Showing papers on "Control variable published in 1990"

A general stochastic maximum principle for optimal control problems

Abstract: The maximum principle for nonlinear stochastic optimal control problems in the general case is proved. The control domain need not be convex, and the diffusion coefficient can contain a control variable.

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.

Method and apparatus for process control

Abstract: A statistical process control system provides asymmetrical nonlinear automatic closed-loop feedback control. The system has application, for example, in the control of equipment that responds to a controlled variable signal to vary a measurable characteristic of a process. The system uses an accumulated deviation of a measured subgroup means minus a target value, divided by the subgroup standard deviation, to modify the manipulated variable each time the accumulated value exceeds a decision interval above or below the target. The system permits the use of independent slack variables subtracted from the accumulated deviation to more closely model the underlying process. Likewise, the system permits the use of independent alarm values and variable gains to permit greater control of the process.

An optimal control problem with a random stopping time

Abstract: This paper deals with a stochastic optimal control problem where the randomness is essentially concentrated in the stopping time terminating the process. If the stopping time is characterized by an intensity depending on the state and control variables, one can reformulate the problem equivalently as an infinite-horizon optimal control problem. Applying dynamic programming and minimum principle techniques to this associated deterministic control problem yields specific optimality conditions for the original stochastic control problem. It is also possible to characterize extremal steady states. The model is illustrated by an example related to the economics of technological innovation.

Robustness Improvement of Actively Controlled Structures through Structural Modifications

Abstract: The parameter variations introduced by the analysis model, uncertain material properties, or optimization may adversely influence the stability and performance characteristics of a closed-loop controlled structure. The improvement of robustness of actively controlled structures through structural modifications is considered in this work. The stability and performance robustness indices are defined as measures of robustness of actively controlled structures. The integrated structural/control design problem is considered as a multiobjective optimization problem, in which three objectives—structural weight, stability robustness index, and performance robustness index—are considered for minimization. The utility function, lexicographic, and goal programming methods are applied to solve the multiobjective nonlinear programming problem. Two examples, a two-bar truss and two-bay truss, are considered to demonstrate the procedure. HERE has been a dramatic increase in the past decade in the use of active control systems to improve structural performance.1'2 The major challenge in the field of active control of structures is in the design of control systems for very large space structures. These structures are by nature distributed parameter systems with multiple inputs (controls) and a continuum of outputs (displacements). The finite-ele- ment method is commonly used for the description of these structures. This is a source of parameter errors and truncated (or reduced order) models in the system. In addition, the structural properties of large space structures cannot be tested before they are put into orbit and, hence, sizeable uncertain- ties exist in modal parameters. A great deal of research is currently in progress on develop- ing methods for the simultaneous (integrated) design of the structure and the control system. The weight of the structure was minimized, with constraints on the distribution of the eigenvalues and/or damping ratio of the closed-loop system by Khot et al.3 Miller and Shim4 considered the simultaneous minimization, in structural and control variables, of the sum of structural weight and the infinite horizon linear regulator quadratic control cost. The structure/control system optimiza- tion problem was formulated by Khot et al.,5 with constraints on the closed-loop eigenvalue distribution and the minimum Frobenious norm of the control gains. It can be seen that in all the above works the consideration of robustness of the control system has been ignored. The parameter variations introduced by the analysis model, uncertain material properties, or optimization may adversely influence the stability and performance characteristics of the control system. The robustness is an extremely important feature of a feedback control design. A robust control design is one that satisfactorily meets the system specifications, even in the presence of parameter uncertainties and other modeling errors. Since the system specifications could be in terms of

New general guidance method in constrained optimal control, part 2: application to space shuttle guidance

Abstract: The application and the performance of the neighboring optimal feedback scheme presented in Part 1 of this paper is demonstrated for the heating-constrained cross-range maximization problem of a space-shuttle-orbiter-type vehicle This problem contains five state variables, two control variables, and a state variable inequality constraint of order zero

Supporting neural network method for process operation

Abstract: A method and a system for causing a neural circuit model to learn typical past control results of a process and using the neural circuit model for supporting an operation of the process. The neural circuit model is caused to learn by using, as input signals, a typical pattern of values of input variables at different points in time and, as a teacher signal, its corresponding values of the control variable. An unlearned pattern of input variables is inputted to the thus-learned neuron circuit model, whereby a corresponding value of the control variable is determined. Preferably, plural patterns at given time intervals can be simultaneously used as patterns to be learned.

Existence theorems in optimal control problems without convexity assumptions

Abstract: We give existence theorems of solutions for Lagrange and Bolza problems of optimal control. These results are obtained without convexity assumptions on the cost function with respect to the control variable. We extend a Cesari's theorem to cost functions which are nonlinear with respect to the space variable and to problems which are governed by a differential inclusion. Moreover, we consider the case where the control variable belongs to a space of measurable functions and the case where this variable belongs to a Lebesgue space.

Process control apparatus and method for adjustment of operating parameters of controller of the process control apparatus

Abstract: A process control apparatus and a process control system for observing the waveform of the deviation between a set point which controls the process in a control loop and the process controlled variable transmitted from the process so that the operating parameters are adjusted in accordance with a predetermined adjustment rule by using the deviation based upon the absolute value of a wave for every half cycle of the wave. Therefore, even if the process controlled variable is influenced, the area of the wave cannot be changed significantly. As a result, the operating parameters can be adjusted correctly, causing the process controlled variable to quickly coincide with the set point.

Compound control method for controlling a system

Abstract: A compound control method involves Proportional Plus Integral Plus Derivative Control (PID control) and Fuzzy Control. The method controls a system by inputting data obtained from the system to be controlled to a PID control operation so as to obtain a control variable from the PID control operation, inputting the data obtained from the system to a fuzzy operation so as to obtain a correcting variable from the fuzzy operation, correcting the control variable by the correcting variable so as to obtain a corrected control variable, and inputting the corrected control variable to the system to control the system.

Method and system for acquiring parameters in process control

Abstract: In a process control system prior to execution of process control, a manipulated variable to which an identification signal from an identification signal generator is added is inputted to a process In a preadaptation section, a controlled variable output from the process is sampled to obtain its dead time and maxmum gradient, and the initial values of PID control parameters are calculated on the basis of the dead time and the maximum gradient The initial values of PID control parameters and the like are set in the adaptation section In the adaptation section, a pulse transfer function of the process is acquired, and PID control parameters are calculated from the acquired pulse transfer function by using a partial matching method in a frequency region After these PID control parameters are set in a control section, process control is executed

Multirate self-tuning predictive control with application to binary distillation column

Abstract: An adaptive controller for multi-input multi-output (MIMO) systems is presented, which is able to modify repeatedly the control variables between successive output measurements. This feature makes the proposed approach particularly appealing for the control of composition products of distillation columns when expensive and slow chromatographs are used. The control algorithm is derived by minimizing the predicted error signal, as well as the control increments, over a future control horizon which is suitably selected. Applications to a simulated non-linear model of a binary distillation column demonstrate the effectiveness of the proposed method.

Method and system for changing control parameters in accordance with state of process in process control

Abstract: A determination and control section of a process control system samples a set value, a difference value, a manipulated variable, and a controlled variable to determine a state of a process. When it is determined that the process is in an unstable state, an adaptive unit estimates a dynamic characteristic of the process, and calculate PID control parameters using a partial model matching method in a frequency region. When it is determined that the process is in an insufficient response state, the PID control parameter is regulated without operating the adaptive unit to decrease the gain of the process control system.

Supporting method and system for process operation

Abstract: A method for causing a neural circuit model to learn typical past control results of a process and using the neural circuit model for supporting an operation of the process. The neural circuit model is caused to learn by using, as input signals, a typical pattern of values of input variables at different points in time and, as a teacher signal, its corresponding values of the control variable. An unlearned pattern of input variables is inputted to the thus-learned neuron circuit model, whereby a corresponding value of the control variable is determined. Preferably, plural patterns at given time intervals can be simultaneously used as patterns to be learned.

Continuous deadbeat control system with feedback control

Abstract: A control computation unit includes a delay element in addition to an integrating element and a proportional element. The delay time of this delay element is selected, following the time necessary for due settling (namely, the time required for rendering a controlled variable to follow changes in a control variable command), or the time necessary to restore the controlled variable to the control variable command when a controlled object involved has incurred a certain external disturbance. In case there exist a computation delay time, a dead time, and/or a detection delay time, respectively in the control computation unit, controlled system, and/or a control variable detector, then the delay time is selected equal to one of the computation delay time, dead time, and the detection delay time, or the sum of these times. Additional provision of the delay element with a delay time equal to the time desired for due settling to the control computation unit results in settling within the time equal to the time desired for due settling the response.

Variable speed driver

Abstract: PURPOSE:To prevent the overvoltage puncture through imbalance of a three- phase inverter by equalizing the outputs of two inverters both in case of a variable voltage and variable frequency mode and in case of constant voltage and variable frequency CONSTITUTION:In case of variable voltage and variable frequency, because an input power P2 to a three-phase induction motor 8 decreases after the operation of a difference volume detection circuit 13, an input current I2 to a three- phase inverter decreases and the terminal voltage EC2 of a filter capacitor 5 rises Said operation continues so far as the circuit 13 detects a difference voltage E so that the balance is recovered at the time of EC1=EC2 In case of constant voltage and variable frequency, further, contacts S1, S2 are switched to B side and the difference voltage E is given as a controlled variable to chopper circuits 17, 18 by a command from a control mode determination circuit 21 to a switch circuit 22 In case of the difference voltage E>0, the electric charge of a capacitor 4 is discharge through resistance 19 and the circuit 17 is turned OFF at the time of EC1=EC2 In case of E<0, the charge of the capacitor 5 is discharged until EC1=EC2

Model prediction controller

Abstract: PURPOSE: To start a controller in a short time by providing an evaluation function parameter control means which calculates a stable allowance from the frequency response of a open-loop transfer function in a control system and which decides a stable allowance condition so that it is satisfied. CONSTITUTION: In the control system 11, a manipulated variable (u) for making the controlled variable (y) of a plant 13 to follow a target value is calculated by a model prediction controller 17. A test signal generation means 25 generates a test signal and inputs it to the plant 13. The manipulated variable (u) and the controlled variable (y) are inputted to a dynamice characteristic model estimation means 23 and a dynamice characteirstic model is estimated. The evaluation function parameter control means 2 decides the parameter of an appropriate function from the stable allowance condition which a stable allowance setting means 19 sets and the control constant of the model prediction controller 17 is adjuted. The control system always hold sufficient stability with such constitution and model prediction control persistent to a model difference can be executed. COPYRIGHT: (C)1992,JPO&Japio

Optimal Placement of Actuators and Sensors in Control Augmented Structural Optimization

Abstract: A control-augmented structural synthesis methodology is presented in which actuator and sensor placement is treated in terms of (0,1) variables. Structural member sizes and control variables are treated simultaneously as design variables. A multiobjective utopian approach is used to obtain a compromise solution for inherently conflicting objective functions such as strucutal mass control effort and number of actuators. Constraints are imposed on transient displacements, natural frequencies, actuator forces and dynamic stability as well as controllability and observability of the system. The combinatorial aspects of the mixed - (0,1) continuous variable design optimization problem are made tractable by combining approximation concepts with branch and bound techniques. Some numerical results for example problems are presented to illustrate the efficacy of the design procedure set forth.

Control Augmented Structural Synthesis with Transient Response Constraints

Abstract: An integrated approach to the optimum design of control augmented structural systems is presented in which structural variables and control variables are changed simultaneously during the design process. Constraints are imposed on peak transient dynamic displacements and accelerations, static displacements, natural frequencies, and control system effort. Side constraints imposed on structural member sizes and control system thresholds and actuator output forces insure the generation of physically meaningful designs. Example problems are presented which bring out the benefits of simultaneous treatment of both the structural design variables and the control design variables.

Reply to Jacobson

Abstract: Gary C. Jacobson (1990) would have us believe that my findings pertaining to the economy and the midterm vote are contaminated by "collinearity." Professor Jacobson presents new equations, designed to avoid this "collinearity" problem. Jacobson claims that the results from his equations help to restore the case for an economic effect at midterm. Jacobson exaggerates the extent of our disagreement. Actually, no multicollinearity problem exists. Except for trivial differences in specification, Jacobson's equations are algebraically identical to mine. Consequently, Jacobson's coefficients and mine are very similar. The difference of interpretation regarding whether these coefficients are "statistically significant" boils down a choice between a one-tailed and two-tailed test. First, why is there no multicollinearity problem? Although Professor Jacobson is correct to note that many of the correlations among the variables are quite strong, such strong correlations among variables are often desirable. Jacobson is most disturbed by the very strong + .92 correlation between the vote at t (the dependent variable) and the t 2 vote (the crucial control variable). A strong correlation between the dependent and control variables is actually beneficial because it helps to reduce the standard error of the coefficient for the independent variable of interest. A control variable presents a multicollinearity problem only when it correlates so strongly with the independent variable of interest that the two cannot be disentangled. The t 2 vote as the control presents no such problem, since its correlation with per capita income growth (Tufte measure) is only +.44. In any event, the appropriate diagnostic test for whether a control variable adds undesirable levels of multicollinearity is whether the standard error of the regression coefficient for the independent variable of interest (here, per capita income growth) expands with the introduction of the control (here, the t 2 vote). An examination of my Tables 2, 4, and 6 shows that no such thing happens. The standard error for PCIC is a shade under .20 in all equations for 1946-86. The introduction of the t 2 vote results in an insignificant t-ratio for PCIC not because it inflates the denominator (the standard error) but rather because it shrinks the numerator, the coefficient for PCIC, to half or less of the size obtained using the Tufte specification. The essential feature of Jacobson's respecification is to reconfigure the dependent variable as a change measure, either as a deviation from the t 2 vote (and assuming no effect of the t 2 vote on change) or as a deviation from the

Process for controlling an actuator which can be adjusted by means of a proportional valve.

Abstract: In a process for controlling an actuator which can be adjusted by means of a proportional valve, the proportional valve is controllable by means of two control magnets with control pulses acting in opposite directions. A phase angle between the control pulses is varied as a function of at least one control variable (command variable, correction variable or difference between the amplitudes or pulse durations of the control pulses), i.e. it is reduced as the control variable increases. This measure prevents undesired vibrations of the actuator and resulting noises.

Control method for earth-moving machines

Abstract: The invention relates to a control method for earth-moving machines, such as bucket wheel excavators (5), gravel-bed receivers, etc., in which the morphology, determined in advance, of a deposit or the stratification of embedded material serves as command variables for the movements of the earth-moving machine. The profile of the material to be conveyed is continuously scanned by a light beam (14), and the values determined by the scanning are used as a control variable for the movements of the earth-moving machine.

Supporting method and system for process control

Abstract: This invention discloses a method and a system of making a neuron network model learn the typical past process control results and utilizing this model for the supporting of an operation of a process. The neuron network is given a typical pattern of a plurality of input variables at different points in time as input signals, and is taught corresponding control variables as teaching signals, and untaught input patterns are given to this intelligent neuron network model to have it determine corresponding control variables. A plurality of patterns at predetermined time intervals are preferably used at once as patterns to be taught.

Oil pressure control systems for automatic transmissions

Abstract: An oil pressure control system for an automatic transmission employed in a vehicle comprises a device for producing line pressure, a device for supplying engaging friction elements in the automatic transmission with operating oil pressure obtained from the line pressure, a device for determining a pressure control variable based on operating condition of an engine with which the automatic transmission is coupled, a device for determining a supplemental control variable based on a duration of speed change or a state of speed change in the automatic transmission, a device for revising the supplemental control variable in accordance with the operating condition of the engine to produce a revised supplemental control variable, a device for revising the pressure control variable in accordance with the revised supplemental control variable to produce a revised pressure control variable, and a control device operative to control the line pressure in accordance with the revised pressure control variable when a specific speed change operation is performed in the automatic transmission.