Sensitivity analysis of the stimulus-response function of a staticnonlinear accommodation model

The effect of parameter variation of a nonlinear static feedback control model of the accommodation system was investigated. Simulations of a MATLAB/SIMULINK model showed a nonlinear relationship between stimulus and response in which the response curve was above the 1:1 line in the region to the left of the crossover and below the 1:1 line in the region to the right of the crossover. At the crossover, the response curve exhibited an inflection that was constant and equal to the tonic accommodation value (ABIAS). Sensitivity analysis showed that increasing depth of focus (deadspace range between ±DSP) increased the separation between the boundary lines of the deadspace region, with a larger separation associated with late-onset myopia and congenital nystagmus. Increasing accommodative controller gain (ACG) increased the slope of the function on either side of the deadspace, with lower ACG values corresponding to lower slopes that indicated an amblyopic deficit. Increasing ABIAS increased the accommodative level at the inflection region. In addition, the saturation level of the accommodative response decreased with increasing age, while the slope remained the same, which was consistent with the Hess-Gullstrand theory of presbyopia. The accuracy and relative simplicity of the model indicated that it could serve as a basis for further comprehensive investigation of the basic and clinical aspects of the accommodation system     

Static Behavior of Accommodation and Vergence: Computer Simulation of an Interactive Dual-Feedback System

The complex static behavior of accommodation (focusing) and vergence (eye-turn) of the human eye was modeled as an interactive dual-feedback system. Accommodative and vergence biases accounted for responses under the no stimulus condition. Analytical expressions were derived for relating the linear parameters in the model such as controller and cross gains to variables whose values could be obtained directly from experimental data. These parameters were incorporated into an interactive computer simulation program of the model. The computer simulation program was also used to obtain by trial and error the nonlinear parameters such as the lens plant saturation, accommodative controller gain for negative accommodative error, and the non-linear portion of vergence controller gain. The model responses show good agreement with critical experimental data.     

Effect of Vergence Adaptation on Convergence–Accommodation: Model Simulations

Several theoretical control models depict the adaptation effects observed in the accommodation and vergence mechanisms of the human visual system. Two current quantitative models differ in their approach of defining adaptation and in identifying the effect of controller adaptation on their respective cross-links between the vergence and accommodative systems. Here, we compare the simulation results of these adaptation models with empirical data obtained from emmetropic adults when they performed sustained near task through +2D lens addition. The results of our experimental study showed an initial increase in exophoria (a divergent open-loop vergence position) and convergence-accommodation (CA) when viewing through +2D lenses. Prolonged fixation through the near addition lenses initiated vergence adaptation, which reduced the lens-induced exophoria and resulted in a concurrent reduction of CA. Both models showed good agreement with empirical measures of vergence adaptation. However, only one model predicted the experimental time course of reduction in CA. The pattern of our empirical results seem to be best described by the adaptation model that indicates the total vergence response to be a sum of two controllers, phasic and tonic, with the output of phasic controller providing input to the cross-link interactions.     

Model of static accommodative behavior in human amblyopia

When a target is moved from far to near, the normal accommodative system responds by changing the crystalline lens power until the blur of the retinal image is reduced to an acceptable level for clear vision. This feedback mechanism, along with the experimentally derived deadspace operator (representing the depth of field), forward-loop gain, and tonic accommodative bias level have been successfully incorporated into a static model of the normal accommodative system. We extended this model to the investigation of static accommodative responses in amblyopic eyes. The experimentally derived accommodative controller gain of the amblyopic eye was typically smaller than in the fellow dominant eye. In addition, the experimentally derived depth of field was increased in most amblyopic eyes, while the accommodative bias showed no consistent difference between the two eyes. The decrease in accommodative controller gain and increase in depth of field were sufficient to account for the reduction of accommodative responses found in amblyopic eyes. Theoretically, the model provides a conceptual framework for understanding the static accommodative deficits found in amblyopic eyes. Practically, the accommodative controller gain parameter provides a means for uniform assessment of normal and abnormal accommodative function, both experimentally and in the clinic environment.     

A Quantitative Theory of Control Sharing Between Accommodative and Vergence Controllers

Static responses of human accommodation (focusing) and vergence (eye-turn) are controlled by two separate feedback control systems. The two systems also interact so that each is capable of producing an isolated motor response in the other. When both systems operate together, as in normal binocular movements, they must share control in some manner. A recently proposed static oculomotor control theory predicts that a given system's control responsibility will be determined by the strength (i.e., gain) of its interactive or cross-link pathway in addition to its own feedforward gain. To quantify this control sharing, a theoretically-based sensitivity function was developed which predicted each controller's contributions to the overall motor response. Evaluating these sensitivity functions using data obtained from four normal subjects indicated that a range of control sharing conditions may be used by the nervous system to successfully meet the control objectives of normal binocular vision. This suggests that the theory of vergence and accommodative control is a generalization of earlier theories developed by Maddox and Fincham-Walton which represented two extremes of control sharing seen in normals.     

Experimental evaluation of adaptive and robust schemes for robotmanipulator control

In this paper, adaptive and robust control schemes are compared in the tracking control of robot manipulator. In adaptive control, the authors classify the adaptive control laws that have been proposed into three types. They show that the most important difference among them is that in their PD gains. They investigate their tracking performances by laboratory experiment and show that they can have similar performances by adjusting their equivalent PD gains almost equally. In robust control, two degree of freedom (TDOF) controller is examined. The authors demonstrate its strong disturbance rejection performance and robustness to parameter variation by experiment. They analyze the stability of TDOF controller against the payload change. Finally, through these experiments, they consider the advantages of adaptive and robust schemes for robot manipulator control     

Dynamics of the disparity vergence step response: a model-basedanalysis

A new method to analyze the dynamics of vergence eye movements was developed based on a reconstruction of the presumed motor command signal. A model was used to construct equivalent motor command signals and transform an associated vergence transient response into an equivalent set of motor commands. This model represented only the motor components of the vergence system and consisted of signal generators representing the neural burst and tonic cells and a plant representing the ocular musculature and dynamics of the orbit. Through highly accurate simulations, dynamic vergence responses could be reduced to a set of five model parameters, each relating to a specific feature of the internal motor command. This dynamic analysis tool was applied to the analysis of inter-movement variability in vergence step responses. Model parameters obtained from a large number of response simulations showed that the width of the command pulse was tightly controlled while its amplitude, rising slope, and falling slope were less tightly regulated. Variation in the latter three parameters accounted for the most of the movement-to-movement variability seen in vergence step responses. Unlike version movements, pulse width did not increase with increased stimulus amplitude, although the other command signal parameters were substantially influenced by stimulus amplitude.     

Dynamic interactions between accommodation and convergence

Frequency-domain equations were derived for the current dual-interaction model of accommodation and convergence control, and its adaptive behavior was related to the system's parameters. Contrary to predictions based on the steady-state performance of the model [18], dynamic analysis showed that the AC/A is sensitive to the method of measurement and a procedure is established for its reliable determination. Additionally, the results provided theoretical support for the empirical finding that low AC/A and CA/C ratios are associated with high accommodative and convergence adaptation, respectively [15]. The derived equations should help future studies relate the physiological behavior of accommodative and convergence to specific model parameters.     

Repeated vergence adaptation causes the decline of visual functions in watching stereoscopic television

To evaluate visual fatigue when viewing stereoscopic TV, a technology expected to become the broadcasting display system of the future. Wide public acceptance of stereoscopic TV awaits resolution of many issues, including visual fatigue on viewing TV images. Visual fatigue was induced using a visual function simulator, consisting of prism and lens optical systems, while viewing stereoscopic TV. We assessed subject visual fatigue through subjective reports of symptoms and by the changes in visual functions. These functions included: viewer B [Js fusional break point, recovery point, accommodation step response, and visual evoked cortical potentials (VECP)]. Significant changes of some visual functions were found after watching simulated stereoscopic TV when the vergence load was heavy or when it changed over time; relative vergence limits decreased and the latency of VECP increased after watching, reflecting visual fatigue. After subjects rested, relative vergence limits recovered to pre-viewing levels. Our findings lead us to conclude that, aside from excessive horizontal binocular parallax, discontinuous changes in parallax is also a major factor that contributes to visual fatigue in the viewing of stereoscopic images. It also causes a decreased range of relative vergence, accommodation response, and a delay in the P100 latency of VECP.     

Improvement in Arteral Oxygen Control Using Multiple-Model Adaptive Control Procedures

A computer-based proportional-integral (PI) controller has been developed to control arterial oxygen levels in mechanically ventilated animals. Arterial oxygen saturation is monitored using a noninvasive oximeter and control is effected by adjusting the inspired oxygen fraction. The performance of the feedback system is sensitive to the open-loop gain so that the desired transient specifications can be achieved only by empirical adjustments of the PI controller. Because the open-loop gain includes the animal's response, it may vary with time and with the administration of positive end-expiratory pressure. Multiple-model adaptive control procedures were therefore used to desensitize the system to these variable gains. Computer simulations demonstrated the effectiveness of the algorithm over a wide variation of plant parameters. A comparison to a fixed, well-tuned proportional-integral controller showed an improvement in the regulatory response to a step disturbance. Animal experiments confirmed the feasibility of using multiple-model adaptive control to regulate arterial oxygen saturation.     

New robust adaptive control algorithm for high-performance ACdrives

This paper presents a new robust structure for a model reference adaptive control (MRAC) controller for field-oriented-controlled (FOC) drives which requires no prior knowledge of the drive parameters and is guaranteed to provide global asymptotic stability of the closed-loop system. This structure simplifies the design and implementation of the adaptive controller requiring less effort to synthesis than a standard MRAC system. Discussion on theoretical aspects, such as selection of a reference model, stability analysis proof, gain adaptive process, steady-state error elimination, and robustness to unmodeled dynamics are included. The paper describes many practical aspects of the implementation, such as adaptive gain analysis, adaptive rate selection, the gain variation limits, gain windup prevention measure, and initial values. The new robust adaptive controller has been successfully implemented on an FOC drive and experiment results for dynamic tracking, sudden loading and unloading, and gains adaptation under different operation conditions are presented to support the robustness of the proposed controller     

Fuzzy error governor: A practical approach to counter actuator saturation on flexible joint robots

In this paper, a practical method to counter actuator saturation based on a fuzzy error governor is developed and a complete case study is considered. In addition to good performance, the method has two attracting properties: It does not change the structure of the main controller, and therefore, the theoretically proven characteristics of the system are untouched, and it is simply implementable in practice. The proposed controller structure is applied on a flexible joint robot (FJR). The robust stability of the closed loop system for an n-DOF FJR is thoroughly analyzed and the proposed controller is implemented on a laboratory setup to show the ease of implementation and the resulting closed-loop performance. The main controller used for the n-DOF FJR consists of a composite structure, with a PD controller on the fast dynamics and a PID controller on the slow dynamics. The bandwidth of the fast controller is decreased during critical occasions with the fuzzy logic supervisor, which adjusts the loop gain to a proper level. Using Lyapunov direct method, the robust stability of the overall system is analyzed in presence of modeling uncertainties, and it is shown that if the PD and the PID gains are tuned to satisfy certain conditions, the closed loop system becomes UUB stable.     

Toward switching motor control

Traditionally focused on applications where humans are safely out of reach, the field of robotics was developed to perform repetitive tasks with high precision and accuracy. In contrast, recent developments have opened up a new subfield of robotics in which users are in direct contact with robotic devices. This physical interaction requires the device to be sensitive to human contact while maintaining performance. While hardware designs have progressed to fill this new area, the underlying controller remains nearly the same as that developed for industrial robots decades ago, imposing limits on the performance of the overall system. To address the unique requirements of these human-interactive robotic devices, we are working to replace the traditional amplifier with a high-speed switching controller. Combining the simplicity and efficiency of pulsewidth modulation with the high bandwidth of linear amplifiers, this approach demonstrates performance gains when implemented as a closed-loop current controller. We also extend this design to the proposal of a third-order architecture to combine an amplifier and servo controller into an integrated system with the potential to overcome the fundamental limits of today's systems and enable the future of human-interactive robotics.     

A cross-coupling model of vertical vergence adaptation

Vertical disparity vergence aligns the two eyes in response to vertical misalignment (disparity) of the two ocular images. An adaptive response to vertical disparity vergence is demonstrated by the continuation of vertical vergence when one eye is occluded. The adaptive response is quantified by vertical phoria, the eye alignment error during monocular viewing. Vertical phoria can be differentially adapted to vertical disparities of opposite sign located at two positions along the horizontal or vertical head-referenced axes. Vertical phoria aftereffects vary in amplitude as the eyes move from one adapted direction of gaze to another along the adaptation axis. A cross-coupling model was developed to account for the spatial variations of vertical phoria aftereffects. The model is constrained according to both single cell recordings of eye position sensitive neurons, and eye position measurements during and following adaptation. The vertical phoria is computed by scaling the activities of eye position sensitive neurons and converting the scaled activities into a vertical vergence signal. The three components of the model are: neural activities associated with conjugate eye position, cross-coupling weights to scale the activities, and vertical vergence transducers to convert the weighted activities to vertical vergence. The model provides a biologically plausible mechanism for vertical vergence adaptation     

Adaptive control of closed-circuit anesthesia

Closed-circuit anesthesia (CCA) is more economical and ecologically safer than open circuit anesthesia. However, gas concentrations are more difficult to control. Computer control of CCA has been proposed to facilitate its use. Past efforts have either been limited to the control of anesthetic gas concentrations or apply only to a small group of patients. This paper describes a comprehensive control system applicable to a large class of patients. This system controls the end-tidal oxygen and anesthetic gas concentrations, and the circuit volume. The CCA process was modeled by writing mass balance equations. Simplifying assumptions yielded a bilinear single-input-single-output model for the anesthetic gas concentration and a bilinear multiple-input-multiple-output model for the circuit volume and oxygen concentration. One-step-ahead controllers were used to control these two subsystems. Simulations showed that the control performance was most sensitive to the gas uptakes. Three independent, least-mean-squares estimation schemes were implemented to estimate the uptakes of oxygen, nitrous oxide, and anesthetic gas. These estimates were used in the control law and resulted in explicit adaptive control. The performance of the adaptive controller was compared to that of a fixed controller (with precalculated gas uptakes) in five animal experiments. The adaptive controller performed better than the fixed controller in all cases. The most significant difference was in the anesthetic gas response time 3.6 +/- 0.70 min for adaptive control and 7.04 +/- 5.62 min for fixed control. The adaptive controller was also robust with respect to variations in the system parameters such as the functional residual capacity, leak, deadspace and gas uptakes.(ABSTRACT TRUNCATED AT 250 WORDS)     

Neural network-based control of balanced robotic manipulators with joint flexibility

This paper addresses an improvement on the controlled performance of balanced manipulators in a practical level by implementing neural network based controller. The mass balancing of robotic manipulators has been shown to have favorable effects on the dynamic characteristics. However, it was also pointed out that for the manipulators having a certain degree of flexibility at the joints, due to the lowered structural natural frequencies and reduced velocity related terms, mass balancing results in vibratory motion at high speed operation. Such a vibratory tendency of the balanced flexible joint manipulator limits the admissible range of servo gains of the conventional controllers, making those controllers unsuitable for controlling the manipulator at high speeds.

To avoid such difficulty, an artificial neural network (NN) controller is introduced to realize the dynamic control of the balanced flexible joint manipulators. A feedforward type of NN controller is proposed and its performance is evaluated through a series of numerical simulations. The proposed NN controller showed much better tracking performances over the conventional PD controller.     

Recurrent-neural-network-based adaptive-backstepping control for induction servomotors

This study is concerned with the position control of an induction servomotor using a recurrent-neural-network (RNN)-based adaptive-backstepping control (RNABC) system. The adaptive-backstepping approach offers a choice of design tools for the accommodation of system uncertainties and nonlinearities. The RNABC system is comprised of a backstepping controller and a robust controller. The backstepping controller containing an RNN uncertainty observer is the principal controller, and the robust controller is designed to dispel the effect of approximation error introduced by the uncertainty observer. Since the RNN has superior capabilities compared to the feedforward NN for dynamic system identification, it is utilized as the uncertainty observer. In addition, the Taylor linearization technique is employed to increase the learning ability of the RNN. Meanwhile, the adaptation laws of the adaptive-backstepping approach are derived in the sense of the Lyapunov function, thus, the stability of the system can be guaranteed. Finally, simulation and experimental results verify that the proposed RNABC can achieve favorable tracking performance for the induction-servomotor system, even with regard to parameter variations and input-command frequency variation.     

Multiple-Model Adaptive Control of Blood Pressure Using Sodium Nitroprusside

This paper presents a multiple-model adaptive control procedure for sodium nitroprusside regulation of arterial pressure. Pole-placement, via state-variable feedback, is included in the controller to achieve desired performance characteristics. A Smith predictor in the controller effectively removes the infusion delay time, thus simplifying the control analysis and design. Proportional-plus-integral control is used to achieve zero steady-state error. Computer simulations on linear transfer function models with varying gains, time constants, and infusion delays demonstrate the robustness of this multiple-model controller. Additional simulations on nonlinear, pulsatile-flow cardiovascular models lend further support to the ultimate use of this controller for blood-pressure regulation.     

Fluidic Intraocular Lens With a Large Accommodation Range

Fluidic lenses with tunable focal distances were experimented in a human eye model as intraocular lenses (IOLs). Fluidic IOL shows superior performance to fixed or optic-shift accommodating IOLs because of their much wider accommodation range. Our results demonstrate that fluidic IOL can achieve an accommodation range of eight diopters, equivalent to the performance of young adults. Finite-element analysis shows that the required force for the fluidic IOL to achieve the desired amount of accommodation is comparable to the force generated from ciliary muscles of human eyes, as an initial indication that an implanted fluidic IOL may be controllable by eye muscles.      

Efficient architecture and hardware implementation of hybrid fuzzy-Kalman filter for workload prediction

In modern systems, many well-known techniques (e.g., dynamic voltage and frequency scaling, job scheduling etc.) have been developed to achieve low power, high performance, appropriate quality-of-service or other specific purposes. Workload prediction is an extremely critical factor for bringing these techniques into full play. However, it is very difficult to accurately predict the workloads of upcoming tasks if they are varying drastically. In this paper, we propose a new hybrid fuzzy-Kalman filter and the corresponding area-efficient hardware architecture to accurately and quickly predict the workload with large variation. To decrease the hardware complexity while maintaining sufficient accuracy, the computation of Kalman Gain is simplified with a lookup table method. In addition, the workload and covariance values in Kalman filter are properly normalized and truncated to significantly reduce the bit length of hybrid workload predictor. Furthermore, a simplified fuzzy controller is developed to adaptively adjust the measurement noise covariance of Kalman filter so that the prediction error can be further lowered. Experimental results of real applications exhibit that the proposed hybrid fuzzy-Kalman filter can achieve lower prediction error and smaller hardware area when compared to previous workload predictors.