Multivariable feedforward control design using disturbance cost maps and a genetic algorithm

An underactuated multibody system has less control inputs than degrees of freedom. For trajectory tracking, often a feedforward control is necessary. Two different approaches for feedforward control design are presented. The first approach is based on a coordinate transformation into the nonlinear input–output normal-form. The second approach uses servo-constraints and results in a set of differential algebraic equations. A comparison shows that both feedforward control designs have a similar structure. The analysis of the mechanical design of underactuated multibody systems might show that they are nonminimum phase, i.e., they have unstable internal dynamics. Then the feedforward control cannot be computed by time integration and output trajectory tracking becomes a very challenging task. Therefore, based on the two presented feedforward control design approaches, it is shown that through the use of an optimization procedure underactuated multibody systems can be designed in such a way that they are minimum phase. Thus, feedforward control design using the two approaches is significantly simplified.

Future space structures have many closely spaced, lightly damped natural frequencies throughout the frequency domain. To achieve desired performance objectives, a number of these modes must actively be controlled. For control, a combination of collocated and noncollocated sensors and actuators will be employed. The control designs will be formulated based on models which have inaccuracies due to unmodeled dynamics, and variations in damping levels, natural frequencies and mode shapes. Therefore, along with achieving the performance objectives, the control design must be robust to a variety of uncertainty. This paper focuses on the benefits and limitations associated with multivariable control design using noncollocated versus collocated sensors and actuators. We address the question of whether performance is restricted due to the noncollocation of the sensors and actuators or the uncertainty associated with modeling of the flexible structures. Control laws are formulated based on models of the system and evaluated analytically and experimentally. Results of implementation of these control laws on the Caltech flexible structure are presented.

Feedforward control can be considered as the most well-known control approach to deal with measurable disturbances. It started to be used almost 100 years ago, and since then it is being used in most industrial processes. It is a very simple technique that has been used typically as a complement to PID control, although it can be combined with any feedback controller. The feedforward control design has traditionally been performed assuming perfect cancellation of the disturbance signal, and when this solution was not possible because of non-realisable problems, static design solutions were usually implemented. In the last decade, this issue has been researched and analysed, and new designs for feedforward control have appeared to face these problems. As a result, a set of new simple tuning rules have been obtained providing considerable improvements in the control system performance. This paper presents a summary of these contributions and a short history of feedforward control.

DESIGN CONSIDERATIONS FOR A HYDROCRACKER PREFLASH COLUMN MULTIVARIABLE CONSTRAINT CONTROLLER

This paper illustrates the synthesis of nonlinear feedforward controllers for chemical reactors. In most of the theoretical development and application of feedforward control only linear systems have been considered. There are, however, no inherent linear limitations in feedforward control. Since chemical reactors are usually nonlinear, the effectiveness of control should be improved by including nonlinearities in the design of feedforward controllers. This is particularly true for batch reactors because of the large changes in variables during a batch cycle. Continuous stirred‐tank reactors are studied with single and consecutive reactions of first and higher order. Effectiveness of linear and nonlinear feedforward controllers is compared for disturbances of various magnitude and direction. Feedforward control of batch and tubular reactors is also discussed.

Feedforward control is widely applied on active vibration isolation system to attenuate vibration caused by base disturbance. Design of feedforward controller is conventionally based on plant model. Nevertheless, accurate plant model is normally not easy to be obtained. A data-driven feedforward control method is proposed in this paper to tackle this issue. In this method, no accurate plant model is needed and the problem of designing feedforward controller is formulated as a parameter optimization problem. By adopting FIR filter as feedforward controller structure, the parameter optimization problem is proved to be a convex optimization problem. A parameter iteration scheme based on Gauss-Newton algorithm is proposed to solve this parameter optimization problem. Gradient acquisition is achieved by Toeplitz matrix method. Employing certain step size, the optimal solution can be obtained with only one iteration. The proposed data-driven feedforward control method is validated by simulation and experiments. Experiments are conducted on a pneumatic spring active vibration isolation system.

Multivariable Control Designs for Piezoelectric tubes

Multivariable adaptive control for the design of STT missile autopilots

Approach to robust multivariable combustion control design

Two robust controller structures are presented. These two structures affect the process in different ways, and so both could be joined together to give more robust control than each controller separately. The structure was used for combustion control which represents multivariable and nonlinear problems with time delay. Simulation results for a closed-loop control system are presented and discussed. The procedure for designing robust multivariable control was supported with computer-aided control system design tools.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

In this paper genetic algorithms (GAs) are applied to improve the performance of fuzzy and neural multivariable controllers. In the case of fuzzy multivariable controllers the GA task is to determine an appropriate compensation element which will per–form partial decoupling between system inputs and outputs thus permitting the application of a decentralized control scheme. A novel robust fuzzy logic controller is then applied to each one of the SISO subsystems. The neural multivariable controller is based on the Gawthrop-type MIMO controller but now a neural network is used for the prediction of the system outputs. A genetic algorithm decides how much the control signal should contribute in the cost function thus succeeding the optimal compromise between system stability and zero steady-state error.

Mission of Unmanned Aerial Vehicle (UAV) “Elang Avionik” is surveillance and aerial photographs. Therefore the flight of UAV must be stable and controlable, and first step activity is dynamic modelling and stability analisys. The problems of UAV system is disturbance, noise of sensor, MIMO and uncertainty dynamic model. For good result using the multivariable robust control, with some step research that is: (1)modeling and stability analysis, (2) design and implementation of PID control system, (3) flight dynamic parameter identification, (4) design and implementation of hardware in the loop simulation, (5) design and implementation of multivariable robust control, (6) test and evaluation of system. Simulation result show that the eigen value in longitudinal is: phugoid mode = –0,061293±0,40526i and non-oscillation mode = –6,1121±4,9253. In lateral directional is:dutch roll mode = –0,91089±5,7994i, spiral mode= –0,036563, and roll subsidence mode = –12,7181. Location of poles system on the left of imaginary axis, the means that the character of system is dynamic stable. But settling time to steady state condition is very long and improved by control system design.&#x0D; Key word: State space, Longitudinal, Lateral, Stable static, Stable dynamic

Underwater glider platform represents the maturing technology with a large cost saving over current underwater sampling process. It can survey and monitor the sea environment cost-effective manner combining survey capabilities, simultaneous water sampling and environmental data gathering capacities. It can perform a wide range of fully automated monitoring data measurement over an extended period of time. This paper will focus on the design of multivariable underactuated nonlinear adaptive control using U-model methodologies. Underwater glider control, modelling and identification approach was reviewed in order to formulate the design, development and control approach of underwater glider development using multivariable adaptive U-model nonlinear control approach. U-model methodology simplifies the control synthesis with the influence of the uncertainties and external disturbances by selecting appropriate control structures. Most of the autonomous underwater vehicle (AUV) neglected the coupling effect of the dynamics during process modelling while U-model enables to include the coupling effect using the inverse Jacobian matrix. Thus contributes towards the underactuated nonlinear adaptive control development and process modelling.

This paper focuses on the problem of wind turbine fatigue load reduction by means of individual pitch control (IPC). The control approach has a two‐degree‐of‐freedom structure, consisting of an optimal multivariable LQG controller and a feedforward disturbance rejection controller based on estimated wind speed signals. To make the control design problem time invariant, all signals are transformed to the non‐rotating reference frame using the Coleman transformation. In the Coleman domain, the LQG control objective is minimization of the rotor tilt and yaw moments, whereas the feedforward controller tries to achieve even further improvement by rejecting the influence of the low‐frequency components of the wind on the rotor moments. To this end, the tilt‐ and yaw‐oriented components of the blade‐effective wind speeds are approximated using stochastic random walk models, the states of which are then augmented with the turbine states and estimated using a Kalman filter. The effects of these (estimated) disturbances on the controlled outputs are then reduced using stable dynamic model inversion. The approach is tested and compared with the conventional IPC method in simulation studies with models of different complexities. The results demonstrate very good load reduction at not only low frequencies (1p blade fatigue load reduction) but also at the 3p frequency, giving rise to fatigue load reduction of the non‐rotating turbine components. Copyright © 2008 John Wiley &amp; Sons, Ltd.

ABSTRACTThe considered transport systems, which possess an actuator with a spatial influence characteristic along the transport path, are used for the transport of material between different process stages and for the conditioning of the conveyed goods at the same time. The spatially acting input results in complex input/output behaviour of a Single-Input Single-Output (SISO) system with distributed delays. The feedforward control task under consideration is defined by a setpoint change of the subsequent process stage. For the feedforward controller design, an inversion-based approach in the frequency domain is investigated to steer the output of the transport system towards a predefined constant value. In order to compensate model uncertainties and reject unknown disturbances, the results of the inversion-based feedforward control are used to design a feedback controller.