A closed-loop system for maintaining constant experimental musclepain in man

A microprocessor-based control system for maintaining a constant level of experimental muscle pain was developed. Pain was induced in the relaxed right masseter of healthy young adults by using an infusion pump to inject an algesic 0.15 mL bolus of 5% hypertonic saline over 15 s. Subjects supplied feedback on their present pain intensity (PPI) via a 10-cm-long electronic visual-analog scale (VAS) and a 0.07-Hz zero-order hold. The adaptive controller identified the system dynamic response and proportional-integral-derivative (PID) controller parameters from the subject's initial response to the bolus (pain rise and fall time constants and peak amplitude) as well as his/her response to a 90-s constant infusion. Using the pain feedback, the adaptive PID controller was successfully used to adjust the infusion rate to maintain PPI in five out of seven healthy adults at a mean (SD) 4.8(0.9) PPI level with respect to the 5.0 PPI setpoint for periods up to 15 min (when the experiment was arbitrarily terminated). The infusion rate required to maintain the given level of masseter pain was found to increase by approximately 3 to 5%/minute     

A fully on-line adaptive BCI

A viable fully on-line adaptive brain computer interface (BCI) is introduced. On-line experiments with nine naive and able-bodied subjects were carried out using a continuously adaptive BCI system. The data were analyzed and the viability of the system was studied. The BCI was based on motor imagery, the feature extraction was performed with an adaptive autoregressive model and the classifier used was an adaptive quadratic discriminant analysis. The classifier was on-line updated by an adaptive estimation of the information matrix (ADIM). The system was also able to provide continuous feedback to the subject. The success of the feedback was studied analyzing the error rate and mutual information of each session and this analysis showed a clear improvement of the subject's control of the BCI from session to session.     

An Adaptive Digital Pump Controller for Phase-Locked Servo Systems

A novel adaptive digital pump (phase/voltage) controller (ADPC) is presented for use in phase-locked servo systems without utilizing RC loop filters and high line-density encoders. The proposed ADPC is a multirate processor, of which the linear discrete-time model for the digital pump phase-locked servo system (DPPLS) is constructed. Simulation results that verify the theoretical work are also conducted.     

Automatic Continuous-Time Blood Pressure Control in Dogs by Means of Hypotensive Drug Injection

An automated drug administration system is proposed to control the decrease in the mean arterial blood pressure of dogs. It is accomplished by identifying a model for the open loop system consisting of a syringe pump and a dog. A linear feedback controller is then determined by solving an inverse optimum control problem. The chosen controller is optimal relative to a quadratic critexion. The controller is designed so that the mean arterial blood pressure is maintained at a desired level.     

雷达天线车电液比例泵控系统控制特性分析与仿真研究

通过对雷达天线车电液比例泵控系统控制特性的分析,设计了一种基于神经网络的自适应PID控制器,对系统的压力和流量进行复合控制,并利用AMESim仿真软件对该电液比例泵控系统进行了全数字化建模与仿真,仿真结果表明,所设计的复合控制器能较好地实现系统压力和流量的跟踪控制。     

非理想信道下Colpitts混沌电路的自适应同步研究

本文研究了Colpitts混沌电路在非理想信道条件下的自适应同步问题。引入自适应控制器对输入到响应部分（response system）的衰变混沌信号进行预处理，来补偿衰落信道对于同步性能的影响。给出了系统的数学模型，对恒定信道衰变和时变信道衰变两种非理想信道条件下的混沌ColpiRs电路的自适应同步进行了数值仿真研究。仿真结果表明，对于恒定或慢变化的时变信道衰变，自适应控制器可以有效的提高Colpitts电路响应部分和驱动部分（Drive system）的同步性能。     

Experimental Comparison of Digital Implementations of Single-Phase PFC Controllers

This paper presents the design and the digital implementation of three controllers for a single-phase power factor corrector (PFC). Based on an averaged system model, an adaptive nonlinear control strategy is first designed, followed by a digital redesign of the standard cascaded linear controller and a notch-filter-based variant. All three controllers have been verified via simulation in Simulink using a continuous time plant model and a discrete time controller. Real-time implementation is performed on an experimental testbed utilizing a rapid prototyping tool. The three controllers are experimentally compared for steady-state performance and transient response. It is shown that the nonlinear controller gives a better steady-state performance, whereas the linear strategy and the notch-filter-based variant have a faster dynamic response. Furthermore, although the notch-filter-based linear design shows promise in simulation, practical difficulties degrade its experimental performance. Performance metrics are tabulated for easy comparison.     

A model-based algorithm for blood glucose control in Type Idiabetic patients

A model-based-predictive control algorithm is developed to maintain normoglycemia in the Type I diabetic patient using a closed-loop insulin infusion pump. Utilizing compartmental modeling techniques, a fundamental model of the diabetic patient is constructed. The resulting nineteenth-order nonlinear pharmacokinetic-pharmacodynamic representation is used in controller synthesis. Linear identification of an input-output model from noisy patient data is performed by filtering the impulse-response coefficients via projection onto the Laguerre basis. A linear model predictive controller is developed using the identified step response model. Controller performance for unmeasured disturbance rejection (50 g oral glucose tolerance test) is examined. Glucose setpoint tracking performance is improved by designing a second controller which substitutes a more detailed internal model including state-estimation and a Kalman filter for the input-output representation The state-estimating controller maintains glucose within 15 mg/dl of the setpoint in the presence of measurement noise. Under noise-free conditions, the model based predictive controller using state estimation outperforms an internal model controller from literature (49.4% reduction in undershoot and 45.7% reduction in settling time). These results demonstrate the potential use of predictive algorithms for blood glucose control in an insulin infusion pump     

A new paradigm for the closed-loop intraoperative administration of analgesics in humans

We present a new paradigm for the closed-loop administration of analgesics during general anesthesia. The manipulated variable in the control system is the infusion rate of the opiate alfentanil, administered intravenously through a computer-controlled infusion pump (CCIP). The outputs to be controlled are the patient's mean arterial pressure (MAP) and the drug concentration in the plasma. Maintaining MAP within appropriate ranges provides optimal treatment of the patient's reactions to surgical stimuli. Maintaining plasma drug concentrations close to a reference value specified by the anesthesiologist allows to titrate analgesic administration to qualitative clinical end-points of insufficient analgesia. MAP is acquired invasively through a catheter cannula. Since plasma drug concentrations cannot be measured on-line, they are estimated via a pharmacokinetic model. We describe an explicit model-predictive controller which achieves the above-mentioned objectives. An upper constraint on drug concentrations is maintained to avoid overdosing. Constraints on the MAP are introduced to trigger a prompt controller reaction during hypertensive and hypotensive periods. Measurement artifacts in the MAP signal are rejected to prevent harmful misbehavior of the controller. We discuss the results of the clinical validation of the controller on humans.     

Position regulation in Cartesian space of a class of inextensible soft continuum manipulators with pneumatic actuation

This work investigates the position regulation in Cartesian space of a class of inextensible soft continuum manipulators with pneumatic actuation subject to model uncertainties and to unknown external disturbances that act on the tip. Soft continuum manipulators are characterised by high structural compliance which results in a large number of degrees-of-freedom, only a subset of which can be actuated independently or instrumented with sensors. External disturbances, which are common in many applications, result in uncertain dynamics and in uncertain kinematics thus making the control problem particularly challenging. We have investigated the use of integral action to model the uncertain kinematics of the manipulators, and we have designed a new control law to achieve position regulation in Cartesian space by employing a port-Hamiltonian formulation and a passivity-based approach. In addition, we have compared two adaptive laws that compensate the effects of the external disturbances on the system dynamics. Local stability conditions are discussed with a Lyapunov approach and are related to the controller parameters. The performance of the controller is demonstrated by means of simulations and experiments with two different prototypes.     

A novel dual-axis repulsive Maglev guiding system with permanent magnet: modeling and controller design

In this paper, we extend our previous result (1999, 2000) on designing a single-axis Maglev guiding system to a more involved task of designing a novel dual-axis positioning system. First, important issues related to construction of the mechanism of the dual-axis positioning system are addressed. Then, the dynamics of the dual-axis Maglev guiding system are analyzed. According to the derived analytic model, which is subject to unknown system parameters, an adaptive controller that can control the carrier at the desired target point of each axis with full alignment is presented. From the experiment results, a good performance in terms of regulation of the guiding-axis and tracking of the positioning-axis is achieved. This validates the design of the system hardware and demonstrates the feasibility of the developed controller.     

Design and implementation of an adaptive neural-network compensatorfor control systems

Recently, many studies have been made for intelligent controls using the neural-network (NN). These NN approaches for control strategies are based on the concept of replacing the conventional controller with a new NN controller. However, it is usually difficult and unreliable to replace the factory-installed controller with another controller in the workplace. In this case, it is desirable to install an additional outer control loop around the conventional control system to compensate for the control error of the preinstalled conventional control system. This paper presents an adaptive NN compensator for the outer loop to compensate for the control errors of conventional control systems. The proposed adaptive NN compensator generates a new command signal to the conventional control system using the control error that is the difference between the desired reference input and the actual system response. The proposed NN-compensated control system is adaptable to the environment changes and is more robust than the conventional control systems. Experimental results for a SCARA-type manipulator show that the proposed adaptive NN compensator enables the conventional control system to have precise control performance     

Adaptive neural network feedback control of a passive line-of-sight stabilization system

An adaptive neural network full-state feedback controller has been designed and applied to the passive line-of-sight (LOS) stabilization system. Model reference adaptive control (MRAC) is well established for linear systems. However, this method cannot be utilized directly since the LOS system is nonlinear in nature. Utilizing the universal approximation property of neural networks, an adaptive neural network controller is presented by generalizing the model reference adaptive control technique, in which the gains of the controller are approximated by neural networks. This removes the requirement of linearizing the dynamics of the system, and the stability properties of the closed-loop system can be guaranteed.     

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)     

A controller for regulation of mean arterial blood pressure usingoptimum nitroprusside infusion rate

Automation of drug delivery for control of mean arterial blood pressure is highly desirable in a number of clinical applications. An integrating self-tuning control strategy for control of mean arterial blood pressure using sodium nitroprusside is presented. Next to robust performance, the most attractive feature of the controller is its capability to optimize the quantity of infused medication without introducing a bias in the blood pressure level; a problem that existed in some of the other adaptive control strategies that have been proposed previously. Further, the controller design requires only the knowledge of the pure delay and the order of the transfer function describing the patient's response to the medication; it does not require that the entire transfer function be specified. The derivation of the controller is not based on the patient response to sodium nitroprusside; indeed, it is a general adaptive control strategy for control of systems with transport delay. The controller performs robustly in the presence of variations in the patient response and successfully controls the pressure at the desired level. The ability of this strategy to reduce the amount of infused medication makes it potentially attractive for use in clinical applications, as large doses or long term use of sodium nitroprusside can adversely affect central nervous system and hematopoietic tissues.     

A Decentralized Model Reference Adaptive Controller for Large-Scale Systems

A decentralized model reference adaptive controller (MRAC) for a class of large-scale systems with unmatched interconnections is developed in this paper. A novel reference model is proposed for the class of large-scale systems considered and a decentralized, full-state feedback adaptive controller is developed for each subsystem of the large-scale system. It is shown that with the proposed decentralized adaptive controller, the states of the subsystems can asymptotically track the desired reference trajectories. To substantiate the performance of the proposed controller, a large web processing line, which mimics most of the features of an industrial web process line, is considered for experimental study. Extensive experiments were conducted with the proposed decentralized adaptive controller and an often used decentralized industrial proportional-integral (PI) controller. A representative sample of the comparative experimental results is shown and discussed     

An electronic controller for adaptive suspension

Developing an adaptive suspension system for a luxury car weighing over 2000 kg presents an exacting challenge in embedded control. This article describes the development of an adaptive suspension controller by Lamerholm Fleming Ltd for Rolls-Royce Motor Cars Ltd. during the period from 1995 to 1997. The controller replaced an earlier design, which had originally entered production in 1989. The new system uses hydraulic dampers (shock absorbers) which can be switched between three different settings by means of two solenoid controlled valves. Each damper setting provides a different complex force/velocity response for bounce and rebound. These mechanical characteristics are determined by the precise magnitudes of springs and orifices within the damper. The function of the electronic controller is to select the optimum damper setting for any particular driving conditions and to choose the best time to switch from one setting to another. The aim of the whole suspension system is to provide the best possible compromise between ride, comfort and handling     

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     

Dynamical neural network organization of the visual pursuit system

The central nervous system is a parallel dynamical system which connects sensory input with motor output for the performance of visual tracking. This paper applies elementary control system tools to extend dynamical neural network models to the visual smooth pursuit system. Observed eye position responses to target motions and characteristics of the plant (eye muscles and orbital mechanics) place dynamical constraints on the interposed neural network controller. In the process of constructing a model for the controller, we show two previous pursuit system models, using efference copy and feedforward compensation, are equivalent from an input-output standpoint. We introduce a controller model possessing a potentially highly parallel implementation and offer an example with supporting neural firing rate data. Changes in time delays or other system dynamics are expected to lead to compensatory adaptive changes in the controller. A scheme to noninvasively simulate such changes in system dynamics was developed. Actual physiologic data of adaptive responses to increased time delay is presented as an example of the utility of this parallel controller. Compensatory changes in our parallel controller model are easily predicted. These results suggest a productive interaction between neural network modeling, neurophysiology, and control systems engineering.