Virtual Input Research Articles

Evaluating the Performance of Hand-Based Probabilistic Text Input Methods on a Mid-Air Virtual Qwerty Keyboard.

Integrated hand-tracking on modern virtual reality (VR) headsets can be readily exploited to deliver mid-air virtual input surfaces for text entry. These virtual input surfaces can closely replicate the experience of typing on a Qwerty keyboard on a physical touchscreen, thereby allowing users to leverage their pre-existing typing skills. However, the lack of passive haptic feedback, unconstrained user motion, and potential tracking inaccuracies or observability issues encountered in this interaction setting typically degrades the accuracy of user articulations. We present a comprehensive exploration of error-tolerant probabilistic hand-based input methods to support effective text input on a mid-air virtual Qwerty keyboard. Over three user studies we examine the performance potential of hand-based text input under both gesture and touch typing paradigms. We demonstrate typical entry rates in the range of 20 to 30 wpm and average peak entry rates of 40 to 45 wpm.

UbiSurface: A Robotic Touch Surface for Supporting Mid-air Planar Interactions in Room-Scale VR

Room-scale VR has been considered an alternative to physical office workspaces. For office activities, users frequently require planar input methods, such as typing or handwriting, to quickly record annotations to virtual content. However, current off-the-shelf VR HMD setups rely on mid-air interactions, which can cause arm fatigue and decrease input accuracy. To address this issue, we propose UbiSurface, a robotic touch surface that can automatically reposition itself to physically present a virtual planar input surface (VR whiteboard, VR canvas, etc.) to users and to permit them to achieve accurate and fatigue-less input while walking around a virtual room. We design and implement a prototype of UbiSurface that can dynamically change a canvas-sized touch surface's position, height, and pitch and yaw angles to adapt to virtual surfaces spatially arranged at various locations and angles around a virtual room. We then conduct studies to validate its technical performance and examine how UbiSurface facilitates the user's primary mid-air planar interactions, such as painting and writing in a room-scale VR setup. Our results indicate that this system reduces arm fatigue and increases input accuracy, especially for writing tasks. We then discuss the potential benefits and challenges of robotic touch devices for future room-scale VR setups.

Dynamic compensator-based near-optimal control for unknown nonaffine systems via integral reinforcement learning

In this paper, a dynamic compensator-based near-optimal control approach for unknown nonaffine nonlinear systems is developed by using integral reinforcement learning. Since system dynamics is unknown, it is difficult to obtain the optimal control policy via neuro-dynamic programming. To address this problem, a general dynamic compensator is introduced as the virtual control input to augment the unknown nonaffine nonlinear system as a partially unknown affine system. For the augmented system, a novel quadratic value function is designed with the system states, the actual control input and the virtual control input. The optimal control of the augmented system can be regarded as the near-optimal control for the original system since the novel optimal value function is an upper bound of the original optimal value function. In order to avoid the identification of system dynamics, the integral reinforcement learning framework is utilized to derive the optimal control based on the solution of Hamilton–Jacobi–Bellman equation via the critic-only structure. Meanwhile, the weight learning rule of the critic neural network is presented with the experience replay technique to relax the persistence of excitation condition. Moreover, the uniform ultimate boundedness of weight estimation errors and the stability of the closed-loop system are guaranteed by using the Lyapunov’s direct method. Finally, simulation results of two examples demonstrate the effectiveness of the developed dynamic compensator-based near-optimal control method.

State evaluation method for complex task network models

This paper focuses on the assessment of non-complete states in complex task networks and establishes an online capability boundary assessment model based on real-time feature parameters. By utilizing multivariate heterogeneous state features for data mining and pattern recognition, we delve into virtual sample generation technology using an overall diffusion trend approach. This starts by examining the validity of sample data and analyzing the relevance of data acquired within the feature space. Building on this foundation, the overall diffusion trend method is applied to generate virtual sample input that aligns with the sampling distribution characteristics of avionics system equipment. To address non-complete state evaluation in complex task networks, we design a comprehensive model. This model involves constructing a system signal flow block diagram and a structural diagram depicting the available capacity of subsystems and the overall system tasks. The establishment of unknown function relationships in signal connections is achieved through a fusion of neural network and fuzzy logic systems. Finally, an intelligent optimization algorithm is employed to determine system parameters. Utilizing this neuro-fuzzy system in simulation, we attain real-time system responses and evaluate the output regarding the available capacity of system tasks.

Sampled‐data robust practical tracking of Euler‐Lagrange systems with an uncertain exosystem

AbstractThis article focuses on the problem of sampled‐data practical tracking for Euler‐Lagrange systems subject to uncertain parameters. We assume that the system matrix of the exosystem and the Euler‐Lagrange system both contain unknown parameters, which is clearly more practical. The existence of unknown system parameters invalidates existing control strategies and poses a major challenge to the solvability of the problem. With the help of the internal model principle and the adaptive control technology, we propose a novel sampled‐data dynamic compensator to overcome the challenge of unknown parameters in exosystems. In particular, a virtual sampling adaptive control input is proposed to deal with uncertainties in the exosystem matrix. Then, a sampled‐data control law is constructed to guarantee that, by any fast predefined rate, the tracking error exponentially approaches any small predefined ranges of the origin, thus ensure the tracking performance. Finally, we apply our result to a three‐link robot manipulator system.

Energy Efficiency of Kenaf Cultivation in Kelantan, Malaysia by Using Data Envelopment Analysis

Agricultural production has become aggressive energy concentrated in a determination to supply more food to the increasing population and provide sufficient and adequate essentials. The limited natural resources and the effect of the use of different energy sources on the environment and human health such as global warming, it is necessary to investigate energy consumption patterns in agriculture. In this study data envelopment analysis method approach has been used to determine the efficiencies of kenaf farmers concerning energy use in kenaf cultivation production activities in Kelantan, Malaysia. The study’s empirical result will help to segregate the 15 most efficient farmers and inefficient ones. Other than that, the data envelopment analysis method also will help to identify wasteful uses of energy from different sources by inefficient farmers and to suggest reasonable savings in energy uses from different sources of inputs. The rank method and distribution of virtual inputs are used to get insights into the individual Kenaf farmer performance, rank efficient farmers and identify the improved operating practices followed by a group of truly efficient Kenaf farmers. The result of the analysis indicates the average value of technical efficiency, Pure technical efficiency and Scale efficiency scores of kenaf cultivation production were 0.854, 0.962 and 0.887 respectively. These results imply that all inefficient farmers operate at optimum scale size and 11.3% energy saving is possible without affecting Kenaf yield level. Result also reveals higher potential contribution to total energy saving is from fertilizer (86.5%), Diesel (13%), Pesticides (0.3%) and human labor (0.2%).

The Virtual Resistor Used in Droop Control of DC Microgrids is a Series Loss-Free Resistor (SLFR)

The recently introduced series loss-free resistor (SLFR) is used to obtain the non-linear differential equations that describe the dynamic response of a DC-DC converter with virtual resistor and input LC filter, employed in the voltage droop control of DC microgrids. The equilibrium point and the stability criteria of the converter are presented. The results of the theoretical analysis are validated via computer simulation of largeamplitude transients for step change of the input voltage, the output current and the voltage reference signal. It is shown that the energy absorbed by the virtual resistor is not converted into heat but returned to the input stage by a power source. Thus, the paradox of the virtual resistor, which causes a voltage drop but does not consume power, is resolved. This is a theoretical study.

Iterative Learning Consensus for Nonstrict Feedback Multiagent Systems With Unknown Control Direction and Saturation Input

This article addresses the adaptive iterative learning control consensus problem for a class of unknown nonlinear high-order nonstrict feedback multiagent systems with partially unknown virtual and actual control directions and saturation inputs. Due to the unknown nonlinear dynamics of all follower agents, fuzzy logic systems combined with adaptive way are employed to design control protocol. And the Nussbaum-gain method is utilized to deal with partially unknown virtual and actual control directions in each step of the backstepping design procedure. With backstepping design process constructing adaptive fuzzy iterative learning control scheme for each agent, our proposed new control algorithm ensures that the outputs of all follower agents can accurately track the leader on finite time <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$[ {0,T} ]$</tex-math></inline-formula> . Finally, the performance of our new algorithm is demonstrated by two simulation examples.

Global Predefined-Time Adaptive Neural Network Control for Disturbed Pure-Feedback Nonlinear Systems With Zero Tracking Error.

This article presents a global adaptive neural-network-based control algorithm for disturbed pure-feedback nonlinear systems to achieve zero tracking error in a predefined time. Different from the traditional works that only solve the semiglobal bounded tracking problem for pure-feedback systems, this work not only achieves that the tracking error globally converges to zero but also guarantees that the convergence time can be predefined according to the user specification. In order to get the desired predefined-time controller, first, a mild semibound assumption for nonaffine functions is skillfully proposed so that the design difficulty caused by the structure of pure feedback can be easily solved. Then, we apply the property of radial basis function (RBF) neural networks (NNs) and Young's inequality to derive the upper bound of the term that contains the unknown nonlinear function and external disturbances, and the designed adaptive parameters decide the derived upper and robust control gain. Finally, the predefined-time virtual control inputs are presented whose derivatives are further estimated by utilizing finite-time differentiators. It is strictly proved that the proposed novel predefined-time controller can guarantee that the tracking error globally converges to zero within predefined time and a practical example is shown to verify the effectiveness and practicability of the proposed predefined-time control method.

Systematic design of a multi-input multi-output controller by model-based decoupling: a demonstration on TCV using multi-species gas injection

In this paper, we present the first results of a systematically designed multi-input multi-output gas-injection controller on Tokamak á Configuration Variable (TCV). We demonstrate the simultaneous real-time control of the NII emission front position and line-integrated electron density using nitrogen and deuterium gas injection. Injection of nitrogen and/or deuterium affects both the NII emission front position and line-integrated electron density. This interplay between control loops is termed interaction and, when strongly present, makes designing a controller a significantly more complex problem. Interaction between the control loops can be reduced to an acceptable level by redefining inputs, decoupling the multi-input multi-output control problem to separated single-input single-output problems. We demonstrate how to achieve this by defining virtual control inputs from linear combinations of the actuators available. For the demonstration on TCV, linear combinations of deuterium and nitrogen gas injection are computed from transfer-function models to obtain these virtual inputs. The virtual inputs reduce the interaction in the control-relevant frequency range to a point where control of the NII emission front position and line-integrated electron density can be considered decoupled, allowing for the much simpler design of single-input single-output controllers for each loop. Implementing the controllers with the virtual inputs gives the multi-input multi-output gas-injection controller. This approach is well established in the control community, and is presented here as a demonstration to drive developments of multi-input multi-output control strategies. In particular, the envisioned control of particle- and heat fluxes impacting the divertor targets by injection of multiple gas species.

Adaptive Neural Network Output-Constraint Control for a Variable-Length Rotary Arm With Input Backlash Nonlinearity.

This article studies the problem of deformation reduction and attitude tracking for a rotated and extended flexible crane arm with input backlash-saturation and output asymmetrical constraint. By employing Halmilton's principle, the arm system model is formulated by a set of partial and ordinary differential equations (ODEs). Given the modeling inaccuracy, a radial neural network (RNN) is used to approximate system parameters. To better design the controllers, the backstepping technique is applied to the control design. For input nonlinearities with backlash and saturation, we reversely transform them as an asymmetric saturation constraint via a virtual input. A barrier Lyapunov function (BLF) containing logarithmic terms is constructed to guarantee the asymmetric output constraints and the uniformly ultimate boundedness and stability of the arm system are proved. Finally, to testify the effectiveness of the proposed controllers, numerical simulations are carried out, and responding simulation diagrams are displayed.

Adaptive observer-based control of arbitrarily switched fractional-order uncertain nonlinear system subject to input nonlinearities and asymmetric output constraints

This paper addresses an adaptive observer-based control for a switched fractional-order uncertain nonlinear system subject to input nonlinearities and asymmetric output constraints. The switching signal is arbitrary, and the designed controller needs no information regarding this signal. By employing a universal framework, the designed controller can cope with different input nonlinearities in different modes without having any information about their characteristics. It is assumed that only system output is measured, and a designed observer estimates the system states for control implementation. The uncertainties are approximated by intelligent approximators like fuzzy logic systems (FLS) or neural networks (NN). The time-varying asymmetric output constraints have been satisfied using a common barrier Lyapunov function (BLF). Using the common Lyapunov function (CLF) method and the backstepping control technique, the virtual controllers, adaptive law and control input have been designed, and the boundedness of closed-loop signals has been guaranteed. The effectiveness of the designed control scheme has been demonstrated through a numerical and two practical simulation examples.

On novel distributed fixed-time formation tracking of multiple hypersonic flight vehicles with collision avoidance

This paper aims to achieve collision-free formation tracking for multiple hypersonic flight vehicles without introducing conventional approximators such as fuzzy logic systems and neural networks. By relying on the hierarchical control theory, a globally fixed-time distributed formation control protocol with guaranteed tracking behaviors is proposed. Firstly, for the purpose of deriving the virtual control commands in the three-dimensional translational dynamics, a fixed-time consensus control input is presented. Then, by designing a comprehensive performance-preserving feedback control law, the actual control commands are derived, so that the virtual control inputs can be tracked timely and accurately. Meanwhile, with resort to a novel exponential-type artificial potential function, a collision-free control input is proposed to avoid close proximity between adjacent hypersonic flight vehicles. Summarily, the outstanding advantage of the proposed protocol is that it simultaneously realize the collision-free formation tracking, fixed-time stability and performance-preserving control for multiple hypersonic flight vehicles with uncertain dynamics. Lyapunov stability criterion verifies the closed-loop stability, showing that the error signals can attenuate to a tiny region around the origin within a user-specified time. Finally, comparative numerical simulations are performed to further demonstrate the theoretical results.

Trajectory tracking control of unmanned surface vehicle based on exponential global fast terminal sliding mode control

This article studies the trajectory tracking of an underactuated unmanned surface vehicle. The trajectory tracking controller is divided into two parts: the kinematics controller and the dynamic controller. Taking the hull coordinate system of the unmanned surface vehicle as the datum plane, the kinematics controller is designed based on the backstepping method, and the virtual control input of the speed and the heading deviation of the ship are obtained. A dynamic controller is designed through an exponential sliding surface to stabilize the error of speed and heading deviation, thereby stabilizing the position error. In the process of designing the controller, the stability of the entire closed-loop control system is analyzed using Lyapunov stability theory. Finally, the designed numerical simulation experiment is carried out. The proposed exponential sliding mode controller is compared with the global integral sliding mode controller, and it is verified that the proposed controller has better effectiveness and robustness in the trajectory tracking of the underactuated unmanned surface vehicle.

Flatness-based control in successive loops for electropneumatic actuators and robots

The control problem for the nonlinear dynamics of robotic and mechatronic systems with electropneumatic actuation is solved with the use of a flatness-based control approach which is implemented in successive loops. The state-space model of these systems is separated into a series of subsystems, which are connected between them in cascading loops. Each one of these subsystems can be viewed independently as a differentially flat system and control about it can be performed with inversion of its dynamics as in the case of input–output linearized flat systems. In this chain of i=1,2,…,N subsystems, the state variables of the subsequent (i+1th) subsystem become virtual control inputs for the preceding (ith) subsystem, and so on. In turn, exogenous control inputs are applied to the last subsystem and are computed by tracing backwards the virtual control inputs of the preceding N−1 subsystems. The whole control method is implemented in successive loops and its global stability properties are also proven through Lyapunov stability analysis. The validity of the control method is confirmed in two case studies: (a) control of an electropneumatic actuator, (ii) control of a multi-DOF robotic manipulator with electropneumatic actuators.

Generating virtual samples to improve learning performance in small datasets with non-linear and asymmetric distributions

In today’s highly competitive environment, modeling the relationship between inputs and outputs using limited data for a management system at the early stages is important, but difficult. The virtual sample generation (VSG) method has been proposed in many studies to explore potential information to improve prediction performance of learning models for small datasets. However, those studies in general must assume an underlying distribution such as a linear triangular membership function or a Gaussian distribution to generate virtual samples. Thus, previous VSG methods may not effectively upgrade learning performance when the assumed distribution is not elastic enough for small datasets. To address this issue, in this paper, we proposed a novel VSG method called Newton-VSG method to generate virtual samples for small datasets with non-linear and asymmetric distributions. In the suggested method, we used Newton's method to estimate the minimum value of data range and the shape of data distribution. Further, we developed two deep learning models including Siamese network (SN) model for screening virtual sample input values and bagging auto-encoder (AE) model for predicting virtual sample output to ensure the quality of virtual samples. One real dataset for solidification cracking susceptibility test data and an other real dataset obtained from TFT-LCD process of a leading company in Taiwan were used to demonstrate the efficacy of the proposed method. On the partial least square regression (PLSR) and the back propagation neural network (BPNN) predictive models, we compared the proposed method with three state-of-the-art VSG methods in items of the mean absolute error (MAE) and the root mean squared error (RMSE). The experimental results demonstrated that the proposed method outperforms the other three VSG methods in prediction accuracy for small datasets.

Adaptive LDPC coded high data rate based IR-UWB combined with VMIMO system for vehicular communication links

The automotive industry is developing significantly with the evolution of vehicular networks, intelligent transportation systems, and autonomous cars. These technologies are made achievable by recent advances in software, hardware, and communication systems, along with various applications and standardization efforts. On the other side, many impairments have existed due to these evolving, limiting the process of vehicular networks. So, this paper proposes an implementation of adaptive LDPC-coded, high data rate-based Impulse Radio-Ultra Wide Band (IR-UWB) combined with Virtual Multiple Input Multiple Output (VMIMO) systems for Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) wireless communications. Furthermore, they are merged with orthogonal pulse shaping using a modified Hermite pulse. Besides, the paper compares Zero-Forcing (ZF) and Minimum Mean Square Error (MMSE) detection techniques. To enhance and mitigate the existing impairments facing the vehicular networks. The proposed system’s performance is evaluated using both techniques, utilizing Bit Error Rate (BER), processing time, and resultant throughput analysis for both channel models. The simulation results include Non-Line of Sight (NLoS) and Line of Sight (LoS) scenarios for both channel models. Moreover, the simulation considers the effect of path loss and noise on the transmitted signal. In addition, the recent LDPC decoding algorithm termed “Modified Implementation Efficient Reliability Ratio Weighted Bit Flipping” (MIERRWBF) is evaluated through all latter channel models. At last, a novel adaptive LDPC decoder is proposed to further enhance the performance of all vehicular communication links under study. Consequently, the paper proposed an integrated adaptive LDPC-coded high data rate communication system to enhance vehicular communications using VMIMO mixed with modified Hermite orthogonal pulse shaping and deploying the MMSE recommended detection technique.

Neural Adaptive Fixed-Time Control for Nonlinear Systems With Full-State Constraints.

This article aims at this problem of adaptive neural tracking control for state-constrained systems. A general fixed-time stability criterion is first presented, by which an adaptive neural control algorithm is developed. Under the action of the proposed adaptive neural tracking controller, the tracking error converges into a small neighborhood around the origin in fixed time; meanwhile, all system states abide by the corresponding state constraints for all the time. The main difference between the present research and the previous control schemes for state-constrained systems is that this article proposes a novel and feasible approach to ensure that the constructed virtual control signals satisfy the state constraints on the corresponding states viewed as the virtual control inputs. Such an approach guarantees theoretically that all the system states cannot violate their constrained requirements at any time. Finally, two simulation examples provide support to the proposed results.

Integrated vehicle control using adaptive control allocation

SummaryThe focus of this paper is an integrated, fault‐tolerant vehicle control algorithm for the overall stability of ground vehicles. The proposed scheme comprises a high‐level controller that creates a virtual control input and a low‐level adaptive control allocator that distributes the virtual control effort among redundant actuators. The proposed control framework distinguishes itself from earlier results in the literature by its ability to blend active suspension, steering and traction control channels, in the presence of uncertainties and time‐varying dynamics, without the need for fault identification. The control structure is validated in the simulation environment using a fourteen‐degree‐of‐freedom non‐linear vehicle model. The integrated controller is compared to the case of a conventional control approach where each control problem is solved separately. Our results show that, compared to the conventional approach, the proposed method ensures that the vehicle follows driver inputs with up to % higher longitudinal maneuver velocity, despite the presence of actuator failures and slippery road conditions. Furthermore, to demonstrate the benefit of integrating active suspension control to the overall control scheme, we replaced the suspension control of the proposed approach with an independent suspension control system for comparison purposes. We then showed that the integrated case provided % lower roll angle deviation, and % lower pitch angle deviation, in the presence of actuator effectiveness loss and adverse road conditions.

A point-wise minimization model for data envelopment analysis considering environmental variables

Environmental variables are widely recognized as a cause of differences in efficiency measurement. However, the existing literature on data envelopment analysis (DEA) in environmental factors ignores the impact of demand on output. To address this gap, we propose the Point-wise Minimization DEA model (PWMDEA), which considers contextual variables that affect demand and lead to differences in efficiency. The model obtains efficiency value by considering the minimum of virtual inputs and virtual demand. Then, efficiency is evaluated by minimizing the ratio of above minimum to virtual output. This one-step model avoids issues of multi-stage assumptions and requires less data, making it more applicable. Moreover, we demonstrate the accuracy of our new model by conducting simulations with given true efficiency values. The simulation results demonstrate that our model has the lowest ranking error when the output is affected by multiple inputs or when demand has a significant impact. In addition, we evaluate the efficiency of healthcare in 31 Chinese provinces by considering two environmental factors. The results suggest that provinces with lower financial investments or population loss received higher rankings from our proposed model. These findings provide plausible explanations and demonstrate the practical usefulness of our model.