Showing papers on "Control theory published in 1990"

Stable Adaptive Teleoperation

Abstract: Telerobotics, the body of science and technology which bridges human control and purely autonomous machines, is expected to be a merging point of modern developments in robotics, control theory, cognitive science, machine design, and computer science. Besides traditional applications in space, subsea, and handling of hazardous material, many new potential uses of advanced telerobotic systems have recently been suggested or explored, such as safety applications or microsurgery. This paper is a preliminary study of what role recent results in adaptive robot control and the understanding of time-delays may play in the development of effective telerobotics systems, which would best exploit the presence of the human operator while making full use of available robot control technology and computing power. The key paradigm it explores is that of simplifying, transforming, or enhancing the remote dynamics perceived by the operator by proper use of adaptive robot control techniques and tools from passivity theory.

Neural networks for self-learning control systems

Abstract: It is shown that a neural network can learn of its own accord to control a nonlinear 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 that steers a trailer truck while the truck is 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 should be applicable to a wide variety of nonlinear control problems. >

Positive position feedback control for large space structures

Abstract: A new technique for vibration suppression in large space structures is investigated in laboratory experiments on a thin cantilever beam. This technique, called Positive Position Feedback, makes use of generalized displacement measurements to accomplish vibration suppression. Several features of Positive Position Feedback make it attractive for the large space structure control environment: The realization of the controller is simple and straightforward. Global stability conditions can be derived which are independent of the dynamical characteristics of the structure being controlled, i.e., all spillover is stabilizing. The method cannot be destabilized by finite actuator dynamics, and the technique is amenable to a strain-based sensing approach. The experiments control the first six bending modes of a cantilever beam, and make use of piezoelectric materials for actuators and sensors, simulating a piezoelectric active-member. The modal damping ratios are increased by factors ranging from 2 to 130.

A permanent magnet motor drive without a shaft sensor

Abstract: A simple control for a permanent motor drive is described which provides a wide speed range without the use of a shaft sensor. Two line-to-line voltages and two stator currents are sensed and processed in analog form to produce the stator flux linkage space vector. The angle of this vector is then used in a microcontroller to produce the appropriate stator current command signals for the hysteresis current controller of the inverter so that near unity power factor can be achieved over a wide range of torque and speed. A speed signal is derived from the rate of change of angle of the flux linkage. A drift compensation program is proposed to avoid calculation errors in the determination of angle position and speed. The control system has been implemented on a 5 kW motor using Nd-Fe-B magnets. The closed loop speed control has been shown to be effective down to a frequency of less than 1 Hz, thus providing a wide range of speed control. An open loop starting program is used to accelerate the motor up to this limit frequency with minimum speed oscillation. >

Robot control by using only joint position measurements

Abstract: An observer for reconstructing the joint velocities of rigid-joint robots is proposed. The approach consists of exploiting the structural properties of the robot dynamics. The associated error dynamics are shown to be asymptotically stable. The observer furnishes the state estimate directly in the physical coordinates, so that no transformation is needed. The stability of some state feedback controllers having the proposed observer inserted in the feedback loop is proved. The structure of the observer and its convergence are shown. The stability of the whole system is analyzed when the observer is used in connection with a point-to-point controller and a trajectory controller. >

The influence of thruster dynamics on underwater vehicle behavior and their incorporation into control system design

Abstract: A nonlinear parametric model of a torque-controlled thruster is developed and experimentally confirmed. The model shows that the thruster behaves like a sluggish nonlinear filter, where the speed of response depends on the commanded thrust level. A quasi-linear analysis which utilizes describing functions shows that the dynamics of the thruster produce a strong bandwidth constraint and a limit cycle, which are both commonly seen in practice. Three forms of compensation are tested, utilizing a hybrid simulation combining an instrumented thruster with a real-time mathematical vehicle model. The first compensator, a linear lead network, is easy to implement and greatly improves performance over the uncompensated system, but does not perform uniformly over the entire operating range. The second compensator, which attempts to cancel the nonlinear effect of the thruster, is effective over the entire operating range but depends on an accurate thruster model. The final compensator, an adaptive sliding controller, is effective over the entire operating range and can compensate for uncertainties or the degradation of the thruster. >

A novel hysteresis control method for current-controlled voltage-source PWM inverters with constant modulation frequency

Abstract: A hysteresis control method for three-phase current-controlled voltage-source PWM inverters is presented. The method minimizes interference among phases, thus allowing phase-locked loop (PLL) control of the modulation frequency of inverter switches. The control theory is discussed, and the controller implementation is described. Design criteria are also given. The results of experimental tests show excellent static and dynamic performance. >

Foundations of robotics : analysis and control

Abstract: Foundations of Robotics presents the fundamental concepts and methodologies for the analysis, design, and control of robot manipulators It explains the physical meaning of the concepts and equations used, and it provides, in an intuitively clear way, the necessary background in kinetics, linear algebra, and control theory Illustrative examples appear throughoutThe author begins by discussing typical robot manipulator mechanisms and their controllers He then devotes three chapters to the analysis of robot manipulator mechanisms He covers the kinematics of robot manipulators, describing the motion of manipulator links and objects related to manipulation A chapter on dynamics includes the derivation of the dynamic equations of motion, their use for control and simulation and the identification of inertial parameters The final chapter develops the concept of manipulabilityThe second half focuses on the control of robot manipulators Various position-control algorithms that guide the manipulator's end effector along a desired trajectory are described Two typical methods used to control the contact force between the end effector and its environments are detailed For manipulators with redundant degrees of freedom, a technique to develop control algorithms for active utilization of the redundancy is described Appendixes give compact reviews of the function atan2, pseudo inverses, singular-value decomposition, and Lyapunov stability theoryTsuneo Yoshikawa teaches in the Division of Applied Systems Science in Kyoto University's Faculty of Engineering

Digital control of repetitive errors in disk drive systems

Abstract: Tracking errors in disk drive systems have a significant repetitive component that is not explicitly taken into account in conventional servo controllers. A modified prototype repetitive controller is applied as a plug-in module to a Winchester disk drive with a preexisting analog feedback controller to demonstrate its efficacy in the reduction of this periodic component. A review of the Winchester disk drive system is included. >

Adaptive sliding mode control of autonomous underwater vehicles in the dive plane

Abstract: The problem of controlling an autonomous underwater vehicle (AUV) in a diving maneuver is addressed. Having a simple controller which performs satisfactorily in the presence of dynamical uncertainties calls for a design using the sliding mode approach, based on a dominant linear model and bounds on the nonlinear perturbations of the dynamics. Nonadaptive and adaptive techniques are considered, leading to the design of robust controllers that can adjust to changing dynamics and operating conditions. The problem of using the observed state in the control design is addressed, leading to a sliding mode control system based on input-output signals in terms of drive-phase command and depth measurement. Numerical simulations using a full set of nonlinear equations of motion show the effectiveness of the proposed techniques. >

Learning control theory for robotic motion

Abstract: A new concept of learning control for the improvement of robot motions is proposed, which can be referred to a mathematical modelling of learning and generation of motor programmes in the central nervous system. It differs from conventional classical and modern control techniques. It stands for the repeatability of operating a given robotic system and the possibility of improving the command input on the basis of actual measurement data acquired at the previous operation. Hence adequate conditions on the repeatability and invariance of the system dynamics are assumed, but no precise description of the dynamics is required for construction of the learning algorithms. Two types of iterative learning algorithm are proposed: one uses a PD-type (proportional and differential) update of input commands and the other a PI-type (proportional and integral) update where velocity signals are regarded as outputs. It is shown that in both types a better performance is realized at every attempt of operation, provided a desired motion is given a priori and the actual motion (velocity signals) can be measured at every operation. Further, the robustness of such learning control algorithms with respect to the existence of perturbed errors of initialization of the robot, disturbances and measurement noise during operation is analysed in detail. It is shown that in PD-type learning laws such errors are neither amplified nor aggregated in later consecutive trials of operation. In the case of PI-type learning laws it is shown that such a robustness property is assured if a forgetting factor is adequately introduced into the repetitive learning law.

Incremental fuzzy expert PID control

Abstract: An approach to intelligent PID (proportional integral derivative) control of industrial systems which is based on the application of fuzzy logic is presented. This approach assumes that one has available nominal controller parameter settings through some classical tuning technique (Ziegler-Nichols, Kalman, etc.). By using an appropriate fuzzy matrix (similar to Macvicar-Whelan matrix), it is possible to determine small changes on these values during the system operation, and these lead to improved performance of the transient and steady behavior of the closed-loop system. This is achieved at the expense of some small extra computational effort, which can be very easily undertaken by a microprocessor. Several experimental results illustrate the improvements achieved. >

Robot manipulator control using decentralized linear time-invariant time-delayed joint controllers

Abstract: It is shown that robot manipulator control can be accomplished using simple decentralized linear time invariant time-delayed joint controllers instead of the complicated computed torque control scheme. This means that all the online computational problems associated with computing robot inverse dynamics can be avoided, and the robot control problem is essentially reduced to that of computing n linear proportional-derivation controls for n joint subsystems. The proposed control technique employs a time-delayed control with a specially designed constant diagonal gain matrix to decouple and linearize the robot joint dynamics so that linear centralized joint control can be achieved. It is shown that the proposed controller is stable, and the value of the special gain matrix can be selected based on a sufficient condition of stability presently developed. However, the establishment of this sufficient condition requires knowledge of the inertial matrix of the robot. It is also shown that the controller is robust in the presence of payload uncertainty. A two-link planar robot is presented to illustrate the controller design procedures and the performance of the controller. >

Neural network process measurement and control

Abstract: A computer neural network process measurement and control system and method uses real-time output data from a neural network to replace a sensor or laboratory input to a controller. The neural network can use readily available, inexpensive and reliable measurements from sensors as inputs, and produce predicted values of product properties as output data for input to the controller. The system and method overcome process deadtime, measurement deadtime, infrequent measurements, and measurement variability in laboratory data, thus providing improved control. An historical database can be used to provide a history of sensor and laboratory measurements to the neural network. The neural network can detect the appearance of new laboratory measurements in the history and automatically initiate retraining, on-line and in real-time. The system and method can use either a regulatory controller or a supervisory control architecture. A modular software implementation simplifies the building of multiple neural networks, and also optionally provides other control functions, such as supervisory controllers, expert systems, and statistical data filtering, thus allowing powerful extensions of the system and method. Template specification for the neural network, and data specification using data pointers allow the system and method to be more easily implemented.

Brushless DC motor control without position and speed sensors

Abstract: The control strategy using a DSP-TMS320C25 is developed on the basis of the instantaneous voltage equation of the brushless DC motor. Without a position sensor, the controller has no information about rotor position: therefore, the controller determines the applied voltage according to the hypothetical rotor position, which is not necessarily coincident with the actual rotor position. Since the voltage supplied through the inverter is the actual applied voltage, it can be measured. Under the ideal condition that the hypothetical rotor position is coincident with the actual rotor position, the ideal applied voltage can be calculated by using the instantaneous voltage equation of the motor and the detected current. The difference between the actual and the ideal voltages is proportional to the angular difference of the hypothetical and actual rotor positions. Therefore, self-synchronization is possible by reducing the angular difference to zero. Once the rotor position is estimated, the motor can be operated without position and speed sensors. >

Dynamic simulation of autonomous legged locomotion

Abstract: Accurate simulation of Newtonian mechanics is essential for simulating realistic motion of joined figures. Dynamic simulation requires, however, a large amount of computation when compared to kinematic methods, and the control of dynamic figures can be quite complex. We have implemented an efficient forward dynamic simulation algorithm for articulated figures which has a computational complexity linear in the number of joints. In addition, we present a strategy for the coordination of the locomotion of a six-legged figure - a simulated insect - which has two main components: a gait controller which sequences stepping, and motor programs which control motions of the figure by the application of forces. The simulation is capable of generating gait patterns and walking phenomena observed in nature, and our simulated insect can negotiate planar and uneven terrain in a realistic manner. The motor program techniques should be generally applicable to other control problems.

Robust Control for Automatic Steering

Abstract: Robust control problems in automatic steering are due to the wide range of velocity, mass and road conditions under which such vehicles operate. In earlier design studies and road tests for a bus it has been shown that it is possible to design a fixed gain controller such that the automatic steering operates satisfactorily over the entire range of parameters. In the present paper it is shown that the performance of such a robust automatic steering system can be considerably improved by the addition of a gyro measuring the yaw rate and feeding it back into the controller.

Process control method and system for performing control of a controlled system by use of a neural network

Abstract: A method for controlling a controlled system by a controller such that a controlled variable can be brought into conformity with a desired value. With respect to at least one of input/output variables for a combined controlling-controlled system, which includes in combination the controller and the controlled system, and input/output variables for the controlled system, information containing its characteristics is taken out from the combined controlling-controlled system. The information with the characteristics contained therein is inputted to a neural network which has been caused beforehand to learn a correlation between the information containing the characteristics and control parameters. From the neural network, one or more of the control parameters, said one or more control parameters corresponding to a corresponding number of inputs to the neural network, are outputted to the controller.

Synchronization of two motion control axes under adaptive feedforward control

Abstract: In this paper, motion synchronization of two d-c motors, or motion control axes, under adaptive feedforward control is considered. The adaptive feedforward control system for each axis consists of a proportional feedback controller, an adaptive disturbance compensator and an adaptive feedforward controller. If the two adaptive systems are left uncoupled, a disturbance input applied to one of the two axes will cause a motion error in the disturbed axis only, and the error becomes the synchronization error. To achieve a better synchronization, a coupling controller, which responds to the synchronization error, i.e., the difference between the two motion errors, is introduced. In this case, when a disturbance input is applied to one axis, the motion errors appear in the undisturbed axis as well as in the disturbed axis. The motion error in the undisturbed axis is introduced by the coupling controller and the adaptive feedforward controller. The adaptive synchronization problem is formulated and analyzed in the continuous time domain first, and then in the discrete time domain. Stability conditions are obtained. Effectiveness of the adaptive synchronization controller is demonstrated by simulation.

A framework for real-time discrete event control

Abstract: The TTM/RTTL (timed transition model with real-time temporal logic) framework is presented for modeling, specifying, and analyzing real-time discrete-event systems. TTMs are used to represent the process of the plant and its controller. RTTL is the assertion language for specifying plant behavior and verifying that a controller satisfies the specification. The framework adapts features from the program verification literature which are useful for posing problems of interest to the control engineer, such as modular synthesis and design. Examples are used to illustrate the ideas presented. The authors' published analytical results are summarized and referenced. >

Adaptive inverse control

Abstract: Adaptive control is seen as a two part problem control of plant dynamics and control of plant noise. The two parts are treated separately. An unknown plant will track an input command signal if the plant is driven by a controller whose transfer function approximates the inverse of the plant transfer function. An adaptive inverse identification process can be used to obtain a stable controller even if the plant is nonminimum phase. A model reference version of this idea allows system dynamics to closely approximate desired reference model dynamics. No direct feedback is used except that the plant output is monitored and utilized in order to adjust the paramters of the controller. Control of internal plant noise is accomplished with an optimal adaptive noise canceller. The canceller does not affect plant dynamics but feeds back plant noise in a way that minimizes plant output noise power. Key words. Adaptive control modeling identification inverse modeling noise cancelling deconvolution adaptive inverse control.© (1990) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

Control architecture for the Belgrade/USC hand

Abstract: This chapter describes design and control features of a five-fingered anthropomorphic end-effector designed primarily for grasping tasks Advantages and limitations of the design are discussed, and special emphasis is placed on its suitability for autonomous, non-numerical or reflex control of grasp Following a discussion of its mechanical design, we present the controller and sensor features incorporated into the current finger model A knowledge-based control of hand preshape (prior to grasping) is then outlined, and the hand’s suitability as a testbed for the study of human and robot hand motion control is discussed The final section of this chapter describes future directions

ATM communications network control by neural networks

Abstract: A learning method that uses neural networks for service quality control in the asynchronous transfer mode (ATM) communications network is described. Because the precise characteristics of the source traffic are not known and the service quality requirements change over time, building an efficient network controller which can control the network traffic is a difficult task. The proposed ATM network controller uses backpropagation neural networks for learning the relations between the offered traffic and service quality. The neural network is adaptive and easy to implement. A training data selection method called the leaky pattern table method is proposed to learn precise relations. The performance of the proposed controller is evaluated by simulation of basic call admission models. >

Nonlinear controllers for non-integrable systems: the Acrobot example

Abstract: Recent developments in the theory of geometric nonlinear control provide powerful methods for controller design for a large class of nonlinear systems. Many systems, however, do not satisfy the restrictive conditions necessary for either full state linearization [6, 5] or input-output linearization with internal stability [2]. In this paper, we present an approach to controller design based on finding a linearizable nonlinear system that well approximates the true system over a desirable region. We outline an engineering procedure for constructing the approximating nonlinear system given the true system. We demonstrate this approach by designing a nonlinear controller for a simple mechanical system patterned after a gymnast performing on a single parallel bar.

Automatic image stabilizing system by full-digital signal processing

Abstract: An automatic image-stabilizing system for camcorders and VCRs utilizing only digital signal processing has been developed New technologies for this system are (1) the BERP (band extract representative point) matching technique with a small-scale circuit, (2) an adaptive system control algorithm to discriminate moving objects, and (3) suppression of motion vectors due to noise Calculations show that the motion vector detector requires only 11000 gates, the electronic zoom controller requires only 8500 gates, and only one 8-b field memory is required >

Geometric methods for nonlinear process control. 2. Controller synthesis

Abstract: We focuse on exact linearization methods including Su-Hunt-Meyer, input/output, and full linearization. The internal model control (IMC) and globally linearizing control (GMC) structures are reviewed and interpreted in the context of input/output linearization. Further topics of current research interest are also identified