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This paper presents a structure of robust adaptive control for biped robots, which includes balancing and posture control for regulating the centre-of-mass (COM) position and trunk orientation of bipedal robots in a compliant way. First, the biped robot is decoupled into the dynamics of COM and the trunks. Then, the adaptive robust controls are constructed in the presence of parametric and functional dynamics uncertainties. The control computes a desired ground reaction force required to stabilise the posture with unknown dynamics of COM and then transforms these forces into full-body joint torques even if the external disturbances exist. Based on Lyapunov synthesis, the proposed adaptive controls guarantee that the tracking errors of system converge to zero. The proposed controls are robust not only to system uncertainties such as mass variation but also to external disturbances. The verification of the proposed control is conducted using the extensive simulations.

This study presents an adaptive robust fault-tolerant control (FTC) method for spacecraft proximity operations, in the presence of external disturbances, actuator faults, and input saturation. Firstly, a coupled 6-degrees-of-freedom dynamics model is constructed to show the relative motion of the pursuer spacecraft to the target spacecraft. To deal with actuator faults and external disturbances, a basic robust FTC method is designed. Subsequently, an adaptive robust FTC approach is developed to address the negative effect from the input saturation. In particular, by incorporating a novel dead-zone model to represent the saturation nonlinearity, an adaptive technology is applied to compensate for the nondifferentiable integral term in the saturation model. According to Lyapunov stability theory, all the signals in the whole system are proved to be ultimately bounded, and the relative motion tracking errors can converge to arbitrarily small neighborhood around the origin by choosing the suitable parameters. Last but not least, comparative simulations are carried out to validate the superiority of the proposed control strategy.

Despite that multistable isolators have been demonstrated to achieve the superior isolation performance compared with the linear ones, their performances are still inferior at ultra-low frequencies with the multi-solution phenomenon. Furthermore, their performances are dependent on the variation of initial conditions. Therefore, this paper proposes a fast fixed-time sliding mode controller to solve the ineffectiveness of the bistable dual-stage vibration isolator with external disturbances facing the above issues. Firstly, the bistable dual-stage isolator is modelled in the analytical form, and simulated compared with its linear counterpart to show its problems at some conditions. Secondly, the fast fixed-time sliding mode controller is presented with the modeling of sliding manifold and control inputs. Thirdly, the numerical simulation of the bistable dual-stage isolator with the proposed control is conducted, the results of which are compared with the finite-time sliding mode control. Finally, the outstanding feature of the proposed controller is concluded that the short convergence time becomes bounded with increasing initial conditions at both low and high excitation amplitudes. This work provides a valuable method to improve the isolation performance and robustness of the multistable vibration isolators.

This study focuses on a novel event-triggered boundary control design of an uncertain flexible manipulator subject to input constraints and external disturbances. To this end, a novel boundary control law is developed based on the Lyapunov stability theory to suppress elastic deflection and regulate the manipulator to track the desired angular position. Moreover, to approximate the unknown functions and compensate for the effect of input constraints, the neural network technique is adopted with an arbitrarily adjustable approximating error. Simultaneously, the event-triggering mechanism is incorporated with a controller design to alleviate the communication load and the execution rate of the actuator. Lastly, numerical simulations are performed to demonstrate the effectiveness of the derived scheme.

Purpose A multivariable model reference adaptive control method is proposed to solve a distributed leader–follower formation control problem of unmanned aerial vehicles (UAVs) with uncertain parameters and unknown external disturbances for both leader and followers. Design/methodology/approach A case of uncertain stochastic external disturbances for UAVs is considered, and based on the distributed communication network of UAVs, a state-feedback adaptive controller is proposed to maintain the formation of UAVs consistently. Then, the stability and asymptotic tracking performance of the UAV formation control system are analyzed by the Lyapunov function. Findings The simulation results demonstrate that this formation control scheme can effectively solve the stochastic external disturbance problem of UAVs and ensure the stability of their formation. Originality/value The proposed multivariable model reference adaptive control method reduces the error of formation control system and improves the stability and control performance of UAV compared with fixed control.

This article presents the design of a control algorithm based on Artificial Neural Networks (ANNs) applied to a lower-limb exoskeleton, which is aimed to carry out walking trajectories during lower-limb rehabilitation. The interaction between the patient and the exoskeleton leads to model uncertainties and external disturbances that are always present. For this reason, the proposed control considers that the non-linear part of the model is unknown and is perturbed by external disturbances, which are estimated by an active disturbance rejection control via Artificial Neural Networks. To validate the proposed approach, a numerical simulation and an experimental implementation of the ANN-Controller are developed.

This paper investigates the adaptive fault-tolerant formation control scheme for heterogeneous multi-agent systems consisting of unmanned aerial vehicles (UAVs) and unmanned surface vehicles (USVs) with actuator faults, parameter uncertainties and external disturbances under directed communication topology. Firstly, the dynamic models of UAVs and USVs are introduced, and a unified heterogeneous multi-agent system model with actuator faults is established. Then, a distributed fault-tolerant formation controller is proposed for the unified model of UAVs and USVs in the XY plane by using adaptive updating laws and radial basis function neural network. After that, a decentralized formation-tracking controller is designed for the altitude control system of UAVs. Based on the Lyapunov stability theory, it can be proved that the formation errors and tracking errors are uniformly ultimately bounded which means that the expected time-varying formation is achieved. Finally, a simulation study is given to demonstrate the effectiveness of the proposed scheme.

This paper addresses the tracking problem of a perturbed wheeled mobile robot by considering both the kinematic and dynamic models. A modified model is built based on the heading position. Through extracting fundamental elements from the combined disturbance, it becomes possible to estimate and remove the impact of the disturbance using a specific adaptive compensation term. Concurrent learning is integrated into the adaptive law to ease the requirement for persistent excitation. A practical prescribed time-tracking controller is designed based on a time-varying scaling function. Both the settling time and tracking precision are user-defined. What’s more, a sigmoid function-based tracking differentiator is utilized to avoid the complex differentiation of the virtual controller. A Lyapunov-based approach is employed to guarantee globally uniformly ultimately bounded stable tracking. The effectiveness of the proposed control strategy is validated through numerical simulation.

This study proposes a robust cascaded hybrid control architecture combining the Linear Quadratic Regulator (LQR) with Super-Twisting Sliding Mode Control (STSMC) to address the attitude stabilization problem of underactuated quadrotor UAVs. In this framework, the inner LQR loop ensures optimal stabilization, while the outer STSMC loops provide robust reference modification to compensate for uncertainties. To maximize the robustness of the proposed hybrid architecture, the parameters of the STSMC component were globally optimized using the Big Bang-Big Crunch (BB-BC) algorithm. The effectiveness of the proposed strategy is rigorously evaluated on the Quanser 3-DOF Hover system model against two distinct benchmarks: the classical LQR and the optimization-based Model Predictive Control (MPC). Comprehensive simulation scenarios were conducted, including stochastic wind disturbances, time varying thrust coefficients, sudden payload release, and critical motor failure. Additionally, a virtual 3D spatial analysis based on kinematic reconstruction was performed to validate the positional stability of the rotor tips. The comparative results demonstrate that while the MPC controller exhibits a faster transient response compared to the classical LQR, it suffers from significant performance degradation under model mismatches. In contrast, the proposed LQR-STSMC consistently outperforms both benchmarks, achieving error metrics (ISE, IAE, and ITAE) orders of magnitude lower than the competitors. The findings confirm that the hybrid scheme offers superior fault tolerance, faster stabilization, and precise tracking capabilities, making it a highly reliable alternative for safety critical flight operations in uncertain environments.

This article explores the leader-following consensus tracking (LFCK) control issues of multi-agent systems (MASs) in the presence of external disturbances and general directed fixed communication topology. Its purpose is to enable all follower agents to achieve consensus tracking for the leader agent. Firstly, this article introduces an extended state observer for estimating each follower agent’s unknown state and external disturbance. Subsequently, on the basis of the above-extended state observer and a dynamic event-triggered strategy, a distributed consensus tracking control protocol with disturbances restraint is developed, which can reduce the MAS’s update frequency on the premise of ensuring the control protocol’s effectiveness. Furthermore, the MAS’s stability and the absence of Zeno behavior are analyzed and proved by the established Lyapunov functional and linear matrix inequality theory. Finally, the validity and feasibility of the proposed approach are validated through a group of comparative numerical simulation experiments.