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The purpose of this paper is to find an omnidirectional robust gust response stabilization (GRS) scheme with anti-disturbance and state-limited features.

Disturbance observer and barrier Lyapunov techniques, which can, respectively, estimate the lumped disturbances of the dynamic system in real-time and ensure the middle states within some prescribed ranges according to some flight safety indexes.

In the existing literature, almost all of the GRS controllers are either only for the longitudinal dynamics or only for the latitudinal dynamics. Few studies have considered the gust response alleviation problem with omnidirectional wind disturbance and full aircraft model.

This paper proposes a fresh scheme to deal with a more holistic GRS problem; the disturbance observer based (DOB) barrier Lyapunov backstepping longitudinal controller has been put forward; DOB nonlinear dynamic inversion to handle the multi-input-multi-output lateral dynamics; and to closely connect the two loops of the latitudinal dynamics, a manipulating variable conversion method is proposed.

Considering the complexity of dynamic and friction modeling, this paper aims to develop an adaptive trajectory tracking control scheme for robot manipulators in a universal unmodeled method, avoiding complicated modeling processes.

An augmented neural network (NN) constituted of radial basis function neural networks (RBFNNs) and additional sigmoid-jump activation function (SJF) neurons is introduced to approximate complicated dynamics of the system: the RBFNNs estimate the continuous dynamic term and SJF neurons handle the discontinuous friction torques. Moreover, the control algorithm is designed based on Barrier Lyapunov Function (BLF) to constrain output error.

Lyapunov stability analysis demonstrates the exponential stability of the closed-loop system and guarantees the tracking errors within predefined boundaries. The introduction of SJFs alleviates the limitation of RBFNNs on discontinuous function approximation. Owing to the fast learning speed of RBFNNs and jump response of SJFs, this modified NN approximator can reconstruct the system model accurately at a low compute cost, and thereby better tracking performance can be obtained. Experiments conducted on a manipulator verify the improvement and superiority of the proposed scheme in tracking performance and uncertainty compensation compared to a standard NN control scheme.

An enhanced NN approximator constituted of RBFNN and additional SJF neurons is presented which can compensate the continuous dynamic and discontinuous friction simultaneously. This control algorithm has potential usages in high-performance robots with unknown dynamic and variable friction. Furthermore, it is the first time to combine the augmented NN approximator with BLF. After more exact model compensation, a smaller tracking error is realized and a more stringent constraint of output error can be implemented. The proposed control scheme is applicable to some constraint occasion like an exoskeleton and surgical robot.

This paper aims to design an event-triggered adaptive prescribed performance controller for flexible manipulators, with the primary objectives of achieving output performance constraints and addressing communication resource limitations.

The authors propose a novel prescribed performance barrier Lyapunov function (PP-BLF) that considers both output and tracking performance constraints. The PP-BLF ensures that the system's output, transient behavior and steady-state performance, adhere to prescribed constraints. The boundary of the PP-BLF is established by an exponential function that decays over time. Notably, the PP-BLF can be applied seamlessly in unconstrained cases without necessitating controller redesign. Moreover, the controller design incorporates an event-triggered mechanism, effectively reducing the frequency of controller updates and optimizing the utilization of communication resources. Additionally, the authors employ adaptive techniques to estimate the system's unknown parameters and approximate unknown nonlinear functions using radial basis function neural networks (RBFNN). To address the challenge of “complexity explosion”, dynamic surface technology is employed.

Numerical simulations are conducted under five different cases to verify the effectiveness of the proposed controller. The results demonstrate that the controller successfully constrains the output tracking error within the prescribed performance boundary. Moreover, compared with the traditional time-triggered mechanism, the event-triggered mechanism significantly reduces the controller's update frequency, resolving the problem of limited communication resources.

The paper reduces the update frequency of control signals and improves resource utilization through an event-triggered mechanism in the form of relative thresholds. The authors recognize that the event-triggered mechanism may impact the output performance of the system. To address this challenge, the authors propose a prescribed performance Barrier Lyapunov Function (PP-BLF). The PP-BLF is designed to effectively constrain the output performance of the system, ensuring satisfactory control even when the control signal updates are reduced.

This paper aims to study the application of reinforcement learning (RL) in the control of an output-constrained flapping-wing micro aerial vehicle (FWMAV) with system uncertainty.

A six-degrees-of-freedom hummingbird model is used without consideration of the inertial effects of the wings. A RL algorithm based on actor–critic framework is applied, which consists of an actor network with unknown policy gradient and a critic network with unknown value function. Considering the good performance of neural network (NN) in fitting nonlinearity and its optimum characteristics, an actor–critic NN optimization algorithm is designed, in which the actor and critic NNs are used to generate a policy and approximate the cost functions, respectively. In addition, to ensure the safe and stable flight of the FWMAV, a barrier Lyapunov function is used to make the flight states constrained in predefined regions. Based on the Lyapunov stability theory, the stability of the system is analyzed, and finally, the feasibility of RL in the control of a FWMAV is verified through simulation.

The proposed RL control scheme works well in ensuring the trajectory tracking of the FWMAV in the presence of output constraint and system uncertainty.

A novel RL algorithm based on actor–critic framework is applied to the control of a FWMAV with system uncertainty. For the stable and safe flight of the FWMAV, the output constraint problem is considered and solved by barrier Lyapunov function-based control.

The purpose of this study is to improve vehicles’ brake stability, the problem of constraint control for an antilock braking system (ABS) with asymmetric slip ratio constraints is concerned. A nonlinear control method based on barrier Lyapunov function (BLF) is proposed not only to track the optimal slip ratio but also to guarantee no violation on slip ratio constraints.

A quarter vehicle braking model and Burckhardt’s tire model are considered. The asymmetric BLF is introduced into the controller for solving asymmetric slip ratio constraint problems.

The proposed controller can implement ABS zero steady-state error tracking of the optimal wheel slip ratio and make slip ratio constraints flexible for various runway surfaces and runway transitions. Simulation and experimental results show that the control scheme can guarantee no violation on slip ratio constraints and avoid self-locking.

The slip rate equation with uncertainties is established, and BLF is introduced into the design process of the constrained controller to realize the slip rate constrained control.

This paper aims to study the boundary disturbance rejection control design for a flexible Timoshenko robot arm to diminish external disturbances and achieve desired angle tracking, with system vibration and elastic deformation considered.

This study introduces disturbance observer and disturbance rejection mechanism into the boundary control design for flexible Timoshenko robot arm systems. The uniform bounded stability of controlled systems is proved via Lyapunov analysis without any simplification of the infinite-dimensional system dynamics.

The proposed boundary disturbance rejection control scheme can effectively suppress vibrations and shear deformations, achieve the required angular positioning and reject external disturbances. Numerical simulations developed by the finite difference method are adapted to demonstrate the validity of the designed controller.

The originality of this study is to design boundary disturbance rejection control to suppress vibrations and shear deformations for the flexible Timoshenko robot arm, thereby improving the performance and control accuracy of the system.

The purpose of this study is to propose an adaptive fault-tolerant control approach based on output feedback for a class of quadrotor unmanned aerial vehicles system. In the event of a controlled actuator failure, a stable flying of the aircraft can be achieved by selecting an appropriate sliding mode surface.

Aiming at the actuator failure of quadrotor aircraft during flight in the controllable range, a dynamic surface sliding mode passive fault-tolerant controller based on output feedback is designed based on the strong robustness of sliding mode method. Due to the unknown nonlinearity dynamics and parameter uncertainties in the system, a nonlinear observer is used to estimate them online.

The stability of the suggested algorithm is established using appropriate Lyapunov functions, and the performance of the proposed control approach is demonstrated using hardware-in-the-loop simulation.

An error performance function is introduced into the controller to ensure the convergence speed and accuracy of errors are within the predetermined range. By using the norm estimation method, there is only one parameter that needs to be updated in each step of the control process, which considerably minimizes the calculation burden. Finally, the validity of the proposed control scheme is verified on the hardware-in-the-loop simulation, and the results show that the proposed control method has achieved the desired results.

This study aims to propose a novel lightweight tendon-driven musculoskeletal arm (LTDM-arm) robot with a flexible series–parallel mixed skeletal joint structure and modularized artificial muscle system (MAMS). The proposed LTDM-arm exhibits human-like flexibility, safety and operational accuracy. In addition, to improve the safety and stability of the LTDM-arm, a control method is proposed to solve local artificial muscle overload accidents.

The proposed LTDM-arm comprises seven degrees of freedom skeletons, 15 MAMSs and various sensor systems (joint sensing, muscle tension sensing, visual sensing, etc.). It retains the morphology of a human skeleton (humerus, ulna and radius) and a simplified muscle configuration. This study proposes an input saturation control with full-state constraints to reduce local artificial muscle overload accidents caused by redundant muscle tension calculations.

3D circular trajectory experiments were conducted to verify the stability of the control method and the flexibility of the LTDM-arm. The results showed that the average error of the muscle length was approximately 0.35 mm (0.38%), which indicates that the proposed control scheme can make the output follow the target trajectory while ensuring constraint satisfaction.

The human arm is capable of performing compliant operations rapidly, flexibly and robustly in unstructured environments. Existing musculoskeletal arm robots lack simulations of the full morphology of the human arm and are insufficient in dexterity. However, the flexibility and safety features of the proposed LTDM-arm were consistent with that of the human arm. Therefore, this study offers a new approach for investigating the advantages of the musculoskeletal system and the concepts of muscle control.

The purpose of this paper is to investigate the pose control of rigid spacecraft subject to dead-zone input, unknown external disturbance and parametric uncertainty in space maneuvering mission.

First, a 6-Degree of Freedom (DOF) dynamic model of rigid spacecraft with dead-zone input, unknown external disturbances and parametric uncertainty is derived. Second, a super-twisting-like fixed-time disturbance observer (FTDO) with strong robustness is developed to estimate the lumped disturbances in fixed time. Based on the proposed observer, a non-singular fixed-time terminal sliding-mode (NFTSM) controller with superior performance is proposed.

Different from the existing sliding-mode controllers, the proposed control scheme can directly avoid the singularity in the controller design and speed up the convergence rate with improved control accuracy. Moreover, no prior knowledge of lumped disturbances’ upper bound and its first derivatives is required. The fixed-time stability of the entire closed-loop system is rigorously proved in the Lyapunov framework. Finally, the effectiveness and superiority of the proposed control scheme are proved by comparison with existing approaches.

The proposed NFTSM controller can merely be applied to a specific type of spacecrafts, as the relevant system states should be measurable.

A NFTSM controller based on a super-twisting-like FTDO can efficiently deal with dead-zone input, unknown external disturbance and parametric uncertainty for spacecraft pose control.

This investigation uses NFTSM control and super-twisting-like FTDO to achieve spacecraft pose control subject to dead-zone input, unknown external disturbance and parametric uncertainty.

This paper aims to investigate an adaptive prescribed performance control problem for switched pure-feedback non-linear systems with input quantization.

By using the semi-bounded continuous condition of non-affine functions, the controllability of the system can be guaranteed. Then, a constraint variable method is introduced to ensure that the tracking error satisfies the prescribed performance requirements. Meanwhile, to avoid the design difficulties caused by the input quantization, a non-linear decomposition method is adopted. Finally, the feasibility of the proposed control scheme is verified by a numerical simulation example.

Based on neural networks and prescribed performance control method, an adaptive neural control strategy for switched pure-feedback non-linear systems is proposed.

The complex deduction and non-differentiable problems of traditional prescribed performance control methods can be solved by using the proposed error transformation approach. Besides, to obtain more general results, the restrictive differentiability assumption on non-affine functions is removed.