Asian Journal of Control最新文献

In this paper, we study the output consensus problems for heterogeneous multi-agent systems (MASs) with a high-dimensional leader and directed topology, where the followers are subject to unknown uncertainties. First, an observer is designed to estimate leader's unmeasurable states. Then, by using neural network (NN) approximation theory, a neuro-adaptive controller consists of a compensation part and a cooperative term is proposed. Unlike most existing neuro-adaptive controllers that utilize some discontinuous functions to eliminate the effects of NN approximation errors, a new class of continuous function is used and thereby the chattering effect is avoided. By using tools from graph theory and stability analysis of dynamical systems, it is proven that asymptotical output consensus can be achieved if the underlying network topology contains a directed spanning tree rooted at the leader and the control parameters are suitably chosen. Finally, a numerical simulation is presented to verify the theoretical result.

The current manuscript investigates the dynamics of a fractal–fractional malaria disease model caused by the Plasmodium parasite, in the framework of Caputo operator. The model under consideration has six compartments, namely, four categories of humans and two of vector populations. By utilizing the fixed point theory, we establish the existence results and uniqueness of solution for the aforementioned model. The Ulam–Hyers (UH) approach is carried out to evaluate the stability of the considered system. For the approximate solution of the model, the fractional Adams–Bashforth iterative techniques have been employed to obtain the numerical solution for the specified system. To simulate the model behavior, we use various values for the fractional order $��\beta �$ and for the fractal dimension $��\theta �$, based on real parameter values. The application of fractal–fractional calculus is shown to be beneficial in understanding real-world problems and in managing the global situation of the pandemic. Numerical simulations illustrate the significance of arbitrary order derivatives, suggesting that fractal–fractional order offer more insights into the dynamics of the proposed malaria model. Multiple graphical representations are provided to illustrate the model dynamics across different fractional orders. Finally, we apply a deep neural network (DNN) to analyze the considered system data and demonstrate high accuracy during training, testing, and validation as well as absolute errors.

One of the main problems when controlling a differential drive mobile robot is the nonholonomic restriction of its motion. The full-state stabilization problem resides, according to the fundamental work of R. Brockett, in the fact that the designed controller has to be either discontinuous or time-varying. In this work, we propose a novel robust position feedback controller for a class of perturbed nonholonomic robots. The controller has a rather simple to implement “proportional plus damping injection” structure that incorporates an adaptive velocity observer to exactly estimate constant external disturbances. The proposed observer is based on the well-known Immersion & Invariance technique. In order to overcome the nonholonomic restriction, the control law is time-varying. To the best of the authors' knowledge, this is the first solution to the robust output-feedback stabilization problem with a continuous time-varying controller. The formal convergence proof is established via Barbalat's lemma, and simulation results are reported to validate the performance of our method.

State-triggered impulsive logical control networks (STILCNs) with input constraints can not only describe the mutation behavior of gene regulatory networks, but also characterize the impact of external environments on them. Under the framework of a hybrid-index model, this paper investigates controllability of STILCNs with input constraints. By using the techniques of input transformation, quotient mapping, and $��\xi �$-domain construction, the necessary and sufficient conditions for time-domain and hybrid-domain controllability are both derived. Finally, an example is given to demonstrate the correctness and effectiveness of the obtained results.

In this paper, the improved decentralized controllers for a MIMO system are designed using the sequential and simultaneous relay autotuning method. Conventionally, the decentralized controllers are designed without including the effect of higher-order harmonics. Few papers in the literature, discuss the design of controllers with the consideration of higher-order harmonics using simultaneous relay autotuning for different levels of interactive processes. The selection of the relay autotuning method based on the level of interaction has not been revealed in the literature so far. In this paper, the controllers are designed for the TITO system using the improved method of sequential and simultaneous relay autotuning by considering the effect of higher-order harmonics, and also the interaction is analyzed. The relay feedback test using sequential and simultaneous loop tuning methods is performed. The effect of higher-order harmonics from the initial dynamics of the sustained relay oscillation is analyzed for the Wood and Berry Distillation Column. The decentralized controllers are designed with the relay response data and the improved amplitude values are estimated by incorporating the higher-order harmonics. To verify the usefulness of the proposed method, the improved method of sequential and simultaneous relay autotuning method is demonstrated in a real-time laboratory experimental setup. From the qualitative and quantitative analysis, it is inferred that the improved method of simultaneous autotuning provides good setpoint tracking and disturbance rejection. It also gives better robustness and effect of measurement noise than the sequential autotuning method.

This paper proposes a sequential visual servo control scheme for quadcopters, where the main goal is to drive a quadcopter to a desired pose while crossing windows, doors, or corridors using only visual feedback. This control scheme is composed of two stages that act in sequence: the approaching and pose regulation phases. A switching control law in the approaching stage is in charge of bringing the quadcopter in front of the target while keeping the target in the camera's field of view (visibility task). The target could be a window, a door, or the end of a corridor. The pose regulation control drives the quadcopter through the target to reach the desired pose. The stability analysis of the closed-loop switching control law is provided considering the approaching and visibility tasks. This allowed us to verify the asymptotic convergence of the errors to the origin. The effectiveness of the proposed control scheme was validated with experiments using a commercial quadcopter.

This paper is concerned with the problem of generalized finite-time/fixed-time stabilization control for a class of fuzzy neural networks system with time-varying delay and discontinuous activation. Firstly, considering the time delay and fuzzy logics, a discontinuous fuzzy neural networks system with time-varying delay is formulated. Secondly, by introducing the finite-time/fixed-time stability lemmas, two switching state feedback control laws are designed, and a generalized framework of finite-time/fixed-time stabilization control for the discontinuous fuzzy neural networks system is presented. By relaxing the conditions of the Lyapunov function, the finite-time/fixed-time stabilization of the fuzzy neural networks system is proved by using inequality techniques. A more accurate settling time can be estimated by some special functions. Finally, the effectiveness of the developed method is verified by two typical numerical examples.

This paper investigates the regional fractional enlarged controllability of parabolic linear systems governed by Riemann–Liouville fractional time derivatives. The goal is to design a control that, while ensuring the fractional derivative of the system's state satisfies predetermined criteria, steers the system's fractional output to a desired state within a designated subregion of the domain. The optimal control is defined using two separate methods: subdifferential theory and the Lagrangian technique. We establish the prerequisites for achieving regional controllability by employing these methods. Finally, a Uzawa-type algorithm is developed and used in numerical simulations to validate the theoretical results, demonstrating the practicality of the proposed control in real-world scenarios.

This paper studies the controllability of multi-agent systems with directed and matrix-valued weighted networks. Almost equitable partition theory is increasingly being chosen by researchers as a framework for controllability of multi-agent systems from graph-theoretic perspective. Based on the matrix-valued almost equitable partition, we propose a graph-theoretic characterization of the upper bounds of dimension of controllable subspace. Then, we provide graph-theoretic necessary conditions for controllability and methods of constructing uncontrollable topologies. Several examples are given to illustrate these results.

This research focuses on the stability of discrete-time Markovian jump neural networks with time-varying delays. To this aim, a nonorthogonal free-matrix-based summation inequality (NFMSI) is proposed by constructing nonorthogonal polynomials with auxiliary scalars as well as auxiliary vectors. The NFMSI can avoid time-varying delays in the denominator and prevent the loss of some coupling information compared to exiting summation inequalities. Then, an ameliorative stability criterion is derived by utilizing the NFMSI and other estimation method. Finally, the advantage of the obtained stability criterion is demonstrated by a few numerical examples.