No abstract is provided for this article.
It is valuable for the real world to find the opinion leaders. Because different data sources usually have different characteristics, there does not exist a standard algorithm to find and detect the opinion leaders in different data sources. Every data source has its own structural characteristics, and also has its own detection algorithm to find the opinion leaders. Experimental results show the opinion leaders and theirs characteristics can be found among the comments from the Weibo social network of China, which is like Facebook or Twitter in USA.
ABSTRACT In this article, the identifier–critic–actor neural adaptive optimal control issue is addressed for a class of fully nonaffine pure‐feedback nonlinear cyber‐physical systems with input quantization and time‐reference‐dependent output constraints. By constructing a time‐varying asymmetric barrier Lyapunov function and integrating it with the dynamic surface control method and a reinforcement learning algorithm, a controller is designed based on a neural network approximation of the identifier–critic–actuator structure. In this framework, the identifier estimates unknown dynamics, the critic evaluates system performance, and the actor executes the control action. The control scheme involves designing the actual control inputs for all virtual and dynamic surface controls as the optimal solutions of their corresponding subsystems. The updated law is derived by taking the negative gradient of a simple positive function, which is constructed from the partial derivatives of the Hamilton–Jacobi–Bellman equation. In parallel, the proposed quantizer combines the benefits of both hysteretic and uniform quantization. A key aspect of this article is the simultaneous consideration of constraint boundaries related to both the reference signal and time, which adds complexity to the design of the control algorithm. Stability analysis confirms that all signals remain bounded and adhere to the time‐ and reference‐dependent constraints imposed on output.
In this paper, an improved recurrent neural network (RNN) scheme is proposed to perform the trajectory control of redundant robot manipulators using remote center of motion (RCM) constraints. Firstly, learning by demonstration is implemented to model the surgical operation skills in the Cartesian space. After that, considering the kinematic constraints associated with the optimization control of redundant manipulators, we propose a novel RNN-based approach to facilitate accurate task tracking based on the general quadratic performance index, which includes managing the constraints on RCM joint angle, and joint velocity, simultaneously. The results of the conducted theoretical analysis confirm that the RCM constraint has been established successfully, and accordingly. The corresponding end-effector tracking errors asymptotically converge to zero. Finally, demonstration experiments are conducted in a laboratory setup environment using KUKA LWR4+ to validate the effectiveness of the proposed control strategy.
The use of magnetorheological (MR) dampers for mitigating vibrations caused by seismic motions in civil engineering structures has attracted much interest in the scientific community because of the advantages of this class of device. It is known that MR dampers can generate high damping forces with low energy requirements and low cost of production. However, the complex dynamics that characterize MR dampers make difficult the control design for achieving the vibration reduction goals in an efficient manner. In this article, a semiactive controller based on the backstepping technique is proposed. The controller was applied to a three-story building with an MR damper at its first floor subjected to seismic motions. The performance of the controller was evaluated experimentally by means of real time hybrid testing.
Vehicle crashes are considered to be events that are extremely complex to be analyzed from the mathematical point of view. In order to establish a mathematical model of a vehicle crash, one needs to consider various areas of research. For this reason, to simplify the analysis and improve the modeling process, in this paper, a novel adaptive neurofuzzy inference system (ANFIS-based) approach to reconstruct kinematics of colliding vehicles is presented. A typical five-layered ANFIS structure is trained to reproduce kinematics (acceleration, velocity, and displacement) of a vehicle involved in an oblique barrier collision. Subsequently, the same ANFIS structure is applied to simulate different types of collisions than the one which was used in the training stage. Finally, the simulation outcomes are compared with the results obtained by applying different modeling techniques. The reliability of the proposed method is evaluated thanks to this comparative analysis.
Summary This paper studies the fault estimation problem for a class of nonlinear semi‐Markovian jump systems with partly unknown transition rates and output quantization. Firstly, a mode‐dependent intermediate variable is designed, which constructs an intermediate estimator to estimate fault signals of system. Secondly, the partly unknown transition rates and output quantization are considered and the sufficient conditions of stability for the error system are given in three cases by linear matrix inequalities. Moreover, the proposed nonlinear function satisfies Lipschitz condition, which can be employed in a multitude of practical systems. Finally, simulation results are provided to illustrate the effectiveness of the proposed theoretical results.
The synthesis of optimal controllers for vibrational protection of large‐scale structures with multiple actuation devices and partial state information is a challenging problem. In this study, the authors present a design strategy that allows computing this kind of controllers by using standard linear matrix inequality optimisation tools. To illustrate the main elements of the new approach, a five‐story structure equipped with two interstory actuation devices and subjected to a seismic disturbance is considered. For this control setup, three different controllers are designed: an ideal state‐feedback H ∞ controller with full access to the complete state information and two static output‐feedback H ∞ controllers with restricted neighbouring state information. To assess the performance of the proposed controllers, the corresponding frequency responses are investigated and a proper set of numerical simulations are conducted, using the full scale North‐South El Centro 1940 seismic record as ground acceleration input. The obtained results indicate that, despite the severe information constraints, the proposed static output‐feedback controllers attain a level of seismic protection that is very similar to that achieved by the ideal state‐feedback controller with complete state information.
No abstract is provided for this article.
The ocean world is full of Unknown Marine Objects (UMOs), making it difficult to deal with unknown ocean targets using the traditional instance segmentation model. This is because the traditional instance segmentation networks are trained on a closed dataset, assuming that all detected objects are Known Marine Objects (KMOs). Consequently, traditional closed-set networks often misclassify UMOs as KMOs. To address this problem, this paper proposes a new open-set instance segmentation model for object instance segmentation in marine environments with UMOs. Specifically, we integrate two learning modules in the model, namely a prototype module and an unknown learning module. Through the learnable prototype, the prototype module improves the class’s compactness and boundary detection capabilities while also increasing the classification accuracy. Through the uncertainty of low probability samples, the unknown learning module forecasts the unknown probability. Experimental results illustrate that the proposed method has competitive known class recognition accuracy compared to existing instance segmentation models, and can accurately distinguish unknown targets.
This paper is concerned with the problem of delay-dependent H ∞ control for 2-D (two-dimensional) switched discrete state delay systems described by the second FM (Fornasini and Marchesini) state-space model. Firstly, some sufficient conditions for the exponential stability and weighted H ∞ disturbance attenuation performance of the underlying system are derived via the average dwell time approach. Then, based on the obtained results, a state feedback controller design is proposed to guarantee that the resulting closed-loop system is exponentially stable and achieves a prescribed disturbance attenuation level γ . Finally, a numerical example is provided to verify the effectiveness of the proposed method.
This paper deals with H ∞ static output-feedback control design with constrained information for offshore wind turbines. Constrained information indicates that a special zero–nonzero structure is imposed on the static output-feedback gain matrix. A practical use of such an approach is to design a decentralized controller for a wind turbine. This will also benefit the controller in such a way that it is more tolerant to sensor failure. Furthermore, the model under consideration is obtained by using the wind turbine simulation software FAST. Sufficient conditions to design an H ∞ controller are given in terms of Linear Matrix Inequalities ( LMI s ). Simulation results are given to illustrate the effectiveness of the proposed methodology for different cases of the control gain structures.
In this paper, we present a novel Iterative Linear Matrix Inequality (ILMI) strategy for controller design that makes it possible to compute suboptimal H ∞ static output-feedback (SOF) controllers with high-performance characteristics.The obtained SOF controllers can be effective in reducing the vibrational response of multi-degree-of-freedom structures subjected to broad-band excitations.To demonstrate the effectiveness of the proposed methodology, a SOF controller is designed for the seismic protection of a multi-actuated five-story shear-frame structure with positive results.
