Linear Control Model Research Articles

Optimizing Control of Hot Blast Stoves in Staggered Parallel Operation

An optimizing control scheme is designed for the minimization of the energy consumption in the hot blast stoves. By an appropriate selection of the control structure, the problem of energy minimization at steady-state in the presence of constraints is formulated as a constrained optimal control problem. A model predictive control (MPC) scheme is designed based on a simple linear control model, which is obtained from step response experiments on a detailed dynamical model of the process. The control model is augmented with an integrating disturbance model to compensate the steady-state offset. The performance of the MPC scheme is tested on the detailed dynamical model.

End-to-end deadline control for aperiodic tasks in distributed real-time systems

A category of Distributed Real-Time Systems (DRTS) that has multiprocessor pipeline architecture is increasingly used. The key challenge of such systems is to guarantee the end-to-end deadlines of aperiodic tasks. This paper proposes an end-to-end deadline control model, called Linear Quadratic Stochastic Optimal Control Model (LQ-SOCM), which features a distributed feedback control that dynamically enforces the desired performance. The control system considers the aperiodic task arrivals and execution times' variation as the two external factors of the system unpredictability. LQ-SOCM uses discrete time state space equation to describe the real-time computing system. Then, in the actuator design, a continuous manner is adopted to deal with discrete QoS (Quality of Service) adaptation. Finally, experiments demonstrate that the system is globally stable and can statistically provide the end-to-end deadline guarantee for aperiodic tasks. At the same time, LQ-SOCM is capable of effectively improving the system throughput.

Study and application of crown feedback control in hot strip rolling

Crown feedback control is one part of the automatic shape control (ASC) system. On the basis of large simulation researches conducted, a linear crown feedback control model was put forward and applied in actual strip rolling. According to its successful operation in the ASP1700 hot strip mill of Angang Group for one year and also from the statistical results of several crown measurements, it can be definitely said that this control model is highly effective and shows stable performance. The control effectiveness of different gauges of strips with the feedback control is found to increase by 10%-30% compared with that without feedback control.

Controlling a Semiconductor Optical Amplifier Using a State-Space Model

We derive nonlinear and linear state-space control models for a multichannel semiconductor optical amplifier. Verified against the governing partial differential equations through simulation, the linear model tracks modulations up to 20% qualitatively well. Linear feedback control is then employed to design two interchannel crosstalk suppressing systems, one using state feedback into the electronic drive current and the other using optical output feedback into an optical control channel; the controller designed with the linear model is seen to work well even with 100% modulations of the nonlinear system. This linear state-space model opens the way for further robust analysis, design and control of integrated active photonic circuits

End-to-End Utilization Control for Aperiodic Tasks in Distributed Real-Time Systems

An increasing number of DRTS (Distributed Real-Time Systems) are employing an end-to-end aperiodic task model. The key challenges of such DRTS are guaranteeing utilization on multiple processors to achieve overload protection, and meeting the end-to-end deadlines of aperiodic tasks. This paper proposes an end-to-end utilization control architecture and an IC-EAT (Integration Control for End-to-End Aperiodic Tasks) algorithm, which features a distributed feedback loop that dynamically enforces the desired utilization bound on multiple processors. IC-EAT integrates admission control with feedback control, which is able to dynamically determine the QoS (Quality of Service) of incoming tasks and guarantee the end-to-end deadlines of admitted tasks. Then an LQOCM (Linear Quadratic Optimal Control Model) is presented. Finally, experiments demonstrate that, for the end-to-end DRTS whose control matrix G falls into the stable region, the IC-EAT is convergeut and stable. Moreover, it is capable of providing better QoS guarantees for end-to-end aperiodic tasks and improving the system throughput.

Regime switching in a fishery with stochastic stock and price

A bioeconomic model of a fishery subject to stock uncertainty and price uncertainty is developed. With a linear control model, the optimal harvest policy is a bang–bang approach to the optimal stock level, where one harvests either at minimum or full capacity. It is assumed that changing the harvest rate is subject to a switching cost. In this case it is shown that there are two switching curves in stock-price space, one for entering and one for leaving the fishery. Numerical methods are used to characterise the optimal switching policy for the fishery.

The Economics of Managing Infectious Wildlife Disease

AbstractWe use a two‐state linear control model to examine the socially optimal management of disease in a valuable wildlife population when diseased animals cannot be harvested selectively. The two control variables are nonselective harvests and supplemental feeding of wildlife, where feeding increases both in situ productivity and disease prevalence. We derive a double singular solution which depends on the initial state and does not require bang‐bang controls. The case of bovine tuberculosis among Michigan white‐tailed deer is analyzed. In the base model, the disease is optimally maintained at low levels, with intermittent investments (via feeding) in deer productivity.

Model Predictive Control of A Parafoil and Payload System

A model predictive control strategy is developed for an autonomous parafoil and payload system. Since the technique requires a linear dynamic model of the system, a reduced state linear model based on a nonlinear 6 degree-of-freedom parafoil and payload model is established and validated. In order to use the reduced state linear model for model predictive control the desired trajectory in the x-y plane is mapped into a desired heading angle using Lagrange interpolating polynomials. Flight test results demonstrate that this model predictive control strategy is a natural and effective method of achieving trajectory tracking in a parafoil and payload system.

An Optimal Estimator Model of Multi-Sensory Processing in Human Postural Control

We hypothesized that multi-sensory processing at the central nervous system (CNS) in human postural control can be described using an optimal estimator model. The estimates on body dynamics from multi-sensory signals contain sensory noise, transmission delays, and process disturbances. The state estimates approximate actual body movement. Erroneous estimates degrade the performance of feedback control and could cause a loss of balance if distorted severely. To test the hypothesis, we examined the frequency response of a visually-induced postural sway with stimulus frequency ranging from 0.075 to 1Hz and established an optimal estimator model. Two healthy young (33yrs ± 1) subjects stood on a force platform located 1.25m behind a projection screen with their arms crossed over their chests. They were asked to maintain an upright posture against the sinusoidal visual field stimuli. Each sinusoidal visual stimulus was generated by a projector for 200secs in pitch direction with a maximum pitch angle of 20o. Kinematics data was recorded to calculate the frequency response function of the center of mass (COM). There were three components in the modeling procedure: a biomechanical model of body and sensor dynamics, a linear feedback control model to stabilize the biomechanical model, and a state estimator to estimate body dynamic states based on multi-sensory outputs. We modeled the sensor dynamics of the semicircular canal, otolth, vision, and muscle spindles at the ankle and hip joint. We used the Kalman filter and linear quadratic regulator to determine feedback gains. Results showed that the frequency response function of a visually-induced postural sway decreased as stimulus frequency increased, and this low-pass filtering characteristic with an approximate cutoff frequency of 0.2Hz was also simulated by the postural feedback control model with optimal estimator. Low-pass filtering characteristics of the frequency response are mainly due to body and sensor dynamics, which show reduced responses for high frequency stimulus. The Kalman filter represents that the CNS utilizes redundant sensory information in a way that minimizes discrepancies between actual body dynamics and estimated body dynamics based on sensory output and an internal model. The results suggest that the CNS may make use of an internal representation of body dynamics, and can integrate sensory information in an optimal way to best estimate human postural responses.

Multivariable Predictive Control of an Activated Sludge Process with Nitrogen Removal

Abstract In the paper multivariable predictive control of the wastewater treatment benchmark is presented. The goal of the predictive control is to improve daily disturbance rejection of the plant in relation to nitrogen removal. A simple Dynamic Matrix Controller (DMC) is used as a predictive controller. The multivariable linear black-box model for predictive control is identified in the open-loop using subspace identification method. The model interactions between inputs and outputs and conditioning are analysed. Simulation results show that the chosen predictive controller gives lower violations of the effluent ammonia limit compared to PI controllers at increased energy consumption for aeration.

Full Multiscale Approach for Optimal Control of In Situ Bioremediation

Solving field-scale optimal groundwater remediation design problems is a challenge, especially when computationally intensive reactive transport models are needed. In this paper, a full multiscale approach to partial differential equation (PDE) constrained optimization is developed and is used to solve a successive approximation linear quadratic regulator model for optimal control of in situ bioremediation. The method starts the search for optimal designs from the coarsest mesh and solves for the optimal solution at that level, then uses the optimal solution obtained as the initial guess for the finer mesh. While at the finer mesh, the method switches back to the coarser mesh to solve for the derivatives and uses those derivatives to interpolate back to the finer mesh. This procedure continues until convergence is achieved at the finest level. This approach exploits important interactions between PDE discretization and optimization and achieves significant computational saving by using approximations early in the search when a broad search of the decision space is being performed. As the solution becomes more refined, more accurate estimates are needed to fine-tune the solution, and finer spatial discretizations are used. Application of the method to a bioremediation case study with about 6,500 state variables converges in about 8.8 days, compared to nearly 1 year using the previous model. This substantial improvement will enable much more realistic bioremediation design problems to be solved than was previously possible, particularly once the model is implemented in parallel.

A general linear mathematical model of power flow analysis and control for integrated structure–control systems

Generalized integrated structure–control dynamical systems consisting of any number of active/passive controllers and three-dimensional rigid/flexible substructures are investigated. The developed mathematical model assessing the behaviour of these complex systems includes description of general boundary conditions, the interaction mechanisms between structures, power flows and control characteristics. Three active control strategies are examined. That is, multiple channel absolute/relative velocity feedback controllers, their hybrid combination and an existing passive control system to which the former control systems are attached in order to improve overall control efficiency. From the viewpoint of continuum mechanics, an analytical solution of this generalized structure–control system has been developed allowing predictions of the dynamic responses at any point on or in substructures of the coupled system. Absolute or relative dynamic response or receptance, transmissibility, mobility, transfer functions have been derived to evaluate complex dynamic interaction mechanisms through various transmission paths. The instantaneous and time-averaged power flow of energy input, transmission and dissipation or absorption within and between the source substructure, control subsystems and controlled substructure are presented. The general theory developed provides an integrated framework to solve various vibration isolation and control problems and provides a basis to develop a general algorithm that may allow the user to build arbitrarily complex linear control models using simple commands and inputs. The proposed approach is applied to a practical example to illustrate and validate the mathematical model as well as to assess control effectiveness and to provide important guidelines to assist vibration control designers.

Labour market training in Norway—effect on earnings

By means of a 6-year panel of annual earnings data, we estimate the impact of a labour market training programme (LMT) targeted at unemployed adults. Unlike most nonexperimental studies, we use an internal comparison group of rejected applicants to measure the counterfactual outcome of trainees. For a subsample of applicants, rejections were randomised on a course-by-course basis. Following Heckman and Hotz [J. Am. Stat. Assoc. 84 (1989) 862] we test alternative estimators. All models report positive effects of being offered training, though the effects are not always significantly different from zero. The linear control estimator reports significantly positive effects of training. Analysing courses with ordinary assignment, the pre-training test indicates a positive selection bias. Turning to applicants for courses with random assignment, the linear control model is not rejected by the pre-training test. The fixed effect, random growth and modified random growth models are rejected by the model restriction test; this is the case for courses with ordinary assignment as well as random assignment. We illustrate the value of an internal comparison group in estimating the counterfactual outcome of participants, by comparing the post-training outcomes of rejected applicants and unemployed nonapplicants. Rejected applicants have significantly higher post-training earnings than eligible nonapplicants, which indicates that self-selection into the programme is influenced by unobservables. In general, the results clearly support the view that reliable studies of training effects should be based on internal comparison groups. Moreover, the assumptions motivating nonexperimental estimators should be explicitly tested.

Model Predictive Control of Interconnected Linear and Nonlinear Processes

A plant-wide control strategy based on integration of linear model predictive control (LMPC) and nonlinear model predictive control (NMPC) is proposed. The design method is applicable to plants that can be decomposed according to the nonlinearity properties of the individual unit operations. The basic idea is to apply LMPC and NMPC controllers to the linear and nonlinear subsystems, respectively. A systematic procedure for performing the plant decomposition given nonlinearity information is presented. Because the subsystems are coupled via material and energy flows, a sequential solution procedure that aims to minimize the amount of unknown information in the MPC designs is developed. The plant decomposition and sequential MPC solution algorithms are applied to a large-scale styrene production flowsheet. Three controller coordination strategies are developed to handle the information-exchange problems caused by sequential MPC solution. The methods are shown to be nominally stabilizing for nonlinear plants with a certain triangular structure. A multi-rate extension for plants with time-scale separations is presented. A reaction/separation process with recycle is used to compare the different hybrid MPC approaches.

Modification and Partial Validation of the Driver/Vehicle Module

Four assumptions of the Driver/Vehicle Module of the Interactive Highway Safety Design Model were tested against data obtained in two on-road studies of driver behavior. Supported were assumptions that drivers track to the inside of horizontal curves and that a linear control model is adequate for describing steering behavior. Not supported were assumptions of consistent preferred lateral acceleration in horizontal curves and consistent preferred longitudinal accelerations and decelerations during curve approach and exit. Experimental results support (a) an analytically derived square-root relationship between preferred lateral acceleration and curvature and (b) an analytically derived square-root relationship between longitudinal acceleration or deceleration and total speed change.

Minimum entropy control of non-Gaussian dynamic stochastic systems

This paper presents a new method to minimize the closed loop randomness for general dynamic stochastic systems using the entropy concept. The system is assumed to be subjected to any bounded random inputs. Using the recently developed linear B-spline model for the shape control of the system output probability density function, a control input is formulated which minimizes the output entropy of the closed-loop system. Since the entropy is the measure of randomness for a given random variable, this controller can thus reduce the uncertainty of the closed-loop system. A sufficient condition is established to guarantee the local stability of the closed-loop system. It is shown that this minimum entropy control concept generates a minimum variance control when the stochastic system is represented by an ARMAX model which is subjected to Gaussian noises. An illustrative example is utilized to demonstrate the use of the control algorithm, and satisfactory results are obtained.

Design analysis of plasma position control in SST1

For long-duration steady-state operation of the Steady-state Superconducting Tokamak (SST1), maintenance of the plasma radial and vertical positions within narrow limits would be crucial. For designing the position controller in SST1, we have adopted the simple linear RZIP control model. While the vertical position instability is slowed down by a set of passive stabilizers placed closed to the plasma edge, a pair of in-vessel active feedback coils can adequately control the perturbations to vertical position. The shifts in radial position due to minor disruptions would be controlled by a separate pair of poloidal field coils also placed inside the vessel. However, the radial position controller would ignore fast but insignificant changes in radius arising due to edge localized modes. Design considerations for the passive stabilizers and feedback control coils are discussed, and the simulations for vertical and radial position control using the RZIP model are presented. The parameters of both vertical and radial position control coils and their power supplies are determined based on the RZIP simulations.

Plasma position control in SST1 tokamak

For long duration steady state operation of SST1, it would be very crucial to maintain the plasma radial and vertical positions accurately. For designing the position controller in SST1 we have adopted the simple linear RZIP control model. While the vertical position instability is slowed down by a set of passive stabilizers placed closed to the plasma edge, a pair of in-vessel active feedback coils can adequately control vertical position perturbations of up to 1 cm. The shifts in radial position arising due to minor disruptions would be controlled by a separate pair of poloidal field (PF) coils also placed inside the vessel, however the controller would ignore fast but insignificant changes in radius arising due to edge localised modes. The parameters of both vertical and radial position control coils and their power supplies are determined based on the RZIP simulations.

A hybrid model predictive control strategy for nonlinear plant-wide control

Abstract A plant-wide control strategy based on integrating linear model predictive control (LMPC) and nonlinear model predictive control (NMPC) is proposed. The hybrid method is applicable to plants that can be decomposed into approximately linear subsystems and highly nonlinear subsystems that interact via mass and energy flows. LMPC is applied to the linear subsystems and NMPC is applied to the nonlinear subsystems. A simple controller coordination strategy that counteracts interaction effects is proposed for the case of one linear subsystem and one nonlinear subsystem. A reactor/separator process with recycle is used to compare the hybrid method to conventional LMPC and NMPC techniques.

Open-loop Force Control of A Three-finger Gripper Through PWM Modulated Pneumatic Digital Valves

This paper proposes an open-loop force control system for a three-finger gripper using small digital solenoid valves modulated using PWM (Pulse-Width-Modulation) technique. These valves are good candidates for this robotic application because of their small dimensions, low weight and cost, simple structure and easy operation by means of on/off signals. A linear model for pressure control in the pneumatic actuator's thrust chamber was developed and tested experimentally. A large dead-band was introduced in the PWM driver in order to investigate a suitable method for its correction. Good results were obtained by correcting the static characteristic curve of the three-way equivalent valve that controls the flow rate exchanged with the pneumatic cylinder thrust chamber. These results can be applied to similar solutions with larger digital valves. Gripping mechanism efficiency and the effects of friction forces were taken into account in developing the force control.