International Journal of Adaptive Control and Signal Processing最新文献

Occupational active cable-driven shoulder exoskeletons have many advantages compared to the traditional ones, but strong nonlinearity and complexity in modeling prevent the accurate torque transmission through Bowden cable, which is not conducive to its wide application. Besides, there is an urgent need to develop a more intelligent strategy for human-robot interaction. In this paper, to overcome the dependency on the Bowden cable model and achieve the assist-as-needed (AAN) function during the application of the shoulder exoskeleton, a force-velocity evaluation-based model-free sliding mode control (FVE-MFSMC) is proposed. The controller contains a force-velocity evaluation-based fuzzy admittance control (FVEFAC) strategy as the outer loop controller to determine the needed joint angle of the exoskeleton and a model-free super-twisting global terminal sliding mode control (MF-STGTC) as the inner loop controller to realize accurate motion control. In the outer FVEFAC, an admittance-based reference angle is generated to achieve the needed assistance level. Both the interaction force and the angular velocity of the arm are considered to be the evaluation elements of the subject's intention, and then fuzzy logic is applied to adjust the stiffness parameter based on the evaluated subject's intention. In the inner MF-STGTC, the ultra-local model is used to approximate the original complex system, which avoids the accurate modeling of the Bowden cable. Time-delay estimation is adopted to compensate for the lumped uncertainties, and a global fast terminal sliding mode control algorithm with super-twisting design is used to stabilize the system while minimizing the chattering problem in traditional terminal sliding mode control. Co-simulation experiments through MATLAB/Simulink and SolidWorks are carried out to demonstrate the performance of the proposed MF-STGTC and FVE-MFSMC, where the motion control can achieve the reduction percentage in root mean square error (RMSE) of tracking errors from 34% to 89% under fixed and time-varying Bowden cable disturbance.

In this article, the event-triggered adaptive fuzzy fault-tolerant consensus problem for switched nonlinear multiagent systems (SNMASs) with communication faults and state-dependent switchings is considered. To approximate the unknown nonlinear functions in the systems, an adaptive fuzzy control strategy is developed. To reduce the wastage of communication resources, the event-triggered control (ETC) method is used, and a novel ETC strategy independent of the subsystems' mode is designed. Moreover, state-dependent switching laws are designed using the convex combination technology and single Lyapunov function method to ensure the stability of all agents. Finally, to verify the effectiveness of the developed results, the proposed method is applied to a SNMAS with all unstabilizable subsystems.

An adaptive predefined-time optimal control method based on fuzzy logic system (FLS) is presented in this paper for strict-feedback nonlinear systems. The settling time of predefined-time control can be set in advance, which is independent of the design parameters and initial conditions. The optimal control relies on solving the Hamilton-Jacobi-Bellman (HJB) equation, which is difficult to calculate directly due to its inherent nonlinearity. To overcome this difficulty, the reinforcement learning (RL) strategy of actor-critic structure is used, where the actor and critic are used to achieve control behavior and evaluate system performance, respectively. In addition, the RL algorithm is simplified, and the two conditions of persistence of excitation and known dynamics required for the most optimal controls are eliminated. The main objective of this article is to ensure that the tracking error converges to a small neighborhood near the origin within a predefined time, while minimizing energy consumption. Finally, the efficiency of the suggested control strategy is demonstrated by using a practical simulation example.

This work aims to address the optimized formation fault-tolerant control issue by utilizing reinforcement learning (RL) for the single integral dynamic multi-agent system (MAS) having actuator faults. Because actuator faults have a direct effect on system performance and stability, it is essential to take the fault-tolerant mechanism as a design principle of nonlinear system control. Especially in the optimal control of MAS, actuator faults frequently occur due to real-time information exchange and high control performance requirements. To address the problem, the distributed RL and adaptive estimation are combined, where the RL algorithm is used to generate the optimized formation control protocol, and the adaptive learning is used to estimate the time-varying efficiency factor and bias signal in the faulty actuator model. Finally, it is demonstrated through theory and simulation that the proposed optimized control has the fault-tolerant capability and ensures system stability.

This article investigates the $��{�\ell �}_{�\infty �}�$ zonotopic state estimation for a class of discrete-time switched neural networks (SNNs) with mode-dependent average dwell time (MDADT)switching. First, by building appropriate mode-dependent Lyapunov functions, some sufficient conditions are established to guarantee the stability and $��{�\ell �}_{�\infty �}�$ performance for the considered system. Utilizing the formulated criteria, we propose a codesign scheme for switching signals and the mode-dependent nonlinear observer. In addition, a time-varying state zonotope is constructed, and with this as the basis, estimated bounds are calculated accordingly. Eventually, an illustrative example is utilized to demonstrate the efficacy and superiority of the results derived in this article.

The fault estimation and dynamic output feedback control problems of switched nonlinear systems, influenced by actuator and sensor faults, disturbances and stochastic additive noise, are explored in this paper. In the process of fault estimation, the stochastic noise is decoupled by the observer. The order of the observer can be adjusted within a certain range to harmonize the trade-off between the cost involved in estimation and its accuracy. Based on the estimated information, the nonlinear dynamic output feedback controller is constructed, and it can compensate for the impact of faults on the system. The LMI condition is obtained so that the closed-loop system is asymptotically bounded in mean square. Regional pole constraints are proposed to optimize the performance of the system. Finally, two simulation examples are given.

In this paper, a predefined-time control scheme, which intends to stabilize the concerned nonlinear time-delay systems possessing input unmodeled dynamics, is developed. During the controller design procedure, a predefined-time controller exported by following the backstepping technique is applied to the stabilizer of such concerned system, and the Lyapunov-Krasovskii functionals (LKFs), M-filter are respectively introduced to deal with the time delays and input unmodeled dynamics. Meanwhile, the fractional power piecewise function is constructed in the stabilizer to avoid the singularity problem caused by differentiating the virtual control laws at the origin. In addition, the neural networks are introduced to deal with the apparent nonlinearities by basing on their approximation ability. In stability analysis, under the proper selection of Lyapunov function candidates(LFC), we can verify that all signals in the formulating closed-loop system can possess the property of semi-global uniformly ultimately bounded (SGUUB) within a given predefined-time, and simulations, which cover theoretical and practical simulations, can reflect the availability of the developed scheme.

This article researches a predefined-time adaptive fuzzy tracking control problem for a class of nonlinear systems with unknown hysteresis. Unlike the previous investigations on predefined-time control, unknown input hysteresis is considered within this article. The existence of unknown input hysteresis leads to a control gain whose direction is unknown. The Nussbaum function is introduced to get over this difficulty. Lemma 8 is applied to ensure that an integral term with a Nussbaum function is bounded. Thus, the predefined-time stability criterion is ensured. Furthermore, an adaptive fuzzy control scheme is put forward. The proposed scheme guarantees that the tracking error is able to converge to the vicinity of the origin in an expected time. The practicability of the designed scheme is validated by an electromechanical system.

The research object of coupling identification is for multivariate systems. It is required to study and explore recursive and iterative identification methods for multivariable systems when there exists information coupling and/or parameter coupling between their subsystems. For a multivariable system, namely a multiple-input multiple-output system, after parameterization, its identification model contains the same information vector in all its subsystems. For a multi-input ARX system where there exists the information vector coupling, this paper derives the coupled identification model and investigates recursive parameter identification methods for such partially-coupled information vector systems, and presents a hierarchical recursive gradient identification algorithm and a hierarchical multi-innovation recursive gradient identification algorithm. Finally, the simulation example is provided to show the effectiveness of the proposed algorithms.

In this paper, we address the problem of adaptive consensus tracking control for a class of incommensurate fractional-order switched nonlinear multi-agent systems with external disturbance. All the followers in this study are described as arbitrarily switched heterogeneous fractional-order systems (FOSs), wherein the difficulty of incommensurate fractional order is solved by employing the continuity of fractional differentiation, and the derivative order of the adaptation laws is not determined by the order of the systems. A distributed adaptive control scheme is proposed within the framework of the backstepping control method, common Lyapunov function, and radial basis function neural networks (RBFNNs). By adjusting the parameters appropriately, all the signals of the multi-agent systems (MASs) are bounded, and the consensus tracking error eventually converges to a small neighborhood of the origin. Finally, the effectiveness of the proposed control strategy is verified through a numerical simulation example.