This study provides a bibliometric analysis of research related to the topic of robotic soccer and artificial intelligence (AI), mapping the intellectual and thematic landscape of a rapidly evolving interdisciplinary field. Drawing on 79 peer-reviewed articles from the Web of Science database spanning 1997 to 2024, we examine publication trends, citation impact, prolific contributors, institutional Output, and core research themes. Utilizing the bibliometrix R package and biblioshiny interface, our analysisreveals that research on robotic soccer has experienced episodic growth, with notable surges linked to global competitions such as RoboCup. Results indicate that topics such as multi-agent collaboration, learning algorithms, autonomous decision-making, and human-robot interaction dominate the field's motor themes. Influential institutions include Islamic Azad University and Sapienza University of Rome, while key authors such as Peter Stone and Hiroaki Kitano shape the domain’s scholarly foundation. Despitethe dominance of technical publications, there remains a research gap in addressing sociotechnical dimensions and integrative AI strategies. This study not only clarifies the structure of the field but also identifies emerging trajectories, offering a strategic foundation for future research on intelligent autonomous systems in sports contexts

In this paper, we have exploited the first-order sliding mode control method to track the ECG data of the human heart by three different nonlinear control laws. In order to lessen the intrinsic chattering of the classic sliding mode control system, smooth function approximations of the control input, by means of the hyperbolic tangent and the saturation function, were used. The fast Fourier transform was used to evaluate the average chattering frequency of the control inputs. The synthesized control schemes namely SMC-sign, SMC-tanh, and SMC-sat, were able to track the real-world ECG signal with an average root mean square error of 0. 0306 and a chattering frequency of 92. 7 Hz. The findings show that the sliding mode controllers can be implemented in electronic artificial pacemakers to provide the intended results successfully. Based on today's electronics, the involved frequency range (556. 4 Hz for the worst case) is quite acceptable and practical.

In this paper, modeling, analyzing and controlling nonlinear systems using Polytopic linear models is considered. First, the Output tracking problem is investigated for the state of the system as compared to the affine input, and then the problem is solved for the non-affine state. In the state of determining the parameters of each region to increase the problem solving speed we determine the weighted function in a specific manner that prevents interference between the regions and by solving an linear inequality matrix of controller design, in contrast to the past, it is not necessary to solve a bilinear matrix inequality and only by solving a linear one, the controller will be designed. To stability and design of the controller, a method is used to ensure both the stability of the approximate model (polytopic) and the stability of the main model (nonlinear). Finally, the results are taken and the methods proposed are used to design of an elastic missile system.

Singular systems have been studied extensively during the last two decades due to their many practical applications. Such systems possess numerous properties not shared by the well-known state variable systems. This paper considers the linear tracking problem for the continuous-time singular systems. The Hamilton-Jacobi theory is used in order to compute the optimal control and associated trajectory. Two methods are presented for solving these trajectories. The first method uses the concept of the Drazin inverse, and the second involves the derivation and solution of a Riccati equation. Similar to the linear regulator problem, necessary and sufficient conditions for existence and uniqueness of a solution are stated.

Sliding Mode Control (SMC) is a powerful approach to solve the tracking problem for dynamic systems with uncertainties. However, the traditional SMCs introduce actuator chattering phenomenon which performs a desirable behavior in many physical systems such as servo control and robotic systems, particularly, when the zero steady state error is required. Many methods have been proposed to eliminate chattering from SMCs which use a finite DC gain controller.Although these methods provide a free chattering control but they only deal with the steady state error and are not able to reject input disturbances. This paper presents a fuzzy combined control (FCC) using appropriate PID and SMCs which presents infinite DC gain. The proposed FCC is a free chattering control which guarantees a zero steady state error and rejects the disturbances. The stability of the closed loop system with the proposed FCC is also proved using Lyapunov stability theorem. The proposed FCC is applied to a two degree of freedom robot manipulator to illustrate effectiveness of the proposed scheme.

In this paper, the constrained Output tracking problem for linear systems with fast dynamics is addressed. It is assumed that the system is subject to input/state constraints and the reference signal is piecewise constant. Based upon the closed-loop control behavior, a simple controller is designed and it is shown that the proposed architecture guarantees the feasibility and asymptotic stability of the closed-loop system. Adapting and formulating the proposed controller as a model predictive control problem, an explicit optimal solution is obtained for the proposed controller. It is shown that the proposed approach can be effectively applied when the reference signal takes only some finite predefined values. However, non-predefined references are also handled and it is guaranteed to be feasible under some mild assumptions.

In this paper, to solve the Output tracking problem of a single-link flexible joint manipulator, Polytopic Linear Models (PLMs) of the dynamics are made to take advantage of this method. Although linear control methods are very useful due to their powerful theories and simplicity, they can only be used in a neighborhood of the equilibrium point. One way to solve this problem is a PLMs-based method that linearizes the dynamics around several operating points. Therefore, in this paper, after calculating the PLMs of the manipulator, a state feedback control is applied to the derived linear dynamics that are augmented with the dynamics of the Output tracking error. An extended method is used to decompose the scheduling space to construct PLMs, which is the segregation method improved with an extra aggregation. In order to avoid creating a large number of local models, an axis-oblique decomposition strategy is used instead of an axis-orthogonal decomposition. In addition, the scheduling functions of the PLMs are determined such that overlaps between the regions are avoided. By this selection, the Output tracking problem becomes as a Linear Matrix Inequality (LMI) problem instead of a bilinear matrix inequality problem, which is more difficult to solve and may not lead to an optimal global solution.

Portfolio optimization is one of the most important issues in financial sciences. Various strategies have been used to manage stock portfolios, which can be categorized into types: active and passive strategies. One of the most important passive portfolio management approaches is to form an index tracking portfolio. The purpose of index tracking portfolio is to its performance replicate market index as benchmark as closely as possible with a limited stocks in portfolio which will result in lower transaction costs for investor. In this research, a model for index tracking problem is proposed which aims to minimize undesirable deviations and maximize desirable deviations. Finally, a genetic algorithm is used to solve the leading model. To evaluate the performance of the model, data from four major industries of Tehran Stock Exchange has been used. The results show that the proposed model has a suitable performance in tracking the relevant index and achieving excess return over the benchmark.

In this paper, the tracking control of a robotic arm mounted on a wheeled mobile platform is considered. A nonlinear neural adaptive robust control algorithm is proposed for the Output feedback tracking control of a wheeled mobile manipulator without measuring system velocities to deal with the unmodeled system dynamics, parametric uncertainties and external disturbances. A Lyapunov-based stability analysis shows that tracking and observation errors are Uniformly Ultimately Bounded (UUB) and converge to a small ball containing the origin. A Radial Basis Function Neural Network (RBFNN) is employed to compensate for the uncertainties of mobile manipulator dynamics. Nonparametric uncertainties and NN approximation errors are also compensated by an adaptive robust controller. In addition, hyperbolic tangent function is employed in the design of the Output feedback controller to reduce the risk of actuators saturation and to produce smoother control signals. Finally, simulation results demonstrate the effectiveness of the proposed controller well.