Self-adjusting Control Research Articles

Analysis and control/monitoring of the direct linear drive in end milling

To meet the requirements of higher speeds, greater accuracies and improved reliability, direct linear drives are increasingly employed in new machine tools. In this paper, the modelling, control and monitoring of a direct linear drive for end milling are studied. First, a direct linear drive and its advantage are addressed; then, a dynamical model of the direct drive for end milling is proposed. Second, the friction and damping characteristic of the guideways and its relation to axis velocity and temperature as well as the cogging force of the drive resulting from the interaction of the primary (active) and secondary (passive or stator) part are analysed in detail. Third, possibilities to improve the motion behaviour of the direct linear drive by compensation of disturbances, friction and cogging forces are discussed, and a self-adjusting compensation controller and a state-space controller are proposed. Finally, a practical case demonstrates that the high-frequency cutting force can be tracked by the linear motor current. Then a tool breakage monitoring method by sensing linear motor current with a smoothing non-linear energy operator is proposed and tested with the practical case.

A Sensor-Based Robot Controller for Autonomous Exploration of Unknown Environments

Abstract This paper describes an oscillator-based controller that was implemented on a two-wheeled, differential drive robot. The controller is loosely based on central pattern generator circuits seen in many animals, and was developed to operate in unknown or changing environments. A unique feature is that the controller adjusts its oscillator parameters based on the pattern of sensory feedback. The controller's performance is evaluated in three different test arenas, each with three variable lighting patterns. The goal of the robot was to seek out areas of bright light and “collect” as much light as possible during a trial. The results indicate that the performance of the self-adjusting oscillator-based controller exceeds that of afixed-oscillator controller, with the performance difference increasing as the complexity of the environment increases.

A self-adjusting active compliance controller for multiple robots handling an object

Abstract This paper presents the concept and experimental validation of a self-adjusting active compliance controller for n robots handling its compliant behaviour concerning partly unknown flexible object. The control strategy is based on the decomposition of the 6 n -dimensional position/force space and includes a feedforward and feedback level. The feedforward level contains motion coordination, force distribution of external forces, creation of internal forces, and an additional loop adding the elastic displacements due to the applied forces to the planned robot positions. The feedback level is organized in the form of an active compliance control law. For adjusting the controller to the, in general, unknown flexible behaviour, which in practice is the main problem of the controller design, a quasi-static model of the system is derived for different contact cases of the object and a procedure is presented, which by use of this model is capable of determining the compliance of the considered system and therefore of adjusting the controller. Experiments with two puma-type robots have been conducted to show the applicability of the self-adjusting control strategy. The task has been to grasp and move an unconstrained object. It is shown, that the system can adjust the control parameters to the unknown system compliance and that the control performance is improved considerably.

Artificial neural network and fuzzy logic controller for GTAW modeling and control

An artificial neural network(ANN) and a self-adjusting fuzzy logic controller(FLC) for modeling and control of gas tungsten arc welding(GTAW) process are presented. The discussion is mainly focused on the modeling and control of the weld pool depth with ANN and the intelligent control for weld seam tracking with FLC. The proposed neural network can produce highly complex nonlinear multi-variable model of the GTAW process that offers the accurate prediction of welding penetration depth. A self-adjusting fuzzy controller used for seam tracking adjusts the control parameters on-line automatically according to the tracking errors so that the torch position can be controlled accurately.

Cartesian Control of Robots with Working-Position Dependent Dynamics

This paper addresses the problem of Cartesian control of robots with joints having different rise-times. This leads to working-position dependent dynamics and a coupling of the Cartesian degrees of freedom. A self-adjusting controller is presented which is able to deal with the nonlinear dynamics of the robot. Experimental results show the advantages of the chosen control-law.

Experimental Validation of a Self-Adjusting Active Compliance Controller for Multiple Robots Handling an Object

This paper presents the experimental validation of a self-adjusting active compliance controller for n robots handling a concerning its compliant behaviour partly unknown flexible object. The control strategy is based on the decomposition of the 6n-dimensional position/force space and includes a feedforward and feedback level. For adjusting the controller to the in general unknown flexible behaviour, which is the main problem of the controller design. a quasi-static model of the system is derived for different contact cases of the object and a procedure is presented, which by use of this model is capable of determining the compliance of the considered system and therefore of adjusting the controller. Experiments with two pumatype robots show the applicability of the self-adjusting control strategy.

On Delphi lemmas and other memoing techniques for deterministic logic programs

Three orthogonal memoing techniques for deterministic logic programs are introduced and evaluated on the basis of their empirical performance. They share the same basic idea: efficient memoing is achieved by losing information gracefully, i.e., memoing benefits from a form of abstraction. Abstract answers are most general computed answers of deterministic logic programs obtained through repeated applications of a simple clause composition operator. After describing a meta-interpreter returning abstract answers, we derive a class of program transformations that compute abstract answers more efficiently: they are ideal lemmas due to their goal-independent nature. For this reason, their “hit rate” is usually higher than in the case of conventional memoing. Indexing by structural properties of terms is an effective way to speed up the retrieval of lemmas, especially in the case of simple programs using linear recursion. Delphi lemmas add a self-adjusting control mechanism on the amount of memoing. Answers are memoized only by acquiescence of an oracle. We show that random oracles perform surprisingly well as Delphi lemmas tend naturally to cover the “hot spots” of the program.

Design of an extended Smith controller with gain adaptation

For the design of the benchmark test controller a combination of well-known techniques and an extension of the standard Smith. predictor controller is used. The selection of the design methods was governed by a practical point of view. All methods used as well as the optimisation procedure, should be usable with the tuning tools available to the service and commissioning staff in a “normal” DCS system. Black box approaches and the design of a self-adjusting controller have been avoided.

Generalized optimal active control algorithm with weighting matrix configuration, stability and time-delay

The paper presents a generalized optimal active control algorithm for earthquake-resistant structures. The study included the weighting matrix configuration, stability, and time-delays for achieving control effectiveness and optimum solution. The sensitivity of various time-delays in the optimal solution is investigated for which the stability regions are determined. A simplified method for reducing the influence of time-delay on dynamic response is proposed. Numerical examples illustrate that the proposed optimal control algorithm is advantageous over others currently in vogue. Its feedback control law is independent of the time increment, and its weighting matrix can be flexibly selected and adjusted at any time during the operation of the control system. The examples also show that the weighting matrix based on pole placement approach is superior to other weighting matrix configurations for its self-adjustable control effectiveness. Using the time-delay correction method can significantly reduce the influence of time-delays on both structural response and required control force.

Self-Adjusting Model Algorithmic Control of a Three-Phase Electric Arc Furnace

An advanced control strategy, based on the principles of Self-Adjusting Model Algorithmic Control (SAMAC), for regulating the energy input to a nonlinear electric arc furnace system is presented in this paper. The SAMAC strategy is an extension of Model Algorithmic Control (MAC) designed to accommodate the nonlinear and time-varying characteristics of the arc furnace process. Simulation results show that SAMAC yields an improved performance over both the traditional MAC strategy and a conventional analog control system (typical of many existing furnace installations) in terms of commonly used classical measures of response characteristics, including speed of response, settling time, overshoot, cumulative error, and sensitivity to variations in plant parameter values (robustness).

A model-based self-adjusting two-phase controller for vecuronium-induced muscle relaxation during anesthesia.

A two-phase controller was developed to induce and maintain muscle relaxation with vecuronium. A transient controller administers drug boluses according to a two-compartment pharmacokinetic, nonlinear pharmacodynamic model. Once relaxation is within ±10 percent of the setpoint, a self-adjusting PID controller maintains relaxation. The controller gain is initially identified during the transient phase by weighted recursive least squares. Thereafter, it is automatically adjusted to compensate for background noise and varying patient gain.

Decentralized Dynamic Control of Manipulation Robots

An approach to the synthesis of decentralized self-adjusting controller for manipulation robots is given in the paper. This controller is intended for serving trajectories defined in the space of manipulator generalized coordinates. The synthesis is carried out on a basis of a quadratic performance index. The high speed of controller adaptation to the parameter deviations is achieved due to the open-loop self-adjusting scheme. On-line algorithms for the identification of the payload mass, effective moments of inertia and damping coefficients reduced to the output shafts of gear trains of actuators are presented. Also the computational scheme for gravitational loads which is used for the control purposes is given in the paper. Simulation results are presented and discussed.

Design of Self-Adjusting Controllers

The control of temperature, pressure, flow and any of an array of other physical quantities is frequently realized by application of a proportional-plus-integral-plus-derivative (PID) controller. The values to which the three adjustable parameters of a PID controller are set are critical to the proper operation of the process being controlled. In order to maintain near optimal control of the process, the settings of the three parameters should be adjusted to reflect any substantial changes in process dynamics. An important aspect in the design of an adaptive PID controller is the continuing identification of the changing process. This paper considers the overall design of the adaptive controller with particular emphasis on process identification.

Invariance and Identifiability in Adaptive Coordinate - Parametric Control

The paper is concerned with control of plants whose dynamic features vary widely with time. Selfadjusting controllers are used in such plants. With broad ranges of dynamic responses, however, a selfadjusting controller may prove inadequate for control and purposeful modification of the plant design may prove necessary. The paper synthesizes algorithms for design of selfadjusting controllers and loops for change of the plant design parameters by using nonsearching selfadjusting model reference system; invariance and identifiability of the resultant structures are ascertained.

A self-adjusting computer process control system for chemical pulp refiners

In this ADAPT-SET system, which was put into real-time environment in June 1976, the control policy is to keep the “per unit” energy and the “specific edge load” which characterize the refining operation at their set-point values. The selection of operating points is less restricted using this method than using conventional methods where only the refiner power (load) is controlled. When the production quantity or quality is changed the refining using conventional methods may be changed only by modifying the distances between the disks or by connecting the refiner units in series or parallel. However, using APAPT-SET the desired refiner result is more accurately attained also during the above mentioned circumstances with the additional advantage that the energy consumption is less. Conventional methods use non-controlled asvnchronous motors which operate with an almost constant speed, while this method uses de motors whose speed may be regulated continuously.

Self-adjusting controllers for linear systems with correlated disturbances

An approach to the design of self-adjusting controllers is studied when the various disturbances affecting the controlled system are correlated both in time and with each other. The over-all control problem is separated into two problems: the optimum control problem and the prediction problem. The optimum control is formulated using dynamic programming and the prediction problem is solved with the help of Wiener-Masani predictor. An attempt is also made to obtain an adaptive control program when some of the statistical parameters of the process are unknown. The control is real-time, closed-loop, and may be carried out with the help of an “on-line” digital computer.

Design of self-adjusting control systems by use of a functional derivative technique

Publisher Summary This chapter describes the design of self-adjusting control systems by use of a functional derivative technique. Such systems are usually aimed at compensating drift in the controlled plant, often described by nonlinear differential equations. The compensation will be performed by a mechanism that receives measurements from the observed plant variables and also contains information about the control objective. In addition, owing to the drift, it is normally necessary to incorporate, in some way or another, an identification of the plant. As a rule, the mechanism needed for handling all this information and performing the optimization operations tends to become complex and awkward and impractical. The particular technique explored in the chapter leads to self-adjusting mechanisms whose main part can be implemented by solving differential equations only. There will be no need to store functions, or to invert matrices, and the implementation can be performed by analog means and with digital computers.

Optimized Feedback Control or Dead-Time Plants by Complementary Feedback

This paper deals with feedback control of controlled systems with dominant dead time. It is shown in which way the special difficulties arising with the presence of dead time in a controlled system can be overcome by means of so-called ``complementary feedback,'' so that an optimal control performance will be achieved. In addition, both the influence of unavoidable errors in the technical realization of this principle and the question of the best-suited type of controller will be discussed. Finally, several ways of realization and their adjustment are discussed, and a concept is given for a self-adjusting control in case the dead time of the controlled system is variable.

A comparison of the measuring time in self-adjusting control systems

It seems impossible to select the ‘best’ self-adjusting system out of the many proposals, but it is possible to compare them from a certain point of view. In this paper the point of view is the time required in measuring the parameters to be adjusted, considering random input signals. Three devices are compared which adjust one parameter of a control system in such a way that the R. M. S. error becomes a minimum. Instead of general derivations the comparison is demonstrated, using a very simple control system as an example. In each case the variance of the measuring unit output is estimated, this varies statistically because of the finite averaging time. From the variance the necessary time constants of the smoothing filter are determined. Results: From the three devices the ‘stepwise’ searching method requires long measuring intervals. The method with a ‘periodic searching signal’ is better because the search by trial and error is replaced by an adjustment directed uniquely to the minimum, but the measuring interval is in the same order of magnitude. The method with a ‘reference model’, however, has also the advantage of a shorter measuring time.

On self-adjusting control systems without test disturbance signals

The report presents and analyses one of the principles for designing an adaptive system to control time-varying processes. According to this principle, in the processes of self-adjusting, the dynamic characteristics of the system (plant and controller) and the effect of some uncontrolled external disturbances are taken into consideration. This controlled process is close, on the whole, to the predetermined (reference) behaviour in adjusting controller parameters. A high-speed computer, either a digital or an electronic analogue one, is supposed to be included in the loop of the adaptive control, depending upon the method of the process identification. Dynamic characteristics of the system are found by differential equations with time-constant coefficients, by means of which real and reference processes are approximated. In estimating reference values of controller coefficients, the sum of the squares of real coefficient value deviations from the reference ones is minimized. Mechanization problems of computing the above co-efficients by definite servos are discussed. The report deals with some particular applications of the given method for designing such an adaptive control system.