Showing papers on "Control theory published in 1991"

Stable adaptive neural network controller

Abstract: An adaptive control system uses a neural network to provide adaptive control when the plant is operating within a normal operating range, but shifts to other types of control as the plant operating conditions move outside of the normal operating range. The controller uses a structure which allows the neural network parameters to be determined from minimal information about plant structure and the neural network is trained on-line during normal plant operation. The resulting system can be proven to be stable over all possible conditions. Further, with the inventive techniques, the tracking accuracy can be controlled by appropriate network design.

Planning and Control

Abstract: "Planning and Control" explores planning and control by reformulating the two areas in a common control framework, developing the corresponding techniques side-by-side, and identifying opportunities for integrating their ideas and methods. This book is organized around the central roles of prediction, observation, and computation control. The first three chapters deal with predictive models of physical systems based on temporal logic and the differential calculus. Chapter 4 and 5 present some basic concepts in planning and control, including controllability, observability, stability, feedback control, task reduction, conditional plans, and the relationship between goals and preferences. Chapters 6 and 7 consider issues of uncertainty, covering state estimation and the Kalman filter, stochastic dynamic programming, probabilistic modeling, and graph-based decision models. The remaining chapters investigate selected topics in time-critical decision making, adaptive control, and hybrid control architectures. Throughout, the reader is led to consider critical tradeoffs involving the accuracy of prediction, the availability of information from observation, and the costs and benefits of computation in dynamic environments. This book is useful to researchers in artificial intelligence and control theory, and others concerned with the design of complex applications in robotics, automated manufacturing, and time-critical decision support.

Differential Inclusions and Optimal Control

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Performance evaluation of a six-axis generalized force-reflecting teleoperator

Abstract: Recent work in real-time distributed computation and control has culminated in a prototype force-reflecting telemanipulation system having dissimilar master (cable-driven force-reflecting hand controller) and slave (PUMA 560 robot with custom controller), extremely high sampling rate (1000 Hz), and low loop computation delay (5 ms). In a series of experiments with this system and five trained test operators covering more than 100 h of teleoperation, performance in a series of generic and application-driven tasks with and without force feedback was measured, and with control shared between teleoperation and local sensor referenced control. Measurements defining task performance include 100-Hz recording of six-axis force-torque information, task completion time, and visual observation of predefined task errors. It is shown that all performance measures improved as capability was added along a spectrum of capabilities ranging from pure position control through force-reflecting teleoperation and shared control. Performance was maximal for the barehanded operator. >

Adaptive PD controller for robot manipulators

Abstract: Referring to the point-to-point control problem, a proportional and derivative (PD) control algorithm is presented that is adaptive with respect to the gravity parameters of robot manipulators. The proposed controller is shown to be globally convergent. Following the same approach, an application to the tracking problem is also presented. Simulation tests are included, with reference to a robot having three degrees of freedom. These tests show that the performances of the adaptive PD controller are scarcely influenced by the initial error. >

Neural networks for self-learning control systems

Abstract: Neural networks can be used to solve highly nonlinear control problems. This paper shows how a neural network can learn of its own accord to control a non-linear dynamic system. An emulator, a multilayered neural network, learns to identify the system's dynamic characteristics. The controller, another multilayered neural network, next learns to control the emulator. The self-trained controller is then used to control the actual dynamic system. The learning process continues as the emulator and controller improve and track the physical process. An example is given to illustrate these ideas. The ‘truck backer-upper’, a neural network controller steering a trailer truck while backing up to a loading dock, is demonstrated. The controller is able to guide the truck to the dock from almost any initial position. The technique explored here should be applicable to a wide variety of non-linear control problems.

Progressive gaming control and communication system

Abstract: In order to provide a progressive gaming system with greater speed, flexibility and reliability a communication unit is used to control the information transmitted between a group of gaming machines and a progressive controller. The communication unit collects coin-in information from each of the gaming machines and transmits this information to the progressive controller in response to periodic poll signal signals from the progressive controller.

Longitudinal Vehicle Controller Design for IVHS Systems

Abstract: This paper describes a combined throttle/brake control algorithm designed to control intervehicle spacing within a fully automated "platoon" of vehicles. The control algorithm is developed using a modified sliding control method due to the inherent nonlinearities that exist in current automotive vehicles. Tne controller is designed using a simplified four state vehicle model and then simulated on a more complete nine state model. Simulations are shown for a two vehicle platoon and for a four vehicle platoon. The four vehicle platoon simulations illustrate the need for "feedforward" platoon information to eliminate disturbance amplification within the platoon. It is shown that communicating the lead vehicle's velocity and acceleration to all platoon vehicles is sufficient to prevent this amplification.

Effect of motor dynamics on nonlinear feedback robot arm control

Abstract: A nonlinear feedback robot controller that incorporates the robot manipulator dynamics and the robot joint motor dynamics is proposed. The manipulator dynamics and the motor dynamics are coupled to obtain a third-order-dynamic model, and differential geometry control theory is applied to produce a linearized and decoupled robot controller. The derived robot controller operates in the robot task space, thus eliminating the need for decomposition of motion commands into robot joint space commands. Computer simulations are performed to verify the feasibility of the proposed robot controller. The controller is further experimentally evaluated on the PUMA 560 robot arm. The experiments show that the proposed controller produces good trajectory tracking performances and is robust in the presence of model inaccuracies. Compared with a nonlinear feedback robot controller based on the manipulator dynamics only, the proposed robot controller yields conspicuously improved performance. >

Integrated mobile robot control

Abstract: This paper describes the structure, implementation, and operation of a real-time mobile robot controller which integrates capabilities such as: position estimation, path specification and tracking, human interfaces, fast communication, and multiple client support. The benefits of such high-level capabilities in a low-level controller was shown by its implementation for the Navlab autonomous vehicle. In addition, performance results from positioning and tracking systems are reported and analyzed.

A new adaptive learning rule

Abstract: A method is presented for nonlinear function identification and application to learning control. The control objective is to identify and compensate for a nonlinear disturbance function. The nonlinear disturbance function is represented as an integral of a predefined kernel function multiplied by an unknown influence function. Sufficient conditions for the existence of such a representation are provided. Similarly, the nonlinear function estimate is generated by an integral of the predefined kernel multiplied by an influence function estimate. Using the time history of the plant, the learning rule indirectly estimates the unknown function by updating the influence function estimate. It is shown that the estimate function converges to the actual disturbance asymptotically. Consequently, the controller achieves the disturbance cancellation asymptotically. The method is extended to repetitive control applications. It is applied to the control of robot manipulators. Simulation and actual real-time implementation results using the Berkeley/NSK robot arm show that the proposed learning algorithm is more robust and converges at a faster rate than conventional repetitive controllers. >

Infinite Horizon Optimal Control : Deterministic and Stochastic Systems

Abstract: This book presents a systematic account of the development of optimal control problems defined on an unbounded time interval - beginning primarily with the work of the early seventies to the present. The first five to six chapters provide an introduction to infinite horizon control theory and require only a minimal knowledge of mathematical control theory. The remainder of the book considers extensions of the previous chapters to a variety of control systems, including distributed parameter systems, stochastic control systems and hereditary systems. Throughout the book it is possible to distinguish three categories of research: the extension of the classical necessary conditions to various weaker types of optimality (eg, overtaking optimality); the discussion of various sufficient conditions and verification theorems for the various types of optimality and the discussion of existence theorems for the various types of optimality. The common link between these categories is the "turnpike property" and the notion of "reduction to finite costs". Once these properties are established for a given control system, it is possible to begin investigating the issues described in the above three categories. This monograph on economics, mathematics, systems engineering and operations research is intended for researchers.

Input-output stability of sampled-data systems

Abstract: The authors study the L/sub p/ input-output stability of a continuous-time controller, using the usual arrangement of periodic sampling and zero-order hold. It is noted that even if the hybrid system is exponentially stable, this arrangement does not yield L/sub p/ (1 >

Gaussian Networks for Direct Adaptive Control

Abstract: A direct adaptive tracking control architecture is proposed and evaluated for a class of continuous-time nonlinear dynamic systems for which an explicit linear parameterization of the uncertainty in the dynamics is either unknown or impossible. The architecture employs a network of gausian radial basis functions to adaptively compensate for the plant nonlinearities. Under mild assumptions about the degree of smoothness exhibited by the nonlinear functions, the algorithm is proven to be stable, with tracking errors converging to a neighborhood of zero. A constructive procedure is detailed, which directly translates the assumed smoothness properties of the nonlinearities involved into a specification of the network required to represent the plant to a chosen degree of accuracy. A stable weight adjustment mechanism is then determined using Lyapunov theory. The network construction and performance of the resulting controller are illustrated through simulations with an example system.

Reconfigurable flight control via multiple model adaptive control methods

Abstract: An aircraft flight control system with reconfigurable capabilities is considered. A multiple model adaptive controller (MMAC) is shown to provide effective reconfigurability when subjected to single and double failures of sensors and/or actuators. A command generator tracker/proportional-plus-integral/Kalman filter (CGT/PI/KF) form of controller was chosen for each of the elemental controllers within the MMAC algorithm and each was designed via LQG synthesis to provide desirable vehicle behavior for a particular failure status of sensors and actuators. The MMAC performance is enhanced by an alternate computation of the MMAC hypothesis probabilities, use of maximum a posteriori probability (MAP) versus Bayesian form of the MAC (or a modified combination of both), and reduction of identification ambiguities through scalar residual monitoring for the case of sensor failures. >

Position control of a PM stepper motor by exact linearization

Abstract: The authors consider the position control of a permanent magnet (PM) stepper motor using the exact linearization method. This nonlinear controller takes into account the full dynamics of the stepper motor. In particular, the phase shift between voltage and current in each phase is automatically taken into account. The feedback linearization controller makes the stepper motor into a fast accurate positioning system. The authors consider the feedback linearization technique for the PM stepper motor and show, when the detent torque is neglected, how it quite naturally leads to the well-known DQ transformation of electric machine theory. The authors indicate how constant load torques may be asymptotically rejected by using a nonlinear observer. >

Comparative experiments with a new adaptive controller for robot arms

Abstract: A model-based adaptive controller and proof of its global asymptotic stability with respect to the standard rigid-body model of robot-arm dynamics are presented. Experimental data from a study of one new and several established globally asymptotically stable adaptive controllers on two very different robot arms: (1) demonstrate the superior tracking performance afforded by the model-based algorithms over conventional PD control; (2) demonstrate and compare the superior performance of adaptive model-based algorithms over their nonadaptive counterparts; (3) reconcile several previous contrasting empirical studies; and (4) examine contexts that compromise their advantage. >

Disk array system

Abstract: A method and apparatus for controlling data flow between a computer and a group of memory devices arranged in a particular logical configuration. The system includes a group of first level controllers and a group of second level controllers. The first level controllers and the second level controllers work together such that if one of the second level controllers fails, the routing between the first level controllers and the memory devices is switched to a properly functioning second level controller without the need to involve the computer in the rerouting process. The logical configuration of the memory devices remains constant. The invention also includes switching circuitry which permits a functioning second level controller to assume control of a group of memory devices formerly primarily controlled by the failed second level controller. In addition, the invention provides error check and correction as well as mass storage device configuration circuitry.

Method and apparatus for adaptive control

Abstract: The present invention provides a method and apparatus for adaptive control which does not require a predetermined model of the process to be controlled. The method comprises the steps of 1) setting an initial state vector; 2) setting an initial parameter vector; 3) setting an initial prediction parameter gain between 2 and 10; 4) set the initial covariance matrix; 5) performing a state update 6) estimating the model error; 7) updating the parameter vector, 8) updating the co-variance matrix, and 9) updating the controller output.

Trajectory control of robotic manipulators using neural networks

Abstract: The authors present a nonlinear compensator using neural networks for trajectory control of robotic manipulators. The neural networks are not used to learn inverse-dynamics but to compensate nonlinearities of robotic manipulators. The performance of the proposed neural network controller is compared with that of the adaptive controller proposed by J.J. Craig (1988), and the effectiveness of the proposed neural network controller in compensating the unstructured uncertainties is clarified. A learning scheme using a model of known dynamics of manipulators is also proposed. The model learning can be done offline and needs no data recording of actual manipulator operation. >

Neural network/expert system process control system and method

Abstract: A neural network/expert system process control system and method combines the decision-making capabilities of expert systems with the predictive capabilities of neural networks for improved process control. Neural networks provide predictions of measurements which are difficult to make, or supervisory or regulatory control changes which are difficult to implement using classical control techniques. Expert systems make decisions automatically based on knowledge which is well-known and can be expressed in rules or other knowledge representation forms. Sensor and laboratory data is effectively used. In one approach, the output data from the neural network can be used by the controller in controlling the process, and the expert system can make a decision using sensor or lab data to control the controller(s). In another approach, the output data of the neural network can be used by the expert system in making its decision, and control of the process carried out using lab or sensor data. In another approach, the output data can be used both to control the process and to make decisions.

Real-time vision feedback for servoing robotic manipulator with self-tuning controller

Abstract: The visual information obtained from a camera and an image processing unit are incorporated in an adaptive control algorithm to make a robotic manipulator grasp a moving object. Because of the inherent time delay caused by the image processing, the motion of the moving target is predicted in real time and is used in the online planning of the trajectory for the manipulator motion. Since the dynamics of the target are assumed to be unknown, the prediction is accomplished by means of an autoregressive discrete-time model. On the basis of the predicted motion of the object, the planner determines online at each control sampling instant the desired trajectory point (subgoal) for the controller. The subgoal point is tracked by controlling the end effector with self-tuner until grasping occurs. A simulation study and laboratory experiments are presented to demonstrate the performance of this visual feedback system. >

Automatic impedance matching apparatus and method

Abstract: An automatic impedance matching apparatus for matching an RF-signal generator to a load, such as a plasma etching chamber, is disclosed. The matching apparatus comprises a matching network having two variable impedance devices, a tune detector for detecting the condition of the impedance match between the RF-signal and the load, and a controller for modifying the values of the variable impedance components in response to the measured tune condition. The present invention disclosed improve reset and convergence unit and eliminates the need for the "dead-band" provided around the matching point found in prior art impedance matching controllers. Also disclosed is an improved adjustment unit for adjusting the variable impedance components which is faster and more stable than found in prior art controllers. Also disclosed is a normalization unit for normalizing the input detection siganls such that variations in turning performance due to variations in input power level from the source are substantially reduced.

H ∞ control of linear time-varying systems: a state-space approach

Abstract: In this paper the standard problem of $H^\infty $ control theory for finite-dimensional linear time-varying continuous-time plants is considered. The problem is: given a real number $\gamma > 0$, f...

Arterial CO2 Monitor and closed loop controller

Abstract: The arterial CO2 monitor and closed loop controller for use with a ventilator monitors a patient's breath and determines PaCO2 based upon a determination of a deadspace ratio, which is the ratio of the alveolar deadspace to alveolar tidal volume. The method generally comprises the steps of continuously monitoring measurable parameters of a patient's breath; obtaining an input value of PaCO2 from a blood sample of the patient and using the patient's breath parameters and the input value to calculate the deadspace ratio; and continuously determining PaCO2 based on the assumption that the deadspace ratio subsequently remains constant. Decision rules obtained from other measurable data are preferably also used to identify the onset of changes in the deadspace ratio, and a new deadspace ratio is then determined from the patient's breath parameters and further input value of PaCO2 from the patient's blood sample.

Adaptive nonlinear control of systems containing a deadzone

Abstract: The authors present an adaptive nonlinear controller for a plant containing a nonsmooth nonlinearity with unknown parameters in the span of the control (specifically, a deadzone). They develop a Lyapunov-based adaptation scheme which updates the parameters of the specific region of the deadzone in which the system is operating. They then employ some switching logic to turn on and off the adaptation as the state of the system proceeds from one region of the deadzone to another. A significant feature of the adaptive controller is the presence of set uncertainty. Thus, conceptually, adaptation is not only for unknown parameters, but also for the regions of the piecewise smooth nonlinearity. >