Conventional Linear Control Research Articles

Observer design for multiphase flow in vertical pipes with gas-lift––theory and experiments

Unstable regimes occurring for multiphase flow in vertical risers have successfully been stabilized using conventional linear control techniques. However, these control systems rely on downhole measurements which are at best unreliable, if at all available. In this paper, we design a nonlinear observer for the states of the multiphase flow that relies on topside measurements only, and apply it to estimate downhole pressure for feedback control. A key feature of the design is that it exploits the structure of the model to obtain robustness with respect to the internal flows in the system. Combining the nonlinear observer with conventional PI control of the downhole pressure, we demonstrate in laboratory experiments the potential for increasing production from gas-lift wells by stabilizing the multiphase flow.

Decentralized optimal neuro-controllers for generation and transmission devices in an electric power network

In this paper, the dual heuristic programming (DHP) optimization algorithm is applied for the design of two LOCAL nonlinear optimal neuro-controllers on a practical multi-machine power system. One neuro-controller is designed to replace the conventional linear controllers, which are the automatic voltage regulator (AVR) and speed-governor (GOV), for a synchronous generator. The other is a new external neuro-controller for the series capacitive reactance compensator (SCRC), flexible ac transmission systems (FACTS) device. The PSCAD/EMTDC ® simulation results show that interactions of two DHP neuro-controllers with different control objectives improve the system performance more effectively compared to when each one operates without the presence of the other one.

Anti-Slug control of gas-lift wells - Experimental results

Unstable flow regimes occurring for multiphase flow in gas-lift wells have successfully been stabilized. using conventional linear control techniques. However, the control systems rely on downhole measurements which are at best unreliable, if at all available. In this paper, we employ a nonlinear observer for the states of the multi phase flow in the gas-lift well. The observer relies on topside measurements only, and downhole pressure for feedback control is estimated. Combining the non linear observer with conventional PI control of the downhole pressure, we demonstrate in laboratory experiments the potential for increasing production from gas-lift oil wells by stabilizing the multiphase flow.

New External Neuro-Controller for Series Capacitive Reactance Compensator in a Power Network

The controllable capacitive reactance can be used as the input variable for the external controller of a series capacitive reactance compensator (SCRC) to improve the damping of low-frequency oscillations of the rotor angle and active power in a power system. Conventional linear PI controllers are tuned for best performance at one specific operating point of the nonlinear power system. At other operating point its performance degrades. Nonlinear optimal neuro-controllers are able to overcome this degradation. In this paper, the dual heuristic dynamic programming (DHP) optimization algorithm is applied to design an external nonlinear optimal neuro-controller for the SCRC. Simulation studies using PSCAD/EMTDC/spl reg/ software are presented.

An intelligent based LQR controller design to power system stabilization

This paper presents an intelligent model, named as free model, approach for a closed-loop system identification using input and output data and its application to design a power system stabilizer (PSS). The free model concept is introduced as an alternative intelligent system technique to design a controller for such dynamic system, which is complex, difficult to know, or unknown, with input and output data only, and it does not require the detail knowledge of mathematical model for the system. In the free model, the data used has incremental forms using backward difference operators. A linear transformation is introduced to convert the free model into a linear model so that a conventional linear controller design method can be applied. Also, it is shown that the free model is controllable, observable, and robust to disturbance. In this paper, the feasibility of the proposed method is demonstrated in a three-machine nine-bus power system. The linear quadratic regulator (LQR) method is applied to the free model to design a PSS for the system, and compared with the conventional PSS and LQR controller based on the ARMA-model. The free-model based (FMB) PSS is robust in different loading conditions and system failures such as the outage of a major transmission line or a three phase to ground fault which causes the change of the system structure.

Nonlinear decentralized robust governor control for hydroturbine-generator sets in multi-machine power systems

A novel nonlinear decentralized robust governor control for hydroturbine-generator sets in multi-machine power systems is suggested in this paper. The nonelastic water hammer effect and disturbances are considered in the modeling. The advanced differential geometry theory, nonlinear robust control theory and the dynamic feedback method are combined to solve the problem. The nonlinear decentralized robust control law for the speed governor of hydroturbine-generators has been derived. The input signals to the proposed controller are all local measurements and independent to the system parameters. The derived control law guarantees the integrated system stability with disturbance attenuation, which is significant to the real power system application. Computer tests on an 8-machine, 36-bus power system show clearly the effectiveness of the new control strategy in transient stability enhancement and disturbance attenuation. The computer test results based on the suggested controller are compared favorably with those based on the conventional linear governor control.

Design of a strip thickness control system for tandem cold mills using H∞ control techniques

In order to meet the requirement for higher thickness accuracy in cold-rolling mill processes, it is strongly desired to have an increasing high performance in control units. To meet this requirement, an output regulating control system with a roll-eccentricity estimator for each rolling stand of tandem cold mills was considered. Assuming entry thickness variation as well as roll eccentricity to be the major disturbances, a synthesis of multivariable control systems is presented and based on H∞ control theory, which can reflect knowledge of the input direction and spectrum of disturbance signals on design. Then, to reject roll eccentricity effectively, a weight function having some poles on the imaginary axis is introduced. This leads to a non-standard H∞ control problem, and the design procedures for solving this problem are analytically presented. The effectiveness of the proposed control method is evaluated through computer simulations and compared with the conventional linear quadratic control and feedforward control methods for roll eccentricity.

Positioning performance and straightness error compensation of the magnetic levitation stage supported by the linear magnetic bearing

This paper reports high-precision motion control at the target position and the offline compensation of the straightness error occurring during the motion of the magnetically levitated stage supported by the linear magnetic bearing. The linear magnetic bearing is composed of electromagnets and sensors in the same module to enable easy maintenance and to reduce the machining error, and the conventional linear quadratic Gaussian control is adopted for the motion control of the stage. The test results show that the stage can compensate the straightness error precisely, so that it is expected that this stage can be applied to high-precision linear motion applications.

Intelligent adaptive control of bioreactors

Supervised model-based self-tuning control of fermentation processes is addressed. The diversity, nonlinearity and time-varying nature of these processes make their control a challenging task. Conventional linear (PID) controllers with fixed parameters cannot meet the increasing performance requirements over the whole operating range. In the approach pursued in this research, a local linear model is identified at the current working point by using a limited amount input–output data obtained through an identification experiment. A linear controller is then tuned on the basis of this model. To minimize the intervention into the process operation, this tuning procedure is only initiated if the performance of the current controller deteriorates. To this end, a supervisory expert system is designed whose tasks are to monitor the process performance, design an appropriate identification experiment, validate the obtained model and tune the controller. The supervisory system is based on a combination of a state automaton with a rule-based fuzzy inference system. Experimental results have demonstrated the feasibility of this approach.

ATTITUDE AND CONFIGURATION CONTROL OF FLEXIBLE MULTI-BODY SPACECRAFT

Multi-body spacecraft attitude and configuration control formulations based on the use of collaborative control theory are considered. The control formulations are based on two-player, nonzero-sum, differential game theory applied using a Nash strategy. It is desired that the control laws allow different components of the multi-body system to perform different tasks. For example, it may be desired that one body points toward a fixed star while another body in the system slews to track another satellite. Although similar to the linear quadratic regulator formulation, the collaborative control formulation contains a number of additional design parameters because the problem is formulated as two control problems coupled together. The use of the freedom of the partitioning of the total problem into two coupled control problems and the selection of the elements of the cross-coupling matrices are specific problems ad-dressed in this paper. Examples are used to show that significant improvement in performance, as measured by realistic criteria, of collaborative control over conventional linear quadratic regulator control can be achieved by using proposed design guidelines.

Design of a Fuzzy Logic Controller for a Rotary-type Inverted Pendulum System

Various inverted pendulum systems have been frequently used as a model for the performance test of the proposed control system. We first identify a rotary-type inverted pendulum system by the Euler-Lagrange method and then design a FLC (Fuzzy Logic Controller) fur the plant. FLC`s are one of useful control schemes fur plants having difficulties in deriving mathematical models or having performance limitations with conventional linear control schemes. Many FLC`s imitate the concept of conventional PD (Proportional-Derivative) or PI (Proportional-Integral) controller. That is, the error e and the change-of-error are used as antecedent variables and the control input u the change of control input Au is used as its consequent variable for FLC`s. In this paper we design a simple-structured FLC for the rotary inverted pendulum system. We also perform some computer simulations to examine the tracking performance of the closed-loop system.

Sliding mode neural network inference fuzzy logic control for active suspension systems

In the automotive industry, suspension systems are designed to provide desirable vehicle ride and handling properties. This paper presents the development of a robust intelligent nonlinear controller for active suspension systems based on a comprehensive and realistic nonlinear model. The inherent complex nonlinear system model's structure, and the presence of parameter uncertainties, have increased the difficulties of applying conventional linear and nonlinear control techniques. Recently, the combination of sliding mode, fuzzy logic, and neural network methodologies has emerged as a promising technique for dealing with complex uncertain systems. In this paper, a sliding mode neural network inference fuzzy logic controller is designed for automotive suspension systems in order to enhance the ride and comfort. Extensive simulations are performed on a quarter-car model, and the results show that the proposed controller outperforms existing conventional controllers with regard to body acceleration, suspension deflection, and tire deflection.

Pattern-based predictive control for ETBE reactive distillation

Synthesis of ethyl tert-buty ether (ETBE), a high-performance fuel additive, through reactive distillation (RD) is an attractive route, while its operation and control are exceptionally difficult due to its functional combination and complex dynamics. Modern control technology greatly relies on good process models, while a reasonable RD model is too complex for control design. Moreover, RD contains considerable uncertainties that cannot be well described in process modelling. Alleviating the model requirement, this work aims to maintain the product purity in RD of ETBE through developing a pattern-based predictive control (PPC) scheme. Process dynamics, control structure, nonlinear transformation, feature pattern extraction, and pattern-based prediction and its incorporation with a conventional linear controller are discussed. Case studies show the effectiveness of the proposed method

APPROXIMATE DESIGN METHOD OF MRAC WITH NN FOR PNEUMATIC SERVO SYSTEM

Pneumatic servo systems are used widely in industrial field. However, the dynamic characteristics often change with load mass and there are non-linear elements caused by air compressibility, various frictions and so on. Therefore, it is difficult for conventional linear control methods to accomplish satisfactory control performance. In addition, there is a problem that discrete-time models of pneumatic servo system have possibilities to become non-minimum phase by difference of the sampling period, change in load mass, and so on.In this paper, for these problems, we propose a design scheme which combines a Model Reference Adaptive Control (MRAC) using delta-operator and a Neural Network (NN). In this design, even if the discrete-time model is non-minimum phase, it can be constructed by regarding approximate model of the plant expressed by delta-operator as minimum-phase model, and NN compensates for modeling error and the non-linearity of pneumatic servo systems.

A neural network-based approximation method for discrete-time nonlinear servomechanism problem

A feedback controller that solves the discrete-time nonlinear servomechanism problem relies on the solution of a set of nonlinear functional equations known as the discrete regulator equations. The exact solution of the discrete regulator equations is usually unavailable due to the nonlinearity of the system. The paper proposes to approximately solve the discrete regulator equations using a feedforward neural network. This approach leads to an effective way to practically solve the discrete nonlinear servomechanism problem. The approach has been illustrated using the well-known inverted pendulum on a cart system. The simulation shows that the control law designed by the proposed approach performs much better than the conventional linear control law.

Fuzzy modelling and tracking control of nonlinear systems

This paper presents a fuzzy modelling and tracking control methodology for complex systems by combining the merits of fuzzy logic and conventional linear control theory. Here, fuzzy logic is used to formulate a system model by aggregating a set of linearized local subsystems which identify the nonlinear system approximately, and a fuzzy feedback controller is designed by use of conventional linear feedback theory and fuzzy reasoning. A simulation example of a one-link rigid robotic manipulator is given to demonstrate the validity of the proposed control scheme. It is shown that the fuzzy model can be simplified, and good tracking control performance can be achieved by choosing appropriate fuzzy roles.

Development of a Pipe Positioning System Using a 6-DOF Suspension Mechanism. 1st Report, Study of a Pipe Positioning System and its Control Technique.

This paper mainly focused on the control system design and the control performance of the pipe positioning system. The controller was designed based on the disturbance observer concept. The pipe positioning system has a 6-DOF suspension mechanism, which consists of six servomotors and chains. A relationship between the actuator space (chain length) and task space (position and attitude of a pipe center of gravity) is expressed using the Jacobian matrix. Each element of this Jacobian matrix is generally nonlinear function in space. In this study, a disturbance observer concept was used to remove the nonlinearlity that is caused by the Jacobian matrix, then a conventional linear feedback control low was applied to the control system. The control performance was verified through experiments using a pipe with a diameter of 0.3 m. In the experiments, trajectories of the pipe center of gravity with the Jacobian nonlinearly compensation was compared to the trajectories without Jacobian compensation, and the effectiveness of the proposed pipe positioning system was shown.

A study on interaction control for seismic response of parallel structures

A structure response control approach which uses controlled interactions between two parallel structures (a primary structure and an auxiliary structure) to reduce the seismic response of the primary structure (P-structure) during earthquake excitation, is proposed. Three strategies of the control approach, including optimal passive control, semi-active control, and active control are examined. The optimum passive-coupling elements between two parallel structures under different circumstances are firstly investigated. Then, emphasis has been placed on the derivation of a simple effective algorithm for semi-active control which guarantees the power absorption of the required control force from the P-structure at every time step. Finally, numerical results in the frequency and time domains are presented to demonstrate the effectiveness of these proposed control strategies. The comparison of the structure response time histories when subjected to El Centro 1940 ground excitation with different control methods also illustrates that the semi-active control based on optimum passive coupling element can achieve improvement in reducing the response of P-structure compared to the passive control and is almost comparable to the active control system with the same degree of magnitude of control force, and that the proposed instantaneous power absorption semi-active control algorithm is always superior to conventional linear control law.

A Method for Countering Friction in High Accuracy Precision Mechanisms

In response to the increasing need for accurate motion control in mechatronic systems, this paper presents a method of increasing accuracy by reducing the detrimental effects of friction at very low speeds. A controller is presented which merges the technique of impulsive friction compensation with conventional linear control. The result is a controller that provides a much smoother output than a pure impulsive controller. This controller is analysed and then applied to an XY table machine tool test bed both in simulation and experimentation; results are presented showing improved performance.

Development of a Fuzzy Rule-Based Multi-Objective Hybrid Controller for Static Synchronous Compensator

Static synchronous compensator (Statcom) is a powerful new device for power systems, which can be used for various purposes. The multi-objective demands are quite different in nature, e.g. continuous linear control for voltage maintaining, and discrete bang-bang control for oscillation damping. Unfortunately, they often conflicr with each other. In this respect, a supplementary damping control together with an independent voltage control is normally used. However, inevitable small disturbance and uncertainties will cause problems in the coordination of the two functions. To overcome such difficulties, a fuzzy rule-based hybrid controller is proposed in this paper, which incorporates conventional linear voltage control along with the fuzzy rule based supplementary power damping control to form a unified global controller for Statcom. Because only simple fuzzy rules and a few input signals are involved, it is very easy to implement in a practical power system. The simulation performed on a Sin gle-Machine-Infinite-Bus (SMIB) power system and an actual large power system demonstrates the effectiveness of the proposed approach.