, and boundary disturbances. Furthermore, we show that the flexible manipulator system is uniformly ultimately bounded and stable according to the Lyapunov stability theory. Ultimately, through the numerical simulation, the control strategy’s efficacy is confirmed.

Recently, with the development of the marine economy, marine risers have garnered increasing attention as they present facile and reliable methods for oil and gas transportation. However, these risers are susceptible to vibrations, which can lead to system performance degradation and fatigue damage. Therefore, effective vibration control strategies are required to address this issue. In this study, a novel adaptive fault-tolerant control (FTC) strategy is adopted to suppress the vibrations of a 3-D riser-vessel system against the effects of actuator failures, backlash-like hysteresis, and external disturbances. A barrier-based Lyapunov function is merged to eliminate the time-varying output constraints of the system. Adaptive FTC laws with projection mapping operators are designed to compensate for parameter uncertainties and consider input nonlinearities to improve system robustness. Finally, a rigorous Lyapunov analysis and numerical simulations are performed to verify the validity of the proposed controller and guarantee uniformly bounded stability of the system.

An adaptive neural network (NN) control is proposed for an unknown two-degree of freedom (2-DOF) helicopter system with unknown backlash-like hysteresis and output constraint in this study. A radial basis function NN is adopted to estimate the unknown dynamics model of the helicopter, adaptive variables are employed to eliminate the effect of unknown backlash-like hysteresis present in the system, and a barrier Lyapunov function is designed to deal with the output constraint. Through the Lyapunov stability analysis, the closed-loop system is proven to be semiglobally and uniformly bounded, and the asymptotic attitude adjustment and tracking of the desired set point and trajectory are achieved. Finally, numerical simulation and experiments on a Quanser's experimental platform verify that the control method is appropriate and effective.

This study is aimed to design a fault-tolerant control using a broad learning neural network (BLNN) for a two-degree-of-freedom (2-DOF) nonlinear helicopter system. Compared with the conventional radial basis function neural network, the BLNN can approximate uncertainties and unknown functions with smaller tracking errors by adding incremental and enhancement nodes. Considering possible actuator faults in during actual application, an adaptive auxiliary parameter is established to prevent their effects on control. Through direct Lyapunov method, the stability and convergence of the closed-loop system are analyzed. The results from simulations and experiments conducted on a 2-DOF helicopter laboratory platform of Quanser demonstrate the validity and feasibility of the proposed control method.

This article proposes an adaptive control for a flexible manipulator (FM) under the influence of distributed disturbances, unknown dead zones, and input quantization. First, the hybrid effect of the unknown dead zone and input quantization is formulated and represented based on some essential transformations. Then, an adaptive robust quantized control with online updating laws is developed to address the uncertainty of the dead zone, ensure robustness and angle position, and dampen the vibration in the FM system. Subsequently, the Lyapunov theoretical analysis is employed to ensure the bounded stability of the system. Finally, numerical simulations and experiments with a Quanser platform are given to further verify the feasibility and superiority of the designed scheme.

In this article, we propose a new adaptive fuzzy fault-tolerant control (FTC) for a three-dimensional riser-vessel system with unknown backlash nonlinearity. A model for the smooth inverse dynamics of the backlash is introduced; then, the control input is divided into an expected input and a compensation error. Considering the imprecision of system modeling and unknown external disturbances, we employ a fuzzy adaptive technology to achieve compensation. By incorporating the actuator fault term and backlash error, the adaptive FTC is developed to resolve loss faults in the actuator and compensate for the unknown backlash to some extent. The direct Lyapunov method is used to demonstrate the system’s bounded stability. Finally, simulation results demonstrate the effectiveness of the derived scheme.

This study focuses on an adaptive fault-tolerant boundary control (BC) for a flexible string (FS) in the presence of unknown external disturbances, dead zone, and actuator fault. To tackle these issues, by employing some transformations, a part of the unknown dead zone and external disturbance can be regarded as a composite disturbance. Subsequently, an adaptive fault-tolerant BC is developed by utilizing strict formula derivations to compensate for unknown composite disturbance, dead zone, and actuator fault in the FS system. Under the proposed control strategy, the closed-loop system proves to be uniformly ultimately bounded, and the vibration amplitude is guaranteed to converge ultimately to a small compact set by choosing suitable design parameters. Finally, a numerical simulation is performed to demonstrate the control performance of the proposed scheme.

We propose an adaptive neural-network-based fault-tolerant control scheme for a flexible string considering the input constraint, actuator gain fault, and external disturbances. First, we utilize a radial basis function neural network to compensate for the actuator gain fault. In addition, an observer is used to handle composite disturbances, including unknown approximation errors and boundary disturbances. Then, an auxiliary system eliminates the effect of the input constraint. By integrating the composite disturbance observer and auxiliary system, adaptive fault-tolerant boundary control is achieved for an uncertain flexible string. Under rigorous Lyapunov stability analysis, the vibration scope of the flexible string is guaranteed to remain within a small compact set. Numerical simulations verify the high control performance of the proposed control scheme.

In this study, we propose an adaptive fuzzy event-triggered control scheme for an autonomous aerial refueling hose system involving uncertainty, an event-triggered mechanism, and actuator failures. The unknown nonlinear function is approximated using the designed fuzzy logic systems. Through introduction of the adaptive compensation scheme, the problem of an infinite number of actuator failures, including partial and complete failures, is solved. In addition, the event-triggered control strategy is designed to achieve vibration suppression while decreasing the communication burden between the controllers and actuators. The stability of the closed-loop system is demonstrated via the Lyapunov direct method. Finally, simulation examples are presented to confirm the validity of the proposed control scheme.

This study addresses adaptive fuzzy control for an axially moving slung-load cable system (AMSLCS) of a helicopter in the presence of an actuator fault, system uncertainty, and disturbances with the aid of a fuzzy logic system (FLS). The actuator fault considered is depicted by a more general faulty plant that includes an unknown actuator gain fault and a fault deviation vector. First, to compensate for system uncertainty and the fault deviation vector, a fuzzy control technique is adopted. Then, under the introduced FLS, a novel adaptive fuzzy control law is developed by employing a rigorous Lyapunov derivation. The closed-loop system of the AMSLCS is proved to be uniformly bounded even when considering the actuator fault, system uncertainty, and disturbances. Finally, a simulation is executed to expound the performance of the developed controller.

This article presents an adaptive control method for dual-arm robot systems to perform bimanual tasks under modeling uncertainties. Different from the traditional symmetric bimanual robot control, we study the dual-arm robot control with relative motions between robotic arms and a grasped object. The robot system is first divided into two subsystems: a settled manipulator system and a tool-used manipulator system. Then, a command filtered control technique is developed for trajectory tracking and contact force control. In addition, to deal with the inevitable dynamic uncertainties, a radial basis function neural network (RBFNN) is employed for the robot, with a novel composite learning law to update the NN weights. The composite learning is mainly based on an integration of the historic data of NN regression such that information of the estimate error can be utilized to improve the convergence. Moreover, a partial persistent excitation condition is employed to ensure estimation convergence. The stability analysis is performed by using the Lyapunov theorem. Numerical simulation results demonstrate the validity of the proposed control and learning algorithm.

This article investigates the adaptive robust spatial vibration control for a flexible Timoshenko manipulator subject to input dead-zone nonlinearity characteristic. The “disturbance-like” terms and dead-zone nonlinearity are first incorporated into the context of control design, and the new boundary robust adaptive control laws are constructed to reduce the shear deformation and elastic oscillation, ensure the expected angle orientation, handle the input dead-zone, and estimate the upper bound of compound disturbances. The convergence of states and the stability of the system are analyzed and proven without simplifying the infinite dimensional dynamics. In the end, the effectiveness of the presented scheme is demonstrated by the result of simulation research.

As the control tasks become complex, fulfilling such tasks cooperatively is the first choice in practice. In this article, the output consensus problem of heterogeneous multiagent systems is studied by deploying distributed observers in follower agents. Each observer in the follower only measures part of the leader’s output, which relieves the burden of a simple agent when the leader’s output is of large-scale dimensions. Then, all followers in the system work cooperatively to estimate the full state of the leader. By using parameterized Riccati equation and output regulation theory, sufficient conditions are given to design the distributed observers and the output consensus protocol. Finally, a numerical example is conducted to verify the obtained result.

This study is concerned with the dynamical analysis and vibration attenuation for a three-dimensional (3-D) suspension cable system of a helicopter preceded by output constraints and input backlash. First, the dynamic model of a 3-D suspension cable is constructed in accordance with Hamilton’s principle. Then, a backlash inverse compensator is established to tackle the input backlash nonlinearities. Subsequently, three boundary controllers together with output signal barrier functions are proposed to dampen the oscillations while not violating the cable swing constraints. The uniform boundedness of the closed-loop system is guaranteed with recourse to the direct Lyapunov’s method. Finally, the validity and efficiency of the derived control laws are testified through simulation results.

In this study, an adaptive robust control technique for an uncertain riser-vessel system in a three-dimensional space is developed. A projection mapping technique and a hyperbolic tangent function are exploited to construct novel adaptive robust controllers based on adaptive laws dynamically updated online to restrain the vibration, tackle parametric uncertainties, compensate for the unknown upper bound of disturbances, and ensure robustness of the coupled system. Lyapunov’s method is adopted to analyze and demonstrate the bounded stability of the closed-loop system. Simulation results are provided to validate the feasibility and effectiveness of the proposed approach.

In this article, a new neural-network-based sliding-mode control (SMC) of an uncertain robot is presented. The distinguishing characteristic of the proposed control scheme is that the switching gain is designed as a dynamic model approximated value, which is handled by using the neural-network strategy to adapt the unknown dynamics and disturbances. In the presented control scheme, the modeling information of the robotic system is not required and only one parameter is required to be estimated in each joint of the robotic system. Subsequently, the Lyapunov method is utilized to prove that the trajectory tracking errors will eventually converge to a neighborhood of zero. Finally, the contrast simulation studies reveal that with the proposed control scheme, the problems of chattering and high-speed switching of control input, which takes place in a conventional SMC, can be addressed, and a satisfactory control precision is guaranteed.

This article develops an adaptive neural-network (NN) boundary control scheme for a flexible manipulator subject to input constraints, model uncertainties, and external disturbances. First, a radial basis function NN method is utilized to tackle the unknown input saturations, dead zones, and model uncertainties. Then, based on the backstepping approach, two adaptive NN boundary controllers with update laws are employed to stabilize the like-position loop subsystem and like-posture loop subsystem, respectively. With the introduced control laws, the uniform ultimate boundedness of the deflection and angle tracking errors for the flexible manipulator are guaranteed. Finally, the control performance of the developed control technique is examined by a numerical example.

This article involves the adaptive inverse control of a coupled vessel-riser system with input backlash and system uncertainties. By introducing an adaptive inverse dynamics of backlash, the backlash control input is divided into a mismatch error and an expected control command, and then a novel adaptive inverse control strategy is established to eliminate vibration, tackle backlash, and compensate for system uncertainties. The bounded stability of the controlled system is analyzed and demonstrated by exploiting the Lyapunov's criterion. The simulation comparison experiments are finally presented to verify the feasibility and effectiveness of the control algorithm.

This article presents a novel controller design to ensure the prescribed performance of a vibrating flexible beam system that possesses boundary output constraints and external disturbances. First, boundary output constraints are visualized as the prescribed performance characteristics, and a smooth performance function along with a proper error transformation is introduced. Second, a boundary constrained controller with a disturbance observer is developed to achieve the present performance, cope with the external disturbance, and stabilize the vibration in the beam system. Third, the principle of invariance is used for rigorous analysis and to ensure the system’s asymptotic stability. Finally, the result of simulation research shows the effectiveness of the suggested approach.

This paper examines the boundary disturbance observer-based control for a vibrating single-link flexible manipulator system possessing external disturbances. Two new boundary anti-disturbance control strategies are presented to eliminate vibration, track disturbance, and determine angle position for the flexible manipulator system. Achieving rigorous analysis with no model reduction, the derived control can ensure the angle positioning and bounded stability in the controlled system. By appropriately designing parameters, the resulting simulation results can demonstrate the control performance.

This paper focuses on adaptive robust vibration control for flexible riser systems affected by input nonlinearities and unknown external disturbances. An auxiliary system is constructed and adjusted to develop a boundary adaptive robust control for restraining the vibrational offset and eliminating the effect of input nonlinearities. Besides, an adaptive law of boundary disturbance upper-bound is constructed together with the vibration control strategy to estimate the magnitude of unknown boundary disturbance. Further, the convergence of states and stability of the system are ensured with the developed control scheme. By choosing the proper control parameters, the control performance is verified with the obtained simulation results.

This paper presents a framework of output feedback stabilization of an axially moving accelerated system influenced by exogenous disturbances. Based on the flexible-rigid coupled dynamical model of the system, a boundary output feedback control is developed to reduce the vibration and estimate the boundary disturbance by fusing Lyapunov's synthetic approach, disturbance rejection theory, and observer technique. Under the developed control, the exponential convergence of system state observer errors and uniformly ultimately bounded stability of the controlled system can be demonstrated adopting rigorous theoretical analysis, and the control performance of the developed control method is verified by simulation results.