Hybrid Predictive Control Research Articles

Hybrid predictive controller for overheating prevention of solar collectors

In this paper, an advanced control algorithm is presented for avoiding fluid overheating when operating solar thermal plants. The control algorithm manipulates the fluid rate to maintain the field output temperature in the desired reference, and defocus the solar collectors when the temperature reaches a maximum limit. Several simulation scenarios are considered for the test of the proposed controller using a lumped parameters model of the solar collectors that has been validated in real plants. A discussion is carried out aiming at analyzing the behavior of the controlled solar plant, and comparing the characteristics and performance of the proposed algorithms.

Hybrid predictive control strategy for a low‐cost converter‐fed IM drive

The converter consisting of a single-phase half-bridge rectifier and four-switch three-phase inverter is a low-cost power converter with complicated operating constraints. It is difficult to control by the conventional strategies. This study proposes a hybrid predictive control strategy with dual loops for this converter. In the outer loop, a proportional–integral controller is designed to regulate the dc-link voltage, capacitor voltage balancing, and the speed and flux of induction motors (IM). Also, in the inner loop, the finite set model predictive control is employed to control the ac input current and stator currents of the IM. The major advantages of the control strategy include: (i) easy to deal with complicated constraints and manage multiple control targets; (ii) without the need of modulators; (iii) good dynamic response. The simulation and experimental results indicate that the proposed method can guarantee the stable operation and good performance.

Predictive Control of Microgrids with Mixed Sources for Desalination in Remote Areas

Abstract: In this paper a proposal for the application of hybrid predictive control to the energy management of a microgrid with two renewable energy sources (solar and wind) for desalination is proposed, and the application to a specific case study in Tunisia is shown.

A hybrid predictive control approach for the management of an energy production–consumption system applied to a TRNSYS solar absorption cooling system for thermal comfort in buildings

The aim of this paper is to propose a control approach for the management of a production–consumption energy system based on model predictive control (MPC). The solar absorption cooling system is seen as part of this production–consumption energy system where the solar hot water storage (SHWS) system is the producer subsystem and the chiller-building system is one of the consumer subsystems. In order to provide modularity to the control structure, the coordination between the subsystems is achieved by designing interactive local predictive controllers. The consumer controllers compute a set of energy demand profiles sent to the producer controller which selects the profile that better minimizes the global optimization cost function. In a first part, the proposed control approach is tested on a simplified linear model composed of one producer and several consumers. In a second part, a more complex case is studied. A simplified model of an absorption cooling system is evaluated using the simulation tool TRNSYS. The producer model is no longer linear, instead it is described by a nonlinear hybrid model which increases the complexity of the optimization problem. The simulations results show that the performance of the controller fulfills the control objectives.

A Methodology Based on Evolutionary Algorithms to Solve a Dynamic Pickup and Delivery Problem Under a Hybrid Predictive Control Approach

This paper presents a methodology based on generic evolutionary algorithms to solve a dynamic pickup and delivery problem formulated under a hybrid predictive control approach. The solution scheme is designed to support the dispatcher of a dial-a-ride service, where quick and efficient real-time solutions are needed. The scheme considers different configurations of particle swarm optimization and genetic algorithms within a proposed ad-hoc methodology to solve in real time the nonlinear mixed-integer optimization problem related with the hybrid predictive control approach. These consist of different techniques to handle the operational constraints (penalization, Baldwinian, and Lamarckian repair) and encodings (continuous and integer). For parameter tuning, a new approach based on multiobjective optimization is proposed and used to select and study some of the evolutionary algorithms. The multiobjective feature arises when deciding the parameters with the best trade-off between performance and computational effort. Simulation results are presented to compare the different schemes proposed and to advise conditions for the application of the method in real instances.

Low speed hybrid generalized predictive control of a gasoline-propelled car

Low-speed driving in traffic jams causes significant pollution and wasted time for commuters. Additionally, from the passengers׳ standpoint, this is an uncomfortable, stressful and tedious scene that is suitable to be automated. The highly nonlinear dynamics of car engines at low-speed turn its automation in a complex problem that still remains as unsolved. Considering the hybrid nature of the vehicle longitudinal control at low-speed, constantly switching between throttle and brake pedal actions, hybrid control is a good candidate to solve this problem.This work presents the analytical formulation of a hybrid predictive controller for automated low-speed driving. It takes advantage of valuable characteristics supplied by predictive control strategies both for compensating un-modeled dynamics and for keeping passengers security and comfort analytically by means of the treatment of constraints. The proposed controller was implemented in a gas-propelled vehicle to experimentally validate the adopted solution. To this end, different scenarios were analyzed varying road layouts and vehicle speeds within a private test track. The production vehicle is a commercial Citroën C3 Pluriel which has been modified to automatically act over its throttle and brake pedals.

Control Design of Elementary Hybrid Petri Nets via Model Predictive Control

This paper addresses the control design of Hybrid Dynamic Systems (HDS) modeled by Hybrid Petri Net (HPN) systems. The resolution of the problem is based on Model Predictive Control (MPC). Our goal is to drive a HPN to reach a desired marking. Since HDS incorporates both discrete and continues processes, the proposed control strategy, called Hybrid Predictive Control, is composed of a discrete and a continuous predictive control. At each sampling period, the discrete predictive control selects a set of feasible sequences constituted of discrete immediate enabled transitions, while the continuous procedure computes the continuous constant control action over a variable prediction horizon.

Optimized treatment of fibromyalgia using system identification and hybrid model predictive control

The term adaptive intervention is used in behavioral health to describe individually tailored strategies for preventing and treating chronic, relapsing disorders. This paper describes a system identification approach for developing dynamical models from clinical data, and subsequently, a hybrid model predictive control scheme for assigning dosages of naltrexone as treatment for fibromyalgia, a chronic pain condition. A simulation study that includes conditions of significant plant-model mismatch demonstrates the benefits of hybrid predictive control as a decision framework for optimized adaptive interventions. This work provides insights on the design of novel personalized interventions for chronic pain and related conditions in behavioral health.

A practical hybrid predictive control algorithm for a low‐temperature thermosolar plant

SummaryThis work presents a control approach to deal with plants formed by several interconnected subprocesses and where the interconnection between subprocesses can change during the plant operation. The change from one operation mode to another, where each operation mode is defined by a discrete variable set, implies a change in the process behaviour that must be counteracted by the control system. In this work, a practical hybrid model predictive control is proposed to take the behaviour changes related to the hybrid nature of the process into account. The proposed control algorithm is based on the adaptation of several well‐known control strategies that have been successfully applied in the industrial field. A solar thermal system (composed of a solar thermal flat collector field, one or more accumulation tanks connected in series and a gas heater) is used to show the resulting control strategy through detailed simulations. Copyright © 2014 John Wiley & Sons, Ltd.

Hybrid predictive control of a coaxial aerial robot for physical interaction through contact

This paper deals with the control of an unmanned coaxial helicopter designed towards active physical interaction with its environment. The system design is tailored to robust interaction through contact (e.g. docking and sliding on walls). Due to the rapid change of the dynamics from free-flight to the helicopter subject to the contact forces, a hybrid model is developed. This model captures all modes of the vehicle dynamics and is the basis for the design of a hybrid model predictive control strategy that ensures the stability of the hybrid dynamics and provides optimal maneuvering, docking on walls as well as sliding on them. The proposed control law is evaluated through experimental studies.

Hybrid Model Predictive Control for Optimizing Gestational Weight Gain Behavioral Interventions.

Excessive gestational weight gain (GWG) represents a major public health issue. In this paper, we pursue a control engineering approach to the problem by applying model predictive control (MPC) algorithms to act as decision policies in the intervention for assigning optimal intervention dosages. The intervention components consist of education, behavioral modification and active learning. The categorical nature of the intervention dosage assignment problem dictates the need for hybrid model predictive control (HMPC) schemes, ultimately leading to improved outcomes. The goal is to design a controller that generates an intervention dosage sequence which improves a participant's healthy eating behavior and physical activity to better control GWG. An improved formulation of self-regulation is also presented through the use of Internal Model Control (IMC), allowing greater flexibility in describing self-regulatory behavior. Simulation results illustrate the basic workings of the model and demonstrate the benefits of hybrid predictive control for optimized GWG adaptive interventions.

Comparison of dynamic control strategies for transit operations

Real-time headway-based control is a key issue to reduce bus bunching in high frequency urban bus services where schedules are difficult to implement. Several mechanisms have been proposed in the literature, but very few performance comparisons are available. In this paper two different approaches are tested over eight different scenarios. Both methodologies solve the same problem, the former based on a deterministic optimization over a long-term rolling horizon, while the latter proposes a hybrid predictive approach considering a shorter horizon and a stochastic evolution of the system. The comparison is conducted through scenarios that include three different dimensions: (i) bus capacities which can be reached or not, (ii) service frequencies, considering high and medium frequency services and (iii) different load profiles along the corridor. The results show that the deterministic approach performs better under scenarios where bus capacity could be reached frequently along the route while the hybrid predictive control approach performs better in situations where this does not happen.

Hybrid Predictive Control Based on High-Order Differential State Observers and Lyapunov Functions for Switched Nonlinear Systems

In this paper, a hybrid predictive controller is proposed for a class of uncertain switched nonlinear systems based on high-order differential state observers and Lyapunov functions. The main idea is to design an output feedback bounded controller and a predictive controller for each subsystem using high-order differential state observers and Lyapunov functions, to derive a suitable switched law to stabilize the closed-loop subsystem, and to provide an explicitly characterized set of initial conditions. For the whole switched system, based on the high-order differentiator, a suitable switched law is designed to ensure the whole closed-loop’s stability. The simulation results for a chemical process show the validity of the controller proposed in this paper.

Hybrid Predictive Control for Building Climate Control and Energy Optimization

The growing world's energy use and energy prices have raised a concern about the efficiency of energy management systems, energy resources and environmental impacts. In developed countries, buildings are accredited for 40% of the global energy consumption, being the heating and cooling system one of the main causes of it; as the growth of population, comfort levels and services are directly related to thermal systems. For this reason we present a Hybrid Model Predictive Control (HMPC) strategy to control all rooms of a building apartment in a centralized manner. This approach decreases energy consumption, maintaining comfort levels in an acceptable range, and showing that MPC control solutions can be substituted by more complex controls (as HMPC) to manage building thermal systems.

A Hybrid Predictive Control Strategy for the Coordinated Voltage

The substation is one of the most important nodes for the power system voltage control. Its dynamic behavior is usually characterized by the interaction of dynamic load models, describing continues variables governed by differential equations, and of discrete controllers and coordinated logic, describing symbolic variables governed by discrete behaviors, logic rules and switching mechanisms. Therefore, the operation and control of the substation is a typical hybrid dynamic system. To make an effective voltage coordinated control in substation, this paper proposed a hybrid predictive control strategy. First, the discrete-time hybrid automata model was given for describing the hybrid voltage control system in substation. Then, by using the voltage-security control constrain instead of the goal of voltage-security control, the voltage coordinated control problem can be converted to a minimization cost problem on the premise of the voltage-security control, based on the technology of the MPC. And, for the discrete control characteristics and different control delays of multi-controllers, a branch and search algorithm was designed to solve the hybrid predictive control problem. This proposed algorithm is suitable for the voltage predictive control of substation, which have less control variables and different controllers’ delays. Finally, the coordinated controller was designed on Dymola. And the example of BPA test system three generations and ten buses included was given for simulation analysis and the verification by the proposed approach for substation coordinated voltage control. The results of simulation showed that the proposed control strategy in the paper is feasible.

Hybrid predictive control strategy for a public transport system with uncertain demand

In this article, a hybrid predictive control (HPC) strategy is formulated for the real-time optimisation of a public transport system operation run using buses. For this problem, the hybrid predictive controller corresponds to the bus dispatcher, who dynamically provides the optimal control actions to the bus system to minimise users’ total travel time (on-vehicle ride time plus waiting time at stops). The HPC framework includes a dynamic objective function and a predictive model of the bus system, written in discrete time, where events are triggered when a bus arrives at a bus stop. Upon these events, the HPC controller makes decisions based on two well-known real-time transit control actions, holding and expressing. Additionally, the uncertain passenger demand is included in the model as a disturbance and then predicted based on both offline and online information of passenger behaviour. The resulting optimisation problem of the HPC strategy at every event is Np-hard and needs an efficient algorithm to solve it in terms of computation time and accuracy. We chose an ad hoc implementation of a Genetic Algorithm that permits the proper management of the trade-off between these two aspects. For real-time implementation, the design of this HPC strategy considers newly available transport technology such as the availability of automatic passenger counters (APCs) and automatic vehicle location (AVL) devices. Illustrative simulations at 2, 5 and 10 steps ahead are conducted, and promising results showing the advantages of the real-time control schemes are reported and discussed.

Output feedback predictive control for uncertain non-linear switched systems

In this paper, a hybrid predictive controller is proposed for a class of uncertain non-linear switched system with input constraints and unavailable state measurements. The main idea is to design a bounded non-linear controller and a predictive controller for each subsystem using Lyapunov functions and state observers, also a suitable switched law to stabilise the closed-loop subsystem, and to provide an explicitly characterised set of initial condition. For the whole switched system, based on the state estimates, a suitable switched law is designed to ensure the whole closed-loop’s stability. The simulation results for a chemical process show the validity of the controller proposed in this paper.

Hybrid predictive control of a DC–DC boost converter in both continuous and discontinuous current modes of operation

AbstractDeveloping efficient and appropriate modeling and control techniques for DC–DC converters is of major importance in power electronics area and has attracted much attention from automatic control theory. Since DC–DC converters have a complex hybrid nature, recently several techniques based on hybrid modeling and control have been introduced. These techniques have shown better results as compared with conventional averaging‐based schemes with limited modeling and control abilities. But the current works in this field have not considered all possible dynamics of the converters in both continuous and discontinuous current modes (CCM, DCM) of operations. These dynamics are results of controlled and uncontrolled switching phenomena in DC–DC converters. This paper proposes a new switching scheme for modeling and controlling a DC–DC boost converter. The converter is represented as a hybrid automaton by considering the all three possible modes. The hybrid automaton is translated into the mixed logical dynamical (MLD) mathematical framework. The switching among these modes is generated by hybrid predictive control method which is calculated by Mixed Integer Quadratic Programming (MIQP). Using the exact model of the converter, the proposed switching algorithm can globally control the converter in all operation regimes, including CCM and DCM operations, considering all constraints in the physical plant, such as maximum inductor current and capacitor voltage limits. The switching algorithm is applied to a real converter circuit, modeled with various parasitic components. Simulation results are provided to show the advantages of the proposed control scheme. Copyright © 2010 John Wiley & Sons, Ltd.

Operation of desalination plants using renewable energies and hybrid control

This paper presents a control system for the operation of desalination plants powered by locally generated renewable energy. To cope with the variability of the water demands and the available energy, the amount of water produced must be manipulated optimally. This is carried out by regulating a variable speed pump, using hybrid predictive control, which takes into account the operational limits of both the pump and the process. The operation is complex because the scheduling of the cleaning operations and the optimization of the energy consumption must be performed simultaneously to the control of the plant, fulfilling a set of additional constraints. The paper uses a continuous reformulation of the problem in order to simplify the optimization process. Simulations of a specific plant show that an optimal operation reduces the extra energy consumption, and makes it possible to supply the variable water demand.

Operation of Medium-size Reverse Osmosis Plants with Optimal Energy Consumption

This paper deals with the optimal operational strategy of a reverse osmosis (RO) plant for remote sites. The electricity supply to these plants comes usually from renewable energies (wind and solar), when they are not temporarily available, they are complemented by a diesel generator and batteries. The water demand of small settlements in arid regions suffers strong variations along a day. As the higher demand of water usually occurs when solar energy is more available, the operational expenses can be reduced by considering the RO plant as an active load. A good control strategy, will implement a variable operation point, taking into account the predictions of water demand, the expected variation of the available energy sources and the scheduling of cleaning operations in the RO plant, in order to optimize the energy use. In this paper a hybrid predictive control is proposed to implement this task. Simulations of a specific plant show that an adequate operation reduces the diesel energy consumption, while satisfying the variable water demand.