Data-based mechanistic modelling, forecasting, and control.

This article briefly reviews the main aspects of the generic data based mechanistic (DBM) approach to modeling stochastic dynamic systems and shown how it is being applied to the analysis, forecasting, and control of environmental and agricultural systems. The advantages of this inductive approach to modeling lie in its wide range of applicability. It can be used to model linear, nonstationary, and nonlinear stochastic systems, and its exploitation of recursive estimation means that the modeling results are useful for both online and offline applications. To demonstrate the practical utility of the various methodological tools that underpin the DBM approach, the article also outlines several typical, practical examples in the area of environmental and agricultural systems analysis, where DBM models have formed the basis for simulation model reduction, control system design, and forecastin

Controllable forms for stabilising pole assignment design of generalised bilinear systems

Bilinear structures are able to represent nonlinear phenomena more accurately than linear models, and thereby help to extend the range of satisfactory control performance. However, closed loop characteristics are typically designed by simulation and stability is not guaranteed. In this reported work, it is shown how bilinear systems are a special case of the more general state dependent parameter (SDP) model, which can subsequently be utilised to design stabilising feedback controllers using a special form of nonlinear pole assignment. To establish the link, however, an important generalisation of the SDP pole assignment method is developed

Proportional-integral-plus (PIP) control of the ALSTOM gasifier problem

Although it is able to exploit the full power of optimal state variable feedback within a non-minimum state-space (NMSS) setting, the proportional-integral-plus (PIP) controller is simple to implement and provides a logical extension of conventional proportional-integral and proportional-integral-derivative (PI/PID) controllers, with additional dynamic feedback and input compensators introduced automatically by the NMSS formulation of the problem when the process is of greater than first order or has appreciable pure time delays. The present paper applies the PIP methodology to the ALSTOM benchmark challenge, which takes the form of a highly coupled multi-variable linear model, representing the gasifier system of an integrated gasification combined cycle (IGCC) power plant. In particular, a straightforwardly tuned discrete-time PIP control system based on a reduced-order backward-shift model of the gasifier is found to yield good control of the benchmark, meeting most of the specified performance requirements at three different operating points

Cost effective combined axial fan and throttling valve control of ventilation rate

This paper is concerned with Proportional-Integral-Plus (PIP) control of ventilation rate in mechanically ventilated agricultural buildings. In particular, it develops a unique fan and throttling valve control system for a 22m3 test chamber, representing a section of a livestock building or glasshouse, at the Katholieke Universiteit Leuven. Here, the throttling valve is employed to restrict airflow at the outlet, so generating a higher static pressure difference over the control fan. In contrast with previous approaches, however, the throttling valve is directly employed as a second control actuator, utilising airflow from either the axial fan or natural ventilation. The new combined fan/valve configuration is compared with a commercially available PID-based controller and a previously developed scheduled PIP design, yielding a reduction in power consumption in both cases of up to 45%

Nonlinear control by input-output pole assignment: state space derivation.

This paper considers pole assignment control of nonlinear dynamic systems described by State Dependent Parameter (SDP) models. The approach follows from earlier research into linear Non-Minimal State Space (NMSS) methods but, in the nonlinear case, the control gains are updated at each sampling instant. The algorithm is derived directly from the NMSS model, necessitating the introduction of a state dependent transformation matrix. This state variable feedback derivation lends itself to straightforward controllability and stability analysis. In this regard, the paper shows that the closed-loop system reduces to a linear transfer function with the specified poles; and that these differ from the closed-loop transition matrix eigenvalues

Non-minimal state dependent Riccati equation and pole assignment control of nonlinear systems.

This paper considers pole assignment and Riccati equation control of nonlinear dynamic systems described by State Dependent Parameter (SDP) models. The approach follows from earlier research into linear Proportional-Integral-Plus (PIP) methods but, in SDP system control, the control coefficients are updated at each sampling instant on the basis of the SDP relationships. Alternatively, algebraic solutions can be derived off-line to yield a practically useful control algorithm that is relatively straightforward to implement on a digital computer, requiring only the storage of lagged system variables, coupled with straightforward arithmetic expressions in the control software. Two examples are used to illustrate the approach. In the first instance, state space matrix analysis of a first order system shows that the expected design response is obtained for specified pole positions, including dead-beat; hence, assuming pole assignability at each sample, global stability of the nonlinear system is guaranteed at the design stage. Secondly, the paper evaluates the approach for a classical, physically-based simulation model of an inverted pendulum

State dependent control of a robotic manipulator used for nuclear decommissioning activities.

This article considers pole assignment control of nonlinear dynamic systems described by State Dependent Parameter (SDP) models, with a particular focus on a Brokk-40 mobile robot and Hydro-Lek HLK-7W two-arm manipulator used for nuclear decommissioning tasks. The UK nuclear legacy comprises a number of facilities that are significantly contaminated by radioactivity and non-radiological toxins. Here, the use of remote and teleoperated robotic solutions provide an invaluable option for the safe retrieval and disposal of contaminated materials. Since the behaviour of hydraulically-driven manipulators is dominated by the nonlinear, lightly-damped dynamics of the actuators, existing systems can suffer from a relatively slow and imprecise control action. For this reason, the research utilises a non-minimal state variable feedback approach to control system design, in which the control gains are updated at each sampling instant