Model predictive control techniques for hybrid systems

This paper describes the main issues encountered when applying model predictive control to hybrid processes. Hybrid model predictive control (HMPC) is a research field non-fully developed with many open challenges. The paper describes some of the techniques proposed by the research community to overcome the main problems encountered. Issues related to the stability and the solution of the optimization problem are also discussed. The paper ends by describing the results of a benchmark exercise in which several HMPC schemes were applied to a solar air conditioning plant.Ministerio de Eduación y Ciencia DPI2007-66718-C04-01Ministerio de Eduación y Ciencia DPI2008-0581

Applications of Model Predictive Controllers in a Sugar Factory

INTERNATIONAL SYMPOSIUM ON ADVANCED CONTROL OF CHEMICAL PROCESSThis paper presents two applications of Model Predictive Control in a sugar factory: temperature control in the diffusion process and density control in the wastewater treatment plant. The implementation is done using a Generalized Predictive Controller (GPC) designed for a wide class of industrial process, with the same computational requirements as a PID routine and embedded in the existing control system. The processes have in common the existence of long and uncertain dead times, therefore the original GPC algorithm is improved by the use of the T polynomial, which increases the stability robustness by filtering the predictions.Comisión Interministerial de Ciencia y Tecnología (CICYT) TAP 96-0884Comisión Interministerial de Ciencia y Tecnología (CICYT) TAP 98-0541Comisión Interministerial de Ciencia y Tecnología (CICYT) 1FD97-083

Dynamic optimization for controller tuning with embedded safety and response quality measures

Controller tuning is needed to select the optimum response for the controlled process. This work presents a new tuning procedure of PID controllers with safety and response quality measures on a non-linear process model by optimization procedure, with a demonstration of two tanks in series. The model was developed to include safety constraints in the form of path constraints. The model was then solved with a new optimization solver, NLPOPT1, which uses a primal-dual interior point method with a novel non-monotone line search procedure with discretized penalty parameters. This procedure generated a grid of optimal PID tuning parameters for various switching of steady-states to be used as a predictor of PID tunings for arbitrary transitions. The interpolation of tuning parameters between the available parameters was found to be capable to produce state profiles with no violation on the safety measures, while maintaining the quality of the solution with the final set points targeted achievable

A survey on fractional order control techniques for unmanned aerial and ground vehicles

In recent years, numerous applications of science and engineering for modeling and control of unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs) systems based on fractional calculus have been realized. The extra fractional order derivative terms allow to optimizing the performance of the systems. The review presented in this paper focuses on the control problems of the UAVs and UGVs that have been addressed by the fractional order techniques over the last decade

Predictive control of anaerobic digestion of wastewater sludge: a feasibility study

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Optimal operation of combined heat and power systems: an optimization-based control strategy

The use of decentralized Combined Heat and Power (CHP) plants is increasing since the high levels of efficiency they can achieve. Thus, to determine the optimal operation of these systems in dynamic energy-market scenarios, operational constraints and the time-varying price profiles for both electricity and the required resources should be taken into account. In order to maximize the profit during the operation of the CHP plant, this paper proposes an optimization-based controller designed according to the Economic Model Predictive Control (EMPC) approach, which uses a non-constant time step along the prediction horizon to get a shorter step size at the beginning of that horizon while a lower resolution for the far instants. Besides, a softening of related constraints to meet the market requirements related to the sale of electric power to the grid point is proposed. Simulation results show that the computational burden to solve optimization problems in real time is reduced while minimizing operational costs and satisfying the market constraints. The proposed controller is developed based on a real CHP plant installed at the ETA research factory in Darmstadt, Germany.Peer ReviewedPostprint (author's final draft

IMC Based Fractional Order Controller for Three Interacting Tank Process

In model based control, performance of the controlled plant considerably depends on the accuracy of real plant being modelled. In present work, an attempt has been made to design Internal Model Control (IMC), for three interacting tank process for liquid level control. To avoid complexities in controller design, the third order three interacting tank process is modelled to First Order Plus Dead Time (FOPDT) model. Exploiting the admirable features of Fractional Calculus, the higher order model is also modelled to Fractional Order First Order Plus Dead Time (FO-FOPDT) model, which further reduces the modelling error. Moving to control section, different IMC schemes have been proposed, where the fractional order filter is introduced along with the conventional integer order filter. Various simulations have been performed to show the greatness of Fractional order modelled system & fractional order filters over conventional integer order modelled system & integer order filters respectively. Both for parameters estimation of reduced order model and filter tuning, Genetic Algorithm (GA) is being applied. 

Process Mining of Programmable Logic Controllers: Input/Output Event Logs

This paper presents an approach to model an unknown Ladder Logic based Programmable Logic Controller (PLC) program consisting of Boolean logic and counters using Process Mining techniques. First, we tap the inputs and outputs of a PLC to create a data flow log. Second, we propose a method to translate the obtained data flow log to an event log suitable for Process Mining. In a third step, we propose a hybrid Petri net (PN) and neural network approach to approximate the logic of the actual underlying PLC program. We demonstrate the applicability of our proposed approach on a case study with three simulated scenarios