Control Game Research Articles

Joint Beamforming Design and Power Control Game for a MIMO Radar System in the Presence of Multiple Jammers

Joint Beamforming Design and Power Control Game for a MIMO Radar System in the Presence of Multiple Jammers

DEVELOPMENT OF A SELF-DIRECTION GAME HANDBOOK TO IMPROVE INHIBITORY CONTROL FOR 10–12-YEAR-OLDS

Elementary school-age children need increased inhibitory control to focus and put aside things that are not priorities. Inhibitory control helps children to stay focused, control behavior, think before they act, and manage emotions. However, efforts specifically to increase the ability of inhibitory control have not been made much. This study aims to develop a guidebook to improve inhibitory control in children aged 10-12 years through the game "Self-Steering". This research is development research with the following steps: First, conducting a needs analysis through teacher interviews and classroom observations. Second, planning the manufacture of products as a solution to research problems. Third, making the product. Fourth, testing the product through expert validation and limited trials. Fifth, improving the product based on suggestions for improvement at the product trial stage. The result of this study is a game guidebook containing 3 inhibitory control games that were declared feasible by expert validators. Based on the field trial results, this game guidebook is an interesting game guide to improve children's inhibitory control. Some things that need to be improved will be discussed further.

The Development of Response and Interference Inhibition in Children: Evidence from Serious Game Training.

A serious game titled "Crossing the Jungle" was developed in this study to train children's inhibition skills using the Stroop task. The effects of inhibitory control on children were tested by a pre-test, post-test, and one-month follow-up test. In the control groups, children were asked to play a commercial game instead. In experiment 1, 48 participants chose either the training or control game voluntarily, whereas, in experiment 2, 44 participants were randomly assigned to either group. In both experiments, children exposed to the serious game demonstrated training effects from the Stroop spatial task and near-transfer effects from the Flanker task. However, transferring effects were not produced by the Go/No-go task. As a result, although the serious game "Crossing the Jungle" does not improve response inhibition, children aged 9 to 12 who play it may benefit from improved interference inhibition abilities. This provides evidence for the mutual independence of interference inhibition and response inhibition in children at this stage.

Designing Collaborative Tech-Rich Learning: Building a Multi-Player Game Controller with Makey Makeys

This lesson challenges learners to consider the educational benefits of using games to support teaching and learning practices through a Makey Makey design challenge. A Makey Makey is a digital toolkit that creates a closed-loop circuit by connecting alligator clips to conductive materials. I introduce the Makey Makey during a unit exploring the use of gaming technologies in PK-12 classrooms. During this lesson, groups of learners use a Makey Makey to build a multi-player game controller. This article describes a lesson that has been successfully integrated with undergraduate students in an Introduction to Technology in Education course and with middle and high school students.

Dynamic event-triggered robust optimal tracking control for multi-player nonzero-sum games with mismatched uncertainties and asymmetric constrained inputs

In this paper, a novel dynamic event-triggered robust optimal tracking control method is proposed for multi-player nonzero-sum game problem with mismatched uncertainties and asymmetric constrained inputs. First, the original asymmetric constrained-input multi-player system is transformed into a new multi-player system with symmetric constrained inputs implemented by the state-space transformation technique, and an augmented system is established based on the new system and the desired trajectory. Next, an auxiliary augmentation system is constructed to handle the robust tracking control problem. Then, a critic neural network with a novel modified weight updating law is established for each player to solve the event-triggered coupled Hamilton–Jacobi equations, which relaxes the restrictions of initial admissible control and persistence of excitation condition. Furthermore, efficient communication and computing are obtained by the newly established dynamic event-triggered mechanism, and the Zeno behavior can be excluded by introducing an exponential term. The uniformly ultimately bounded properties of all signals are proved. Finally, two numerical examples verify the effectiveness of the designed method.

Complex Characteristics and Control of Output Game in Cross-Border Supply Chains: A Perspective of Inter-Chain Competition

In this paper, an output dynamic game model of intertwined supply chains operating in two different countries is established. The Nash equilibrium point of the model and its stable region are obtained using nonlinear dynamic principles. The complex properties of the system, such as stability, period-doubling bifurcations, and chaos, are investigated using numerical simulations. Our results suggest that the level of output and the system’s profits undergo bifurcation and chaos with an increase in the output adjustment speed. An interesting phenomenon occurs in that higher tariffs lead to the expansion of the stable range of the supply chain in the product-exporting country. The chaotic behavior of the system is sensitive to the value of the initial level of output. In supply chain competition, each supply chain firm should make suitable adjustments to the speed of output. To maintain the stability of domestic markets, excessive tariffs should be avoided. It is essential that each supply chain firm evaluates the potential impacts of different initial output values when making initial decisions. Using the method of delayed feedback control, the chaotic behavior of the system can effectively be controlled. These findings offer valuable and novel insight into inter-chain competition in supply chain networks.

Enhancing structural response via macro-micro hierarchy for piezoelectric nanogenerator and self-powered wearable controller

Piezoelectric polymeric composites exhibit pronounced impact on flexible electronic devices, demonstrating the capacity to harvest high-entropy energy. Nevertheless, the conventional incorporation of piezoelectric ceramics is gradually approaching its limitations due to the unexpected increase in Young's modulus and the resulting deterioration in output. In this study, we propose a macro–micro hierarchical (MMH) design concept for the preparation of a polyvinylidene fluoride/barium titanate piezoelectric nanogenerator (PENG). Micro porosity is introduced into the macro truss structure through in-situ foaming additive manufacturing. Leveraging the synergistic effects of the micro-porous structure (stress concentration and piezoelectret) and the macro-truss design (strain accumulation), the output capacity, i.e., open-circuit voltage and short-circuit current of the MMH-PENG, is effectively amplified from 16.1 V and 350 nA to 24.5 V and 642 nA, respectively. Upon further integration onto a flexible substrate, the MMH-PENG exhibits compelling features, including substantial flexibility, controllable sensitivity, easy detachability, and reusability, facilitating practical applications as a wearable and self-powered controller. Illustrated by applications in video game and robotic controllers, the MMH-PENG introduces a pioneering concept that enhances output performance through the simultaneous construction of a macro–micro hierarchy, paving the way for potential large-scale utilization of piezoelectric nanogenerators.

Two-qubit entangling operators as chaos control in a discrete dynamic Cournot duopoly game.

The current trend in economics research is to incorporate quantum mechanical concepts to increase the security of business models. This interdisciplinary field of study represents real-world market dynamics more closely than do its classical counterparts. In this paper, we shed light on the significance of the two-qubit entangling operators in controlling chaos. We introduce a modified Eisert-Wilkens-Lewenstein scheme in a nonlinear Cournot duopoly game with complete and incomplete information. By doing so, the following interesting results are obtained: To begin, monopoly in a duopoly game can be avoided with the use of special perfect entanglers. Also, the stability analysis shows that there exists a class of entangling operators which can stabilize an unstable system and vice versa. Second, numerical analysis highlights the two-qubit entangling operators which can stabilize a chaotic system or at least delay chaos. Finally, we show that with an appropriate choice of initial state and speed of adjustments, entangling operators can decrease the sensitivity of the system. In short, while we know the importance of entangling operators in quantum game theory, in this paper we indicate the significance of operators in the context of a chaotic system.

Initial Excitation-Based Optimal Control for Continuous-Time Linear Nonzero-Sum Games

Initial Excitation-Based Optimal Control for Continuous-Time Linear Nonzero-Sum Games

Barrier-Critic Adaptive Robust Control of Nonzero-Sum Differential Games for Uncertain Nonlinear Systems With State Constraints

Barrier-Critic Adaptive Robust Control of Nonzero-Sum Differential Games for Uncertain Nonlinear Systems With State Constraints

Deep convolutional neural network-based Leveraging Lion Swarm Optimizer for gesture recognition and classification

<abstract> <p>Vision-based human gesture detection is the task of forecasting a gesture, namely clapping or sign language gestures, or waving hello, utilizing various video frames. One of the attractive features of gesture detection is that it makes it possible for humans to interact with devices and computers without the necessity for an external input tool like a remote control or a mouse. Gesture detection from videos has various applications, like robot learning, control of consumer electronics computer games, and mechanical systems. This study leverages the Lion Swarm optimizer with a deep convolutional neural network (LSO-DCNN) for gesture recognition and classification. The purpose of the LSO-DCNN technique lies in the proper identification and categorization of various categories of gestures that exist in the input images. The presented LSO-DCNN model follows a three-step procedure. At the initial step, the 1D-convolutional neural network (1D-CNN) method derives a collection of feature vectors. In the second step, the LSO algorithm optimally chooses the hyperparameter values of the 1D-CNN model. At the final step, the extreme gradient boosting (XGBoost) classifier allocates proper classes, i.e., it recognizes the gestures efficaciously. To demonstrate the enhanced gesture classification results of the LSO-DCNN approach, a wide range of experimental results are investigated. The brief comparative study reported the improvements in the LSO-DCNN technique in the gesture recognition process.</p> </abstract>

Maximal oxygen uptake and cardiovascular adaptation to handball game specific endurance circuit training in handball players

Now a day, the sports team coaches, trainers and other support staffs have a hectic job to deal with the preparation of their team players with consistent, precise and greater effort to meet the demands of the competition in shortest duration. Various training modalities were adopted depending upon the necessity that improves sports performance. Handball team coaches apparently felt that fitness and skill go hand in hand, where sports specific training caters to achieve this goal. Thus, the present study focussed on assessing the adaptations of maximal oxygen uptake and cardiovascular variables to handball game specific endurance circuit training. To accomplish the purpose twenty-four (24) trained university male handball players with a playing experience of more than eight years gave consent to participate. The selected twenty-four players were classified into two groups as Handball Game Specific Endurance Circuit Training Group (HGSECTG = 12) and Control Group (CG = 12). To test the effectiveness of handball game specific endurance circuit training (HGSECT) players performed specifically constructed handball circuit 3 days in a week for 12 weeks at 90-95 % of maximal heart rate for two minutes duration and active recovery by walking. HGSECTG showed significant improvement on maximal oxygen uptake (t = 8,516, p < 0,05) and distance covered in Yo-Yo test also increased (t = 8,4, p < 0,05). Similarly, cardiovascular modifications are noted in maximum heart rate (HRPEAK, t = 2,372, p < 0,05) and resting heart rate (HRREST, t = 3,975, p < 0,05) and percentage of heart rate reserve (%HRRESERVE, t = 3,004, p < 0,05). We conclude that handball game specific endurance training programs could be sufficiently suitable to develop maximal oxygen uptake and positive adaptation of cardiovascular variables in twelve weeks of training among handball players. The intensity, duration, frequency and recovery of the intervention displayed improvement in VO2PEAK with reduced condition on cardiovascular stress

Dynamic muscular endurance as an indicator of functional readiness of cyber-athletes.

Aim: To study the dynamic muscular endurance of hand movement according to the tapping test in connection with the manifestations of cognitive qualities of cyber-athletes and students involved in computer games as a hobby. Materials and Methods: Dynamic muscular endurance of the right and left hands of the examined subjects was studied (using the tapping test method), as well as the reaction to a moving object using the diagnostic complex "Diagnost-1". Correction tables (Landolt rings) were used to study voluntary attention. 45 students of the National University of Ukraine on Physical Education and Sport of both sexes, aged 17-26, took part in the study, among whom 10 are cyber-athletes (sports experience of 1-10 years), 15 amateurs (involved in computer games as a hobby) and 20 students who do not play computer games (control group). Results: In cyber-athletes and students involved in computer games, the dynamic muscular endurance of the movement of the hand of the subdominant hand was greater than in students who did not engage in computer games. A higher level of dynamic muscular endurance for the subdominant hand and less functional asymmetry according to the tapping test scores in cyber-athletes were associated with a more successful performance of the attention test. Conclusions: A higher level of dynamic muscular endurance for the subdominant arm and a smaller functional asymmetry according to the tapping test indicators in e-athletes can be considered as an indicator of functional readiness.

Human Collaborative Control of Lower-Limb Prosthesis Based on Game Theory and Fuzzy Approximation.

For leg prosthesis user, the soft tissue and skin under the stump of are not accustomed to weight bearing, excessive continuous contact pressure can lead to the risk of degenerative tissue ulceration. This article presents a novel human-robot collaborative control scheme that achieves control weight self-adjustment for robotic prostheses to minimize interaction torque. To establish the human-robot interaction relationship, we regard the contact pressure between human residual limb and the prosthetic receiving cavity as the interaction force. We aim at reducing the interaction force under the premise of minimally changing the original motion trajectory of the robotic prosthesis. The control scheme mainly includes trajectory optimization based on a dual-agent game control scheme under a cooperative relationship, and a fuzzy logic system for improving the control accuracy of trajectory tracking of robotic prostheses with unknown dynamic parameters. Experiments were carried out on two amputee participants to verify the proposed human-robot interactive control scheme in a robotic prosthesis. The results show that the interaction torque could be reduced while maintaining minimal trajectory tracking error. The proposed control scheme could potentially facilitate the dexterous manipulation of leg prostheses, thus benefiting amputees.

Risk-sensitive control, single controller games and linear programming

Risk-sensitive control, single controller games and linear programming

Accurate and Efficient Sitting Posture Recognition and Human‐Machine Interaction Device Based on Fabric Pressure Sensor Array and Neural Network

AbstractThis article presents a novel approach to interact with users through posture recognition, leveraging its advantages of convenience and real‐time feedback to enhance user engagement and personalized experiences. In contrast to traditional methods that rely on camera‐based posture detection, this study proposes a deep learning‐based framework for posture recognition by classifying the distribution of body pressure under different sitting positions. The system integrates a large‐area, highly flexible fabric pressure sensor array into the chair, which collects data on posture‐specific pressure patterns for training and identification purposes. A deep learning algorithm, specifically the LeNet architecture, is employed to classify 49 different sitting positions based on angular variations, including body tilt to the left or right, standard posture, and forward or backward leaning. The proposed approach achieves an impressive accuracy rate of 99.86%. Furthermore, the application of this posture recognition system in VR devices enables intelligent chair control for VR games. This research provides strong support for future advancements in chair design and human‐computer interaction technologies, enhancing ergonomic designs in various domains such as automotive seats, office chairs, and medical seating, while simultaneously improving user comfort and well‐being.

GANs training: A game and stochastic control approach

AbstractTraining generative adversarial networks (GANs) are known to be difficult, especially for financial time series. This paper first analyzes the well‐posedness problem in GANs minimax games and the widely recognized convexity issue in GANs objective functions. It then proposes a stochastic control framework for hyper‐parameters tuning in GANs training. The weak form of dynamic programming principle and the uniqueness and the existence of the value function in the viscosity sense for the corresponding minimax game are established. In particular, explicit forms for the optimal adaptive learning rate and batch size are derived and are shown to depend on the convexity of the objective function, revealing a relation between improper choices of learning rate and explosion in GANs training. Finally, empirical studies demonstrate that training algorithms incorporating this adaptive control approach outperform the standard ADAM method in terms of convergence and robustness. From GANs training perspective, the analysis in this paper provides analytical support for the popular practice of “clipping,” and suggests that the convexity and well‐posedness issues in GANs may be tackled through appropriate choices of hyper‐parameters.

Reinforcement learning-based formation-surrounding control for multiple quadrotor UAVs pursuit-evasion games

This paper proposes a reinforcement learning-based formation-surrounding control method for multiple quadrotor unmanned aerial vehicles (UAVs) pursuit-evasion (MPE) games system subject to external disturbances. In the framework of the MPE games, the pursuers aim to equally surround the evaders which try to avoid being surrounded when forming the desired formation. By constructing position and attitude tracking error subsystems of quadrotor UAV, this paper proposes two control strategies which combines the feedforward control technique and reinforcement learning (RL) method. First, two novel cost functions are presented for the quadrotor UAV with external disturbances. Then, two control schemes based on RL have been developed to guarantee the stability of the tracking error subsystem. Subsequently, two critic-only neural networks (NN) weight update laws that only satisfy finite excitation conditions are proposed to estimate the optimal cost function. Furthermore, Nash equilibrium for multiple quadrotor UAVs is achieved by means of RL strategy to solve the Hamilton-Jacobi-Isaacs (HJI) equations. And the property of equally surrounding is proved for the first time by utilizing Euler's formula in this paper. Finally, the numerical simulation results are given to show the effectiveness and superior performance of the proposed control method.

Design and Implementation of Multi Player Pong Game using Altera DE2 Board

Pong game is a simple but entertaining game of logic control. This research paper presents the design and implementation of an FPGA-based Pong game that runs on an Altera DE2 board using Verilog HDL. This article explains the VGA controller, object creation and animation, and text subsystem and of course how to link them all together to build a functioning circuit. There is an interesting multi-player mode and single-player mode feature in this design scheme. This game's multiplayer mode features both real-time and automatic players to create a competitive atmosphere. This design method followed less complicated, fastest processing, and utilized memory requirements and logic elements. The single-player mode uses 1.3% of total logic elements, the two-player mode uses 1.32%, and automatic player vs. real player uses 1.456% of total logic elements which is very small compared to the other gaming schemes and it reduces the processing time that is cost-effective for universal use. All the modules are designed by using Verilog HDL. The synthesis is done with the help of Altera DE2 FPGA. Functional simulation and synthesis prove that the design is universally usable and combines different modules in one module that presents sound entertainment and extends the electronics application-based work in the future.

Convergence Analysis and Strategy Control of Evolutionary Games With Imitation Rule on Toroidal Grid

Convergence Analysis and Strategy Control of Evolutionary Games With Imitation Rule on Toroidal Grid