Identification of dynamic systems under closed-loop control

This paper introduces a novel modelling framework for identifying dynamic models of systems that are under feedback control. These models are identified under closed-loop conditions and produce a joint representation that includes both the plant and controller models in state space form. The joint plant/controller model is identified using subspace model identification (SMI), which is followed by the separation of the plant model from the identified one. Compared to previous research, this work (i) proposes a new modelling framework for identifying closed-loop systems, (ii) introduces a generic structure to represent the controller and (iii) explains how that the new framework gives rise to a simplified determination of the plant models. In contrast, the use of the conventional modelling approach renders the separation of the plant model a difficult task. The benefits of using the new model method are demonstrated using a number of application studies.

Neural modelling, control and optimisation of an industrial grinding process

This paper describes the development of neural model-based control strategies for the optimisation of an industrial aluminium substrate disk grinding process. The grindstone removal rate varies considerably over a stone life and is a highly nonlinear function of process variables. Using historical grindstone performance data, a NARX-based neural network model is developed. This model is then used to implement a direct inverse controller and an internal model controller based on the process settings and previous removal rates. Preliminary plant investigations show that thickness defects can be reduced by 50% or more, compared to other schemes employed. (c) 2004 Elsevier Ltd. All rights reserved.

Learning soft computing control strategies in a modular neural network architecture

Modelling and control of nonlinear dynamical systems is a challenging problem since the dynamics of such systems change over their parameter space. Conventional methodologies for designing nonlinear control laws, such as gain scheduling, are effective because the designer partitions the overall complex control into a number of simpler sub-tasks. This paper describes a new genetic algorithm based method for the design of a modular neural network (MNN) control architecture that learns such partitions of an overall complex control task. Here a chromosome represents both the structure and parameters of an individual neural network in the MNN controller and a hierarchical fuzzy approach is used to select the chromosomes required to accomplish a given control task. This new strategy is applied to the end-point tracking of a single-link flexible manipulator modelled from experimental data. Results show that the MNN controller is simple to design and produces superior performance compared to a single neural network (SNN) controller which is theoretically capable of achieving the desired trajectory. (C) 2003 Elsevier Ltd. All rights reserved.

On velocity-based local model networks for nonlinear identification

This paper exposes the strengths and weaknesses of the recently proposed velocity-based local model (LM) network. The global dynamics of the velocity-based blended representation are directly related to the dynamics of the underlying local models, an important property in the design of local controller networks. Furthermore, the sub-models are continuous-time and linear providing continuity with established linear theory and methods. This is not true for the conventional LM framework, where the global dynamics are only weakly related to the affine sub-models. In this paper, a velocity-based multiple model network is identified for a highly nonlinear dynamical system. The results show excellent dynamical modelling performances, highlighting the value of the velocity-based approach for the design and analysis of LM based control. Three important practical issues are also addressed. These relate to the blending of the velocity-based local models, the use of normalised Gaussian basis functions and the requirement of an input derivative.

A hierarchical physical model-based approach to predictive control of a thermal power plant for efficient plant-wide disturbance rejection

Extending the work presented in Prasad et al. (IEEE Proceedings on Control Theory and Applications, 147, 523-37, 2000), this paper reports a hierarchical nonlinear physical model-based control strategy to account for the problems arising due to complex dynamics of drum level and governor valve, and demonstrates its effectiveness in plant-wide disturbance handling. The strategy incorporates a two-level control structure consisting of lower-level conventional PI regulators and a higher-level nonlinear physical model predictive controller (NPMPC) for mainly set-point manoeuvring. The lower-level PI loops help stabilise the unstable drum-boiler dynamics and allow faster governor valve action for power and grid-frequency regulation. The higher-level NPMPC provides an optimal load demand (or set-point) transition by effective handling of plant-wide interactions and system disturbances. The strategy has been tested in a simulation of a 200-MW oil-fired power plant at Ballylumford in Northern Ireland. A novel approach is devized to test the disturbance rejection capability in severe operating conditions. Low frequency disturbances were created by making random changes in radiation heat flow on the boiler-side, while condenser vacuum was fluctuating in a random fashion on the turbine side. In order to simulate high-frequency disturbances, pulse-type load disturbances were made to strike at instants which are not an integral multiple of the NPMPC sampling period. Impressive results have been obtained during both types of system disturbances and extremely high rates of load changes, right across the operating range, These results compared favourably with those from a conventional state-space generalized predictive control (GPC) method designed under similar conditions.

Plant-wide predictive control for a thermal power plant based on a physical plant model

A constrained non-linear, physical model-based, predictive control (NPMPC) strategy is developed for improved plant-wide control of a thermal power plant. The strategy makes use of successive linearisation and recursive state estimation using extended Kalman filtering to obtain a linear state-space model. The linear model and a quadratic programming routine are used to design a constrained long-range predictive controller One special feature is the careful selection of a specific set of plant model parameters for online estimation, to account for time-varying system characteristics resulting from major system disturbances and ageing. These parameters act as nonstationary stochastic states and help to provide sufficient degrees-of-freedom to obtain unbiased estimates of controlled outputs. A 14th order non-linear plant model, simulating the dominant characteristics of a 200 MW oil-fired pou er plant has been used to test the NPMPC algorithm. The control strategy gives impressive simulation results, during large system disturbances and extremely high rate of load changes, right across the operating range. These results compare favourably to those obtained with the state-space GPC method designed under similar conditions.

Neural network based control for synchronous generators

In this paper, a Radial Basis Function neural network based AVR is proposed. A control strategy which generates local linear models from a global neural model on-line is used to derive controller feedback gains based on the Generalised Minimum Variance technique. Testing is carried out on a micromachine system which enables evaluation of practical implementation of the scheme. Constraints imposed by gathering training data, computational load, and memory requirements for the training algorithm are addressed.

Solventless continuous flow homogeneous hydroformylation of 1-octene

The hydroformylation of 1-octene under continuous flow conditions is described. The system involves dissolving the catalyst, made in situ from [ Rh(acac)(CO)(2)] (acacH = 2,4- pentanedione) and [RMIM][TPPMS] ( RMIM = 1-propyl (Pr), 1-pentyl (Pn) or 1-octyl (O)-3-methyl imidazolium, TPPMS = Ph2P(3-C6H4SO3)), in a mixture of nonanal and 1-octene and passing the substrate, 1-octene, together with CO and H-2 through the system dissolved in supercritical CO2 (scCO(2)). [PrMIM][TPPMS] is poorly soluble in the medium so heavy rhodium leaching (as complexes not containing phosphine) occurs in the early part of the reaction. [PnMIM][ PPMS] affords good rates at relatively low catalyst loadings and relatively low overall pressure (125 bar) with rhodium losses <1 ppm, but the catalyst precipitates at higher catalyst loadings, leading to lower reaction rates. [OMIM][ TPPMS] is the most soluble ligand and promotes high reaction rates, although preliminary experiments suggested that rhodium leaching was high at 5-10 ppm. Optimisation aimed at balancing flows so that the level within the reactor remained constant involved a reactor set up based around a reactor fitted with a sight glass and sparging stirrer with the CO2 being fed by a cooled head HPLC pump, 1-octene by a standard HPLC pump and CO/H-2 through a mass flow controller. The pressure was controlled by a back pressure regulator. Using this set up, [OMIM][ TPPMS] as the ligand and a total pressure of 140 bar, it was possible to control the level within the reactor and obtain a turnover frequency of ca. 180 h(-1). Rhodium losses in the optimised system were 100 ppb. Transport studies showed that 1-octene is preferentially transported over the aldehydes at all pressures, although the difference in mol fraction in the mobile phase was less at lower pressures. Nonanal in the mobile phase suppresses the extraction of 1-octene to some extent, so it is better to operate at high conversion and low pressure to optimise the extraction of the products relative to the substrate. CO and H2 in the mobile phase also suppress the extraction effciency by as much as 80%.

Design and implementation of a field programmable CRC circuit architecture

The design and implementation of a programmable cyclic redundancy check (CRC) computation circuit architecture, suitable for deployment in network related system-on-chips (SoCs) is presented. The architecture has been designed to be field reprogrammable so that it is fully flexible in terms of the polynomial deployed and the input port width. The circuit includes an embedded configuration controller that has a low reconfiguration time and hardware cost. The circuit has been synthesised and mapped to 130-nm UMC standard cell [application-specific integrated circuit (ASIC)] technology and is capable of supporting line speeds of 5 Gb/s. © 2006 IEEE.

Coordinated control of DFIG’s rotor and grid side converters during network unbalance

This paper proposes a coordinated control of the rotor and grid side converters (RSC & GSC) of doubly-fed induction generator (DFIG) based wind generation systems under unbalanced voltage conditions. System behaviors and operations of the RSC and GSC under unbalanced voltage are illustrated. To provide enhanced operation, the RSC is controlled to eliminate the torque oscillations at double supply frequency under unbalanced stator supply. The oscillation of the stator output active power is then cancelled by the active power output from the GSC, to ensure constant active power output from the overall DFIG generation system. To provide the required positive and negative sequence currents control for the RSC and GSC, a current control strategy containing a main controller and an auxiliary controller is analyzed. The main controller is implemented in the positive (dq)+ frame without involving positive/negative sequence decomposition whereas the auxiliary controller is implemented in the negative sequence (dq)? frame with negative sequence current extracted. Simulation results using EMTDC/PSCAD are presented for a 2MW DFIG wind generation system to validate the proposed control scheme and to show the enhanced system operation during unbalanced voltage supply.

Improved Direct Power Control of Grid Connected DC/AC Converters

This paper proposes new direct power control (DPC) strategies for three-phase DC/AC converters with improved dynamic response and steady-state performance. As with an electrical machine, source and converter flux which equal the integration of the respective source and converter voltage are used to define active and reactive power flow. Optimization of the look-up-table used in conventional DPC is outlined first, to improve the power control and reduce the current distortion. Then constant switching frequency DPC is developed where the required converter voltage vector within a fixed half switching period is calculated directly from the active and reactive power errors. Detailed angle compensation due to the finite sampling frequency and the use of integral controller to further improve the power control accuracy, are described. Both simulation and experimental results are used to compare conventional DPC and vector control, and to demonstrate the effectiveness and robustness of the proposed control strategies during active and reactive power steps, and line inductance variations.

Extruder Melt Temperature Control with Fuzzy Logic

In polymer extrusion, the delivery of a melt which is homogenous in composition and temperature is paramount for achieving high quality extruded products. However, advancements in process control are required to reduce temperature variations across the melt flow which can result in poor product quality. The majority of thermal monitoring methods provide only low accuracy point/bulk melt temperature measurements and cause poor controller performance. Furthermore, the most common conventional proportional-integral-derivative controllers seem to be incapable of performing well over the nonlinear operating region. This paper presents a model-based fuzzy control approach to reduce the die melt temperature variations across the melt flow while achieving desired average die melt temperature. Simulation results confirm the efficacy of the proposed controller.

An integrated multi-sensor data fusion algorithm and autopilot iImplementation in an uninhabited surface craft

Unmanned surface vehicles (USVs) are able to accomplish difficult and challenging tasks both in civilian and defence sectors without endangering human lives. Their ability to work round the clock makes them well-suited for matters that demand immediate attention. These issues include but not limited to mines countermeasures, measuring the extent of an oil spill and locating the source of a chemical discharge. A number of USV programmes have emerged in the last decade for a variety of aforementioned purposes. Springer USV is one such research project highlighted in this paper. The intention herein is to report results emanating from data acquired from experiments on the Springer vessel whilst testing its advanced navigation, guidance and control (NGC) subsystems. The algorithms developed for these systems are based on soft-computing methodologies. A novel form of data fusion navigation algorithm has been developed and integrated with a modified optimal controller. Experimental results are presented and analysed for various scenarios including single and multiple waypoints tracking and fixed and time-varying reference bearings. It is demonstrated that the proposed NGC system provides promising results despite the presence of modelling uncertainty and external disturbances.

Feedforward Control of Shape Memory Alloy Actuators Using Fuzzy-Based Inverse Preisach Model

This brief investigates a possible application of the inverse Preisach model in combination with the feedforward and feedback control strategies to control shape memory alloy actuators. In the feedforward control design, a fuzzy-based inverse Preisach model is used to compensate for the hysteresis nonlinearity effect. An extrema input history and a fuzzy inference is utilized to replace the inverse classical Preisach model. This work allows for a reduction in the number of experimental parameters and computation time for the inversion of the classical Preisach model. A proportional-integral-derivative (PID) controller is used as a feedback controller to regulate the error between the desired output and the system output. To demonstrate the effectiveness of the proposed controller, real-time control experiment results are presented.

Internal model control for shape memory alloy actuators using fuzzy based Preisach model

This paper investigates a possible application of Preisach model to control shape memory alloy (SMA) actuators using an internal model control strategy. The developed strategy consists in including the Preisach hysteresis model of SMA actuator and the inverse Preisach model within the control structure. In this work, an extrema input hystory and a fuzzy inference is utilized to replace the classical Preisach model. This allows to reduce a large amount of experimental parameters and computation time of the classical Preisach model. To demonstrate the effectiveness of the proposed controller in improving control performance and hysteresis compensation of SMA actuators, experimental results from real time control are presented.