Physical Plant Model Research Articles

Automated formal synthesis of provably safe digital controllers for continuous plants

We present a sound and automated approach to synthesizing safe, digital controllers for physical plants represented as time-invariant models. Models are linear differential equations with inputs, evolving over a continuous state space. The synthesis precisely accounts for the effects of finite-precision arithmetic introduced by the controller. The approach uses counterexample-guided inductive synthesis: an inductive generalization phase produces a controller that is known to stabilize the model but that may not be safe for all initial conditions of the model. Safety is then verified via bounded model checking: if the verification step fails, a counterexample is provided to the inductive generalization, and the process further iterates until a safe controller is obtained. We demonstrate the practical value of this approach by automatically synthesizing safe controllers for physical plant models from the digital control literature.

A Modeling Approach for HVAC Systems based on the LoLiMoT Algorithm

Abstract In heating ventilating and air conditioning (HVAC) systems, typically two variables (air temperature and air humidity) have to be controlled via several (at least two) actuators. Some of the components show nonlinear behaviour. Therefore, HVAC systems belong to the class of nonlinear multi-input-multi-output systems. A well suited approach to control this class of systems is model predictive control, since the time constants of HVAC systems are high (typically in the range of tens or hundreds of seconds) offering enough time to perform the required online optimization. In order to apply linear predictive control methods, while taking into account the nonlinearities of the plant, a modeling concept based on a physical plant model and a neuro-fuzzy model is proposed. The neuro-fuzzy model is obtained via the so called local linear model tree (LoLiMoT) algorithm. The generation of a linear state space representation from the neuro-fuzzy model is demonstrated. This linear state space model can then be used in a predicitive control scheme, where the linear model is updated each sampling instant from the neuro-fuzzy model. This technique allows the application of standard linear predictive control while taking into account the nonlinearities of the plant. Simulation and measurement results obtained from an industrial test plant are presented.

On fusion of PCA and a physical model‐based predictive control strategy for efficient load‐cycling operation of a thermal power plant

AbstractControlling a thermal power plant optimally during load‐cycling operation is a very challenging control problem. The control complexity is enhanced further by the possibility of simultaneous occurrence of sensor malfunctions and a plethora of system disturbances. This paper proposes and evaluates the effectiveness of a sensor validation and reconstruction approach using principal component analysis (PCA) in conjunction with a physical plant model. For optimal control under severe operating conditions in the presence of possible sensor malfunctions, a predictive control strategy is devised by appropriate fusion of the PCA‐based sensor validation and reconstruction approach and a constrained model predictive control (MPC) technique. As a case study, the control strategy is applied for thermal power plant control in the presence of a single sensor malfunction. In particular, it is applied to investigate the effectiveness and relative advantage of applying rate constraints on main steam temperature and heat‐exchanger tube‐wall temperature, so that faster load cycling operation is achieved without causing excessive thermal stresses in heat‐exchanger tubes. In order to account for unstable and non‐minimum phase boiler–turbine dynamics, the MPC technique applied is an infinite horizon non‐linear physical model‐based state‐space MPC strategy, which guarantees asymptotic stability and feasibility in the presence of output and state constraints. Copyright © 2007 John Wiley & Sons, Ltd.

OBJECT-ORIENTED SIMULATION FOR THE CONTROL OF THE IRIS NUCLEAR POWER PLANT

The case study of the dynamic modelling and simulation of the new IRIS power plant control system is presented. After reviewing the base concepts of object-oriented physical modelling, the principles upon which the component libraries are based, and the structure of the plant, the implementation of a system simulator, comprising the physical plant model and the control system model is described. In particular, it is shown how the object-oriented approach allows an efficient management of the different degrees of detail needed throughout the model life-cycle. A discussion on the simulator performance and some representative results are also included.

Trajectory tracking in batch processes using neural controllers

Optimal operation of batch processes usually involves closely following a pre-optimized batch trajectory, e.g., the temperature trajectory in an exothermic batch reactor. Controllers for trajectory tracking have previously been designed and tuned based on a physical or empirical plant model. In batch processes where it is difficult to build a sufficiently accurate model, it is attractive to tune a nonlinear parameterized controller directly on the plant data, provided the number of batch runs required for the iterative tuning remains acceptably low. In a recent work, the authors have proposed a tuning method that makes the best use of each plant run to rigorously calculate the correct gradient for the iterative tuning optimization. In this work, this method is applied to obtain a tuned neural-network controller for tracking the temperature trajectory in an exothermic batch reactor example taken from the literature. Results indicate the efficacy of the method for optimizing a neural controller without requiring an excessive number of batch runs for the trial-and-error iterations.

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 non-stationary 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 power 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.

A Novel Hierarchical Approach to Plant-Wide Control of a Thermal Power Unit

Abstract This paper presents a novel hierarchical approach to plant-wide control by making effective use of a physical plant model. It incorporates a two-level control structure consisting of lower-level conventional PI regulators and a higher-level non-linear model predictive controller (NMPC). The lower-level PI loops help stabilise the unstable dmm-boiler dynamics and allow faster governor valve action for power and grid-frequency regulation. The higher-level NMPC provides an optimal load demand (or set-point) transition by effective handling of plant-wide interactions and system disturbances. The strategy has been tested on a simulation of a 200 MW oil-fired power plant at Ballylumford in N. Ireland. Impressive results have been obtained during large system disturbances and extremely high rate of load changes. These results compare favourably with those from a non-linear, state-space generalised predictive control (GPC) method designed under similar conditions.