ISA transactions最新文献

Adaptive global predefined-time control is examined for the n-link robotic system, which involves output constraints. The primary challenge in designing the controller is not only to guarantee that the output constraints are never violated, but also to achieve global convergence of the tracking error within a predefined time. First, a barrier function is introduced to transform the output constrained system into an unconstrained system. Then, the command filtering technique is incorporated into the adaptive multiple multidimensional Taylor network (MMTN) control process. Furthermore, a compensation system is constructed to alleviate for errors arising from filtering. Notably, the designed multi-switching-based adaptive MMTN controller realizes the global stability of robotic systems. Finally, a two-link robotic system simulation is presented to demonstrate the feasibility of the proposed control strategy.

Wheel-biped humanoid robots offer a promising solution that combines the bipedal locomotion and manipulation capabilities of humanoids with the mobility advantages of wheeled robots. However, achieving high mobility and adaptive wheel-foot transitions while maintaining essential bipedal functionality in a transformable wheel-biped configuration (TWBC) presents a significant challenge. To address this, this paper proposes a transformable wheel-humanoid framework (TWHF), which enhances traditional humanoid robots by incorporating a compact, decoupled wheeled subsystem. This design effectively balances high-speed wheeling, seamless mode transitions, and fundamental bipedal locomotion. A novel key phase decomposition (KPD) methodology is introduced to analyze and decouple transition motions, providing structured guidance for subsystem design, motion planning, and control. Transition reference motions are optimized using a particle swarm optimization-based motion optimization (PSOMO) approach, leveraging sagittal modeling to ensure dynamic stability and kinematic feasibility. Additionally, the proposed trunk-ankle collaborative control (TACC) strategy further enhances transition adaptability to terrain discrepancies. Extensive experiments conducted on the wheel-humanoid BHR8-2 validate the proposed TWHF, demonstrating stable hybrid locomotion across diverse terrains and achieving wheeling speeds exceeding 10 km/h.

Semi-supervised learning is increasingly applied in industrial fault diagnosis, presuming that the label spaces of labeled samples and unlabeled samples are identical. However, unknown faults in real-world scenarios present a challenge for traditional closed-set approaches. To this end, we propose a novel framework for open-set semi-supervised industrial fault diagnosis, named Evidential Robust Contrastive Learning (ERCL). The theory of evidence is introduced to explicitly assess sample uncertainty, guiding the evidential robust contrastive representation module to implement instance-level training strategies for each unlabeled sample. Additionally, an adaptive out-of-distribution detection module is developed to detect unknown faults by evaluating the difference in mutual information distributions between in-distribution (ID) and out-of-distribution (OOD) samples, thereby avoiding over-reliance on prior knowledge. The proposed framework is validated with the Tennessee Eastman process and polyester esterification process. As proved in the experiments, ERCL exhibits superior diagnosis accuracy in open scenarios with limited labeled data.

For unmanned surface vehicles (USVs) navigating in narrow water areas in the presence of unknown dynamics and ocean environmental disturbances, this paper develops a reinforcement learning (RL)-based optimal control scheme for the trajectory tracking of USVs under motion state constraints. A nonlinear map is introduced to transform constrained motion state errors into bounded transformed errors, and then the motion state-constrained trajectory tracking problem of USVs is equivalently transformed into a boundedness problem of the transformed errors. Furthermore, an actor-critic framework is developed by utilizing adaptive neural networks (NNs). Within the actor-critic framework, a novel weight update law is designed for the critic NN by combining the gradient descent approach and the concurrent learning technology, thereby relaxing the persistent excitation condition required for adaptive critic NN weight updates. Subsequently, a disturbance compensator is designed and combined with the actor-critic framework to learn the trajectory tracking optimal control law for USVs in the presence of unknown dynamics and disturbances. Finally, theoretical analyses prove that the developed control scheme guarantees the boundedness of all signals in the USV closed-loop trajectory tracking control system, and simulation results show that the developed control scheme can make USVs track the desired trajectory in narrow water areas while reducing the energy consumption by approximately 14.6 % compared with an existing controller.

In this paper, an adaptive stabilization control scheme is developed for uncertain nonlinear systems with both quantized input and output. Initially, a finite-time filter is introduced and applied to the discontinuous quantized output. It is shown that the output of the finite-time filter, named filtered quantized output, is continuous and adheres to the sector bound property. Subsequently, an observer is constructed to estimate unmeasurable states, followed by the design of a control scheme utilizing dynamic surface control technique, so as to avoid repeated derivatives of the filtered quantized output. Finally, it is demonstrated by theoretical analysis and simulation results that, even with coarse input and output quantizers, the stabilization error can be driven to a small residual set by adjusting certain design parameters.

Consider an offshore gangway with vessel-motion-induced disturbances, dynamic uncertainties and friction force uncertainties under velocity constrains of the gangway tip (GT). A prescribed-time extended state observer (PTESO) is innovatively constructed by means of an error mapping function with respect to a prescribed performance function (PPF) where the problem that the gains of the existing PTESOs approach to infinity at a prescribed time is solved. The constructed PTESO with prescribed-time convergent estimation errors can provide the estimates of the GT motion states and the gangway total disturbances, respectively. Further, a new barrier function with respect to the PPF, the gangway tip position stabilization (GTPS) error and the GT velocity is proposed to handle the velocity constrains of the GT. Based on the above, a prescribed-performance control law for the GTPS is developed such that the GTPS errors converge to a prescribed tolerance steady-state error band in a prescribed settling time. Therein, our proposed barrier function is used as the component of the gain of the developed control law, unlike in the existing literatures used as the barrier Lyapunov functions, such that the developed control law is easy to implement. Simulation results exhibit that the GTPS errors under our developed control law are decreased by 16 % and 19 % in two cases with different sea states and model parameters, respectively.

This article studies the implementation of practical fixed-time control in stochastic nonlinear systems, implementing event-triggered communication between the controller and the actuator. Firstly, to accomplish the problem of uniform tracking error performance constraints, the improved performance function is investigated, which is combined with the asymmetric barrier Lyapunov function to achieve fast convergence speed and steady state accuracy. Secondly, the practical fixed-time stability is applied in the stochastic nonlinear closed-loop system, which fuses fixed-time command filtering and improved filtering error compensation mechanisms to avoid computational explosion issue. Furthermore, in order to relieve the communication load on the controller and actuator, adjustable trigger thresholds are designed, based on which dynamic event triggering mechanisms are presented for stochastic nonlinear systems. Additionally, the uncertain system behavior is estimated using RBF neuro-networks and the designed controller avoids the singularity problem. Finally, the proposed controller verifies that the system error converges to zero in a fixed time under the Lyapunov stability theory, and that the system output is within the preset boundaries, realizing boundedness of all signals. The superiority of the control method is further demonstrated by three simulation studies including two practical examples.

Advancements in intelligent vehicle technology have spurred extensive research into the impact of driving style (DS) on intelligent transportation systems (ITS), aiming to enhance vehicle safety, comfort, and energy efficiency. Accurate DS identification is pivotal for accelerating ITS adoption, especially in regions where its implementation is still in its infancy. This paper investigates the role of DS recognition methods, particularly clustering and classification techniques, in influencing connected vehicle control and optimizing speed planning within ITS. While traditional speed planning approaches focus on general traffic models, this study emphasizes the critical role of DS in shaping personalized and adaptive speed planning. The paper highlights three primary DS recognition approaches: rule-based, model-based, and learning-based methods, and introduces a framework for integrating DS recognition with speed planning, addressing aspects such as data collection, preprocessing, and classification techniques. This focus provides a novel perspective on leveraging DS recognition to enhance ITS adaptability.

Fault diagnosis and prognosis play important roles in the community of discrete event systems (DESs) and have garnered significant interest from researchers. Despite their close relationship, these concepts are typically formalized and studied independently. This paper introduces a novel concept, known as feature recognition of DESs, which unifies fault diagnosis and prognosis into one framework based on ω-language. For any infinite faulty ω-string, feature string is defined as its some finite prefix by which the faulty behavior can be distinguished from all normal language, and fault diagnosis and prognosis can be decided by the type of feature strings (normal or faulty). Then the problem of feature recognizability is converted to verify the existence of feature strings with respect to every faulty ω-string. Compared with fault diagnosis and prognosis, the notion of feature recognition is more general because it relaxes the restriction of uniformity on reaction bound and helps to understand the essence of fault diagnosis and prognosis more intuitively. More importantly, online recognition algorithms can be designed straightforward according to the definition of feature recognition and online decision can be realized as soon as possible. A necessary and sufficient condition for verifying feature recognizability is concluded and an online recognizer that meets timeliness condition is also constructed to execute fault diagnosis and prognosis synchronously.

This paper studies the double event-triggered synchronization (ETS) of neutral-type semi-Markovian jump (SMJ) neural networks under synchronous attacks. Firstly, synchronous attacks are modeled by an independent semi-Markovian jump process. Secondly, the double dynamic event-triggered mechanisms (DDETMs) introduced offer the advantage of conserving communication resources and alleviating the computational burden. Thirdly, considering that both the network of sensor to controller and controller to actuator may cause time-varying delays (TVDs), and additive TVDs are designed to enhance the dynamics affected by these delays in the network. Subsequently, a semi-Markov dynamic event-triggered controller is designed to ensure the synchronization of neutral-type SMJ neural networks. Then, the conservatism of the synchronization criterion is reduced by using methods such as asymmetric Lyapunov-Krasovskii functions (LKFs) and novel reciprocally convex combination inequality (RCCI). Finally, two sets of values are given to prove the effectiveness of the synchronization criterion of the proposed method.