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In recent years, exoskeleton robots have attracted great interest from researchers in the area of robotics due to their ability to assist human functionality improvement. A wearable lower limb exoskeleton is aimed at supporting the limb functionality rehabilitation process and to assist physical therapists. Development of a stable and robust control system for multi-joint rehabilitation robots is a challenging task due to their non-linear dynamic systems. This paper presents the development of a Swarm-Initialized Adaptive (SIA) based controller, which is a combination of a swarm-based intelligence, named Swarm Beetle Antenna Searching (SBAS), and an adaptive Lyapunov-based controller. The SBAS initializes the parameters of SIA to efficiently improve the performance of the control system and then these controller parameters are updated by an adaptive controller. The control system is validated in a lower limb exoskeleton prototype with four degrees of freedom, using a healthy human subject for sit-to-stand and walking motions. The experimental results show the applicability of the proposed method and demonstrate that our approach obtained efficient control performance in terms of steady-state error and robustness and can be used for a lower limb exoskeleton to improve human mobility.

This paper addresses the critical challenge of interpretability in machine learning methods for machine fault diagnosis by introducing a novel ad hoc interpretable neural network structure called Sparse Temporal Logic Network (STLN). STLN conceptualizes network neurons as logical propositions and constructs formal connections between them using specified logical operators, which can be articulated and understood as a formal language called Weighted Signal Temporal Logic. The network includes a basic word network using wavelet kernels to extract intelligible features, a transformer encoder with sparse and structured neural attention to locate informative signal segments relevant to decision-making, and a logic network to synthesize a coherent language for fault explanation. STLN retains the advantageous properties of traditional neural networks while facilitating formal interpretation through temporal logic descriptions. Empirical validation on experimental datasets shows that STLN not only performs robustly in fault diagnosis tasks, but also provides interpretable explanations of the decision-making process, thus enabling interpretable fault diagnosis.

Relying on composite nonlinear feedback, an output-feedback controller is robustly addressed in the singular models with uncertainties, disturbances and time-delays. For this purpose, an observer-based compensator is utilized to realize the purpose. In the presence of disturbance and uncertainty, it is demonstrated that the tracking error and the states of the overall system are ultimately bounded. Moreover, the asymptotic stability would be specifically established without the external disturbance and uncertain terms. Employing the linear matrix inequality, the control design is translated into an optimization problem. Hence, in solving such an optimization issue, the coefficients of the estimator and the control law are determined simultaneously. Some simulations are provided to show the advantages of the planned strategy compared to a similar one.

Existing cross-domain mechanical fault diagnosis methods primarily achieve feature alignment by directly optimizing interdomain and category distances. However, this approach can be computationally expensive in multi-target scenarios or fail due to conflicting objectives, leading to decreased diagnostic performance. To avoid these issues, this paper introduces a novel method called domain feature disentanglement. The key to the proposed method lies in computing domain features and embedding domain similarity into neural networks to assist in extracting cross-domain invariant features. Specifically, the neural network architecture designed based on information theory can disentangle key features from multiple entangled latent variables. It employs the concept of contrastive learning to extract domain-relevant information from each data point and uses the Wasserstein distance to determine the similarity relationships across all domains. By informing the neural network of domain similarity relationships, it learns how to extract cross-domain invariant features through adversarial learning Eight multi-target domain adaptation tasks were set up on two public datasets, and the proposed method achieved an average diagnostic accuracy of 96.82%, surpassing six other advanced domain adaptation methods, demonstrating its superiority.

This paper investigates the self-triggered control for stabilizing an n-dimensional linear time-invariant system under communication constraints, including finite bit rates and transmission delay. The concerned system is further perturbed by bounded process noise. To resolve these issues, a self-triggering strategy is proposed. Specifically the proposed self-triggering strategy selects the next sampling time from a set of pre-designed time instants based on the sampled system states. By fully exploiting the encoded information of receive time instants of feedback packets, we can achieve the desired input-to-state stability (ISS) at a lower bit rate than that of periodic sampling. Moreover, the proposed self-triggering strategy is free of the burdens of continuously monitoring the system state compared with event-triggered sampling strategies. The efficiency of the proposed self-triggering strategy is further confirmed by simulations.

This paper proposes an innovative approach to address the challenges of dynamic balance and external disturbances in ballbot systems, overcoming the limitations of conventional Proportional Integral Derivative (PID) controllers and their variants in handling highly nonlinear dynamics and external forces. Traditional PID controllers and their variants often have difficulty adapting to complex, real-time dynamic systems, leading to performance degradation under varying conditions. A nonlinear PID controller-based Takagi-Sugeno-Kang 3D Cerebellar Model Articulation Controller (TSK3DCMAC) is introduced to overcome these shortcomings. The proposed controller is developed utilizing a combination of nonlinear PID control, TSK3DCMAC, and the Balancing Composite Motion Optimization (BCMO) algorithm. The TSK3DCMAC is iteratively trained during the ballbot's motion to ensure the system balance in a very steady and seamless manner. Furthermore, the BCMO algorithm is utilized to obtain the optimal gains for precisely modeling the system. The stability of NPID-TSK3DCMAC law is analyzed using the Lyapunov technique. The simulation and experimental results highlight the effectiveness of the NPID-TSK3DCMAC controller. Without external force, it reduces the mean squared error (MSE) by 45.84 % and 99.87 % and the mean absolute error (MAE) by 25.68 % and 63.91 % compared to the PID and NPID controllers, respectively. With external force, it further surpasses the NPID controller by 64.94 % in MSE and 17.67 % in MAE, demonstrating its robustness and precision under varying conditions. Simulation and experiment results reveal that the proposed approach has robustness and effectively regulates the motion of the ballbot system despite external disturbances. This indicates a promising solution for applications requiring precise/agile motion control and stability under varying external conditions.

This article addresses the secure synchronization problem for complex dynamical networks (CDNs) with observer-based event-triggered communication strategy (ETCS) under multi-channel denial-of-service attacks (MCDSAs). Due to external environmental interference, the observers are designed to accurately estimate the state of the network systems. Meanwhile, the impact of cyber attacks on system security is considered. Differing from other studies, MCDSAs in this paper are considered, which present the interference of malicious attacks on the communication topology, observers and controllers. Based on the characteristics of cyber attacks, the model for CDNs with nonperiodic switching topology is established, and the observer-based intermittent control is proposed to achieve synchronization of CDNs. To decrease the network communication frequency and reduce the risk of cyber attacks, a new ETCS depending on estimate states is designed. Then, using Lyapunov stability theory, several synchronization conditions depending on the status parameters of MCDSAs are deduced, which may estimate the allowable ranges for the duration and frequency of MCDSAs. From these theoretical results, it can be observed that the destructive power of network attacks increases exponentially with the increase of attack duration. Finally, from simulation experiments, the availability of the designed controller and communication strategy is verified, which can ensure the synchronization for CDNs with MCDSAs.

This study explores the trajectory tracking control problem in unmanned surface vessels equipped with two rotatable thrusters in an adverse network environment beset by challenges, such as false data injection attacks (FDIAs) and input saturation. This study used the hyperbolic tangent function to provide a smooth transition for the control input, effectively avoiding the system oscillation and instability caused by sudden changes in the input signal. Next, the concept of stepwise reconstruction was used to handle the FDIAs faced by the kinematic and dynamic channels of the system. Under the backstepping framework, the exogenous disturbance bias caused by FDIAs in the kinematic channel was compensated using virtual adaptive technology. In the dynamic channel, the neural-based, high-speed disturbance compensation technology was used to correct the dynamic deviation caused by the composite uncertain dynamics composed of FDIAs and external disturbances. Finite-time technology was further introduced to propose an adaptive finite-time control scheme based on nonlinear decoration. The proposed control scheme was analyzed on the basis of Lyapunov stability theory. The results indicated that all signals in the closed-loop system are bounded. Finally, the effectiveness of the proposed control scheme was verified using simulations. The results revealed that the proposed control scheme enables unmanned surface vessels to track the reference trajectory, exhibiting satisfactory control performance under the constraints of FDIAs, input saturation, and uncertain internal and external dynamics.

As global interest grows in renewable energy sources, the impact of combined Electric Vehicle (EV) and PhotoVoltaic (PV) penetration on the power grid stability requires renewed attention, to incorporate new technologies to maintain the power quality under operational constraints. Energy-saving techniques such as Conservation Voltage Reduction (CVR) allow the power utilities to transmit voltage at a lower operation limit, increasing the generation margin to absorb the peak load demands. Increased reverse PV penetration results in grid overvoltage while EV charging absorbs the reactive power causing grid instability. Both overvoltage and loss of reactive power in the grid can be reduced by using CVR and reactive power injection techniques. A power electronic secondary var controller (SVC) can dynamically inject reactive power into selected grid buses. This work compares the voltage stability of an IEEE 33 bus system operating with and without CVR. The simulation studies analyzed the effects of EV penetration level, and PV hosting capacity with SVC compensation paired with and without conservation voltage reduction technique. The analysis results demonstrate that tandem usage of CVR and SVC maintains the grid voltage under operational limits, meets load and EV demand, and increases power efficiency and PV penetration.

Recent biomedical engineering developments have empowered prosthetic devices to evolve from purely mechanical devices to more sophisticated controlled devices, allowing amputees to perform advanced locomotion modes such as passing stairs and walking on sloped surfaces. However, the strongly coupled nonlinear system dynamics make it difficult for the lower-limb prosthesis (LLP) to adapt to complex tasks and isolate the vibrations and acceleration from the residual limb soft tissue. In this regard, realizing the potential of active LLPs to increase user mobility and efficiency requires reliable, stable, and intuitive control strategies to provide a comfortable gait quality. In this study, a fractional-order dynamic terminal sliding mode controller (FDTSMC) is proposed to effectively isolate the residual limb soft tissue from the vibrations and acceleration arising from the pylon and foot. The proposed sliding surfaces guarantee the fast finite-time system states' convergence, and the chattering is remarkably alleviated. Furthermore, since from the practical viewpoint, the actuators are non-ideal and are affected by dead-zone and hysteresis that degrade the LLP's performance, an observer is augmented with the control system to estimate the lumped uncertainties and compensate for the effects of model nonlinear dynamics and disturbances. The closed-loop system stability is ensured in terms of Lyapunov concept. Comparative performance investigations in ideal and non-ideal situations are carried out, and the proposed control scheme's favorable gait shock absorption performance over observer-based conventional SMC and dynamic SMC approaches is revealed.