Model Control Design Research Articles

Parameter uncertainty modeling for multiobjective robust control design. Application to a temperature control system in a proton exchange membrane fuel cell

Advanced control systems are tuned using dynamic models and optimization techniques. This approach frequently involves satisfying multiple conflicting objectives. Tuning robust controllers requires considering a framework that represents the system uncertainties, and its definition is not a trivial task. When dealing with a nonlinear model with many parameters, a high-quality representation requires a massive sampling of variations. In many cases, this represents an inaccessible computational cost for the optimization process.This work presents a new methodology for parameter uncertainty modeling that is oriented to tuning robust controllers based on multiobjective optimization techniques. The uncertainty modeling formulated represents a feasible computational cost and leads to robust solutions without attributing excessive conservatism. The novelty of this process consists in using the multiobjective space to define a set of scenarios with highly representative properties of the global uncertainty framework that formulate the control problem under a predefined minimization strategy.To demonstrate the effectiveness of this methodology, we present a temperature control design in a micro-CHP system under worst-case minimization. Based on the results, particular interest is given to verifying the appropriate formulation of the uncertainty modeling, which represents a 92.8% reduction of the computational cost involved in solving the robust optimization problem under a global uncertainty framework.

A review of swine heat production: 2003 to 2020

Swine heat production (HP) data are an essential element of numerous aspects affecting swine production sustainability, such as, housing environmental control design, energetics and thermoregulation modeling, as well as understanding of feed energy partitioning. Accurate HP values that reflect the continuous advances in growth, nutrition, health, and reproduction are needed to update outdated models and data; hence, this review of swine HP values is a critical contribution. This review updates the last previous review conducted in 2004, by reviewing literature from growing and breeding pigs from 2003 to 2020. In total, 33 references were identified that provided relevant HP data and from these references, 192 records were identified for pigs ranging in weight from 12.5 to 283 kg and exposed to temperatures between 12.0°C and 35.5°C. For growing pigs at thermoneutral conditions, a 4.7% average increase in HP was observed compared to HP data summarized from 1988 to 2004. Only five records were identified for gestating sows and the 43 records for lactating sows plus litter. This sow data shows high variability and inconsistent trends with temperature, most likely attributed to variation in experimental protocols, management, and limited reported information. There is still a lack of data on growing pigs greater than 105 kg, gilts and gestating sows housed in different systems (stall, pen, mixed, etc.), and latent HP values that reflect different housing systems. Further, there is a need to standardize reporting of HP values (with an example provided) across different disciplines to drive documentation of increased swine production efficiency, environmental control design, and energetics modeling.

Closed-loop identification of multi-axis active magnetic bearing systems based on decentralized and decoupling control

In response to the open-loop instability of magnetic bearing systems, this paper develops a closed-loop identification method to obtain an accurate plant model for controller design. The identification method is based on a decentralized and decoupling control architecture, which allows the decoupling of the multi-input-multi-output (MIMO) system into two single-input-single-output (SISO) systems for simplifying the identification process. The identification uses a pseudo-random binary sequence as the excitation signal. A parameterized model is obtained through a parameter estimation algorithm based on the output error model. To ensure that the model prediction error can converge during the computation process, a filter is incorporated into the estimation algorithm. The filter can be synthesized systematically by solving a set of linear matrix inequalities. The performance of the proposed closed-loop identification method is firstly verified by simulations. Then it is implemented on a five-axis magnetic bearing platform and the parameterized model obtained from the identification is compared to the experimental frequency response.

Dynamic Modeling and Rule-Based Control Design for a Hybrid-Electrified Regional Aircraft

Modeling and control of hybrid-electric propulsion systems (HEPSs) is a challenging area, in particular for regional aircraft. In this research, modeling and rule-based control design for a hybrid-electrified aircraft are presented. For this purpose, real data from the ATR 42-600 as well as the modified CT7-9 turboprop engine, commercial electric machines, and battery experimental characteristics have been used to size a new version of hybrid-electric regional aircraft. Based on the dynamic modeling of HEPS, practical controllers are then designed for the turboprop engine and electric machines. It should be noted that one of the critical tasks of the control strategy for hybrid-electric propulsion is to ensure the aircraft’s safe landing. In this study, a rule-based tunable regulator is proposed to modify the conventional optimal control strategy to achieve the aircraft’s required performance over a real flight mission, in particular, to fulfill the safe landing of aircraft using the remaining battery state of charge (SOC) in the event of all-turbine failure. Finally, the results are presented to demonstrate that the proposed approach is effective for minimizing the HEPS fuel consumption, where the aircraft operational constraints, such as battery SOC and turbine overtemperature, are fulfilled at different phases of the flight mission.

Deriving suitable models for model-based control design of a parallel manipulator with flexible components

Parallel manipulators are more energy-efficient and present higher speed/acceleration ratios when compared with serial manipulators. Moreover, reducing their components’ inertia can further improve these performance criteria. However, this design alternative could induce vibrations due to its lighter components. In this case, model-based control design strategies can help the designer to meet high-performance requirements. These strategies require limited-sized models, while the methods for modeling system’s flexibilities usually yield large-sized models. Therefore, strategies for reducing the size of these large-sized models should be employed. In this manuscript, a two-step model reduction strategy is proposed to derive suitable models for control design. This proposal preserves all rigid modes and selects the most relevant flexible ones. An Linear Quadratic Gaussian is designed for a flexible 3RRR parallel manipulator using a limited-sized model. This model-based control strategy is evaluated in a flexible multibody environment demonstrating the proposal’s viability.

Power-heat conversion coordinated control of combined-cycle gas turbine with thermal energy storage in district heating network

Thermal energy storage, with its low energy storage cost and wide distribution in industrial processes, is an effective way to improve the operational flexibility of power plants. Due to colossal energy storage capacity and small deployment costs, this article proposes connecting district heating networks to combined cycle gas turbine (CCGT) plants as a thermal energy storage capacity, improving the flexibility of CCGTs. The main focus here is on developing an appropriate control strategy to effectively control the power-heat conversion, meet the heat and power demands of the connected network, and the operational flexibility of the plant. The major problem is that the intrinsic static and dynamic conversion relationship of power and heat in the CCGT and district heating network and the buildings are multi-factor interactive and unknown. Therefore, the CCGT bottom cycle and district heating network, and building models were built to obtain the power-heat conversion parameters and the dynamic model for control design. Then, the energy storage coefficient of 0.105 MW/kg/s is obtained through the model simulation instead of a complex thermodynamic calculation, corresponding to the 113.22 GJ energy storage capacity of the district heating network. Based on the obtained conversion rules, a new control strategy called ‘conversion coordinated control’ is designed and applied, using load signal decomposition and synergistic load response of flue gas mass flow rate and steam extraction valve. The simulation results show that the proposed method can promote a ramping load rate of 8.6 MW/min in the first 30 s with only 0.3 °C building temperature variation. The control strategy can effectively reduce the gap between the grid demand and CCGT power and ensure grid stability without compromising thermal users’ comfort.

A three-region movable-boundary helical coil once-through steam generator model for dynamic simulation and controller design

A simple but accurate mathematical model is crucial for dynamic simulations and controller design of helical coil once-through steam generator (OTSG). This paper presents a three-region movable boundary dynamic model of the helical coil OTSG. Based on the secondary side fluid conditions, the OTSG is divided into subcooled region (two control volumes), two-phase region (two control volumes) and superheated region (three control volumes) with movable boiling boundaries between each region. The nonlinear dynamic model is derived based on mass, energy and momentum conservation equations. And the linear model is obtained by using the transfer function and state space transformation, which is a 37-order model of five input and three output. Validations are made under full-power steady-state condition and four transient conditions. Results show good agreements among the nonlinear model, linear model and the RELAP5 model, with acceptable errors. This model can be applied to dynamic simulations and controller design of helical coil OTSG with constant primary-side flow rate.

Energy Management in Buildings: Lessons Learnt for Modeling and Advanced Control Design

This paper presents a comparative analysis of different modeling and control techniques that can be used to tackle the energy efficiency and management problems in buildings. Multiple resources are considered, from generation to storage, distribution and delivery. In particular, it is shown what are the real needs and advantages of adopting different techniques, based on different applications, type of buildings, boundary conditions. This contribution is based widely on the experience performed by the authors in the recent years in dealing with existing residential, commercial and tertiary filed buildings, with application ranging from local temperature control up to smart grids where buildings are seen as an active node of the grid thanks to their ability to shape the thermal and electrical profile in real time. As for control models, a wide range of modeling techniques are here investigated and compared, from linear time-invariant models, to time-varying, to nonlinear ones. Similarly, control techniques include adaptive ones and real-time predictive ones.

Passivity-Based Power-Level Control of Nuclear Reactors

Nonlinear power-level control of nuclear reactors can guarantee wide-range closed-loop stability that is positive for plant load-following capability. Nuclear reactor power dynamics are the tight interconnection of both neutron kinetics and thermal hydraulics, which determines that the corresponding control design model is a complex nonlinear system with large uncertainty. Although nuclear reactor dynamics are complex, it is meaningful to develop simple but effective power-level control methods for easy practical implementation and commissioning. In this paper, a passivity-based control (PBC) is proposed for nuclear reactor power-level dynamics, which has a simple form and relies on the measurement of both neutron flux and average primary coolant temperature. By constructing the Lyapunov function based on the shifted ectropies of neutron kinetics and reactor core thermal hydraulics, the sufficient condition for globally asymptotic closed-loop stability is further given. Finally, this PBC is applied to the power-level control of a nuclear heating reactor, and simulation results show the feasibility and satisfactory performance.

Construction of real-time deviation electric quantity control model for regional electricity spot market

Aiming at the problems of poor control accuracy and long time consuming in traditional deviation electric quantity control methods, a real-time deviation electric quantity control model design for regional electricity spot market is proposed. Through the base electric quantity, generation side electric quantity and consumption side electric quantity, the electricity market assessment and settlement results are obtained. The Monte Carlo method is used to simulate the generation of user-side load scenarios and the corresponding scenario probability, set electric quantity assessment and settlement constraints, verify the actual total electric quantity consumption of all users in the current month, the actual total interactive electric quantity and the actual total exchange market electricity, and obtain the actual total base electric quantity of the current month. Calculate the difference between the actual electric quantity and the planned electric quantity, determine the allowable range of real-time control deviation electric quantity, and use the rolling compensation method to control the electric quantity consumption side. The experimental results show that the proposed model has an error of only 1% for real-time deviation electric quantity control in the regional electricity spot market, and the control time is short.

Mathematical Model and PI Controller Design Based on Indirect Vector Control for Permanent Magnet Synchronous Motor

A mathematical model of a permanent magnet synchronous motor (PMSM) is necessary to design the control of PMSM drives. The mathematical model of a three-phase system is not commonly used for control design since this approach is a time-varying model. As a result, control strategy design becomes even more difficult. Due to this problem, this paper presents a dynamic model of the PMSM using the dq modeling method. The dynamic model derived in this work has been validated with the exact topology model in the MATLAB/Simulink program. In addition, this model is applied to designing the indirect vector control for a PMSM drive. The speed and the current control loops based on the PI controller are considered. The simplified design approach for the PMSM drives is presented in this paper. The simulation results show that the proposed controller design can accurately regulate the actual speed obeying the command speed. The speed accuracy is up to 99.97% in the load torque changes and 99.98% in the command speed changes.

Mathematical Model and PI Controller Design Based on Indirect Vector Control for Permanent Magnet Synchronous Motor

A mathematical model of a permanent magnet synchronous motor (PMSM) is necessary to design the control of PMSM drives. The mathematical model of a three-phase system is not commonly used for control design since this approach is a time-varying model. As a result, control strategy design becomes even more difficult. Due to this problem, this paper presents a dynamic model of the PMSM using the dq modeling method. The dynamic model derived in this work has been validated with the exact topology model in the MATLAB/Simulink program. In addition, this model is applied to designing the indirect vector control for a PMSM drive. The speed and the current control loops based on the PI controller are considered. The simplified design approach for the PMSM drives is presented in this paper. The simulation results show that the proposed controller design can accurately regulate the actual speed obeying the command speed. The speed accuracy is up to 99.97% in the load torque changes and 99.98% in the command speed changes.

ADAPTIVE MODEL PREDICTIVE CONTROLLER FOR TRAJECTORY TRACKING AND OBSTACLE AVOIDANCE ON AUTONOMOUS VEHICLE

Advancement in active steering technology is vital as the autonomous vehicle technology is preparing to enter the commercialization phase. Accurate trajectory tracking and collision free motion have become an active topic being discussed in research field recently. During an emergency obstacle avoidance manoeuvre conditions, tyre force saturation can easily happened when availability of lateral tyre forces is limited by the law of tyre friction circle. This greatly affects the trajectory tracking performance of the vehicle. Existing controllers such as generic model predictive controller (MPC) and geometric controller (Stanley) need a proper gain tuning to cope with this condition. This is due to the control gains were determined by trial and error basis via linearization process at a certain targeted speed. Therefore, the control performance is limited considering the presence of speed variation as well as extreme manoeuvre trajectory. This paper proposes an Adaptive Model Predictive Controller (MPC) controller to solve aforementioned issues. First, optimized weighting gains for the steering control were obtained using PSO algorithm. The optimised weighting gains were then scheduled into the proposed Model predictive Controller via a look-up table strategy. In this work, the proposed adaptive MPC controller was designed by using the linearization of the 7 degree-of-freedom (DOF) non-linear vehicle model. Here, the linearized model for controller design was update based on the instantaneous longitudinal speed of the vehicle system plant.

Coefficient Diagram Method Based Decentralized Controller for Fractional Order TITO Systems

Fractional calculus has gained increasing attention from researchers because of providing accurate modelling and flexible controller design in control applications. More research to design controllers for Fractional Order Two-Input Two-Output (FOTITO) systems, which inherently have certain difficulties, is needed when the studies about these control applications are considered. In this study, Coefficient Diagram Method (CDM) based decentralized controllers are designed for FOTITO systems. For this, integer order approximate models of FOTITO systems are obtained and decoupled into two subsystems by using simplified and inverted decoupling configurations. Obtained high-order approximate subsystem transfer functions are reduced by a model reduction method to facilitate CDM-based decentralized controller design. Then, CDM-based decentralized controllers are designed for each subsystem, which enables to obtain the controllers of the FOTITO system. Simulation results for two different FOTITO systems, one of which is a time delay fractional order system, are demonstrated that the proposed approach exhibits successful performance.

Decentralization Analysis and Control Model Design for PoN Distributed Consensus Algorithm

Decentralization Analysis and Control Model Design for PoN Distributed Consensus Algorithm

Adaptive output regulation of the adaptive optics system

Deformable mirrors are very efficient optical devices that are used in correcting wavefront aberrations and are core elements of adaptive optics systems. Its promising success allows it to operate in a large field of applications. Hence, one of the most important problems in the control of adaptive optics systems is to design a controller that can achieve perfect wavefront correction in completely unknown environmental conditions. To achieve this goal, this study proposes an adaptive internal model control design for deformable mirrors. Considering deformable mirrors as a flexible plate equation, an adaptive internal model-based controller is designed and the effectiveness of the proposed method despite abrupt changes in disturbance is proven by simulation results.

Dynamic modeling of a free-piston engine based on combustion parameters prediction

A dynamic combustion model (DCM) is proposed for model-based control synthesis design in an opposed-piston free-piston engine (FPE). The DCM combines a modified triple Wiebe function with an artificial neural network (ANN), and considers the effects of engine variables such as cylinder wall temperature, fuel injection quantity and ignition timing. Firstly, it was proved by algebraic analysis that the triple Wiebe function can well describe the three combustion stages of the FPE. The ANN was then trained using the data obtained through CFD simulations at different operating points, with the aim of predicting combustion parameters based on engine variables. After the model was calibrated, it was verified by the test results from a prototype. Furthermore, this model was used to investigate combustion control strategies for cold start and stable operation. The results showed that the DCM has high precision and can simulate the complex combustion process under a wide range of FPE conditions, and is a favorable tool to make control strategies without experiments, especially in the cold start phase. Unlike general combustion modeling methods, this study provides a cost-effective way to build such a model for controller design.

A Simplified Modeling Approach of Floating Offshore Wind Turbines for Dynamic Simulations

Currently, floating offshore wind is experiencing rapid development towards a commercial scale. However, the research to design new control strategies requires numerical models of low computational cost accounting for the most relevant dynamics. In this paper, a reduced linear time-domain model is presented and validated. The model represents the main floating offshore wind turbine dynamics with four planar degrees of freedom: surge, heave, pitch, first tower fore-aft deflection, and rotor speed to account for rotor dynamics. The model relies on multibody and modal theories to develop the equation of motion. Aerodynamic loads are calculated using the wind turbine power performance curves obtained in a preprocessing step. Hydrodynamic loads are precomputed using a panel code solver and the mooring forces are obtained using a look-up table for different system displacements. Without any adjustment, the model accurately predicts the system motions for coupled stochastic wind–wave conditions when it is compared against OpenFAST, with errors below 10% for all the considered load cases. The largest errors occur due to the transient effects during the simulation runtime. The model aims to be used in the early design stages as a dynamic simulation tool in time and frequency domains to validate preliminary designs. Moreover, it could also be used as a control design model due to its simplicity and low modeling order.

Recurrent Neural Network-based Internal Model Control design for stable nonlinear systems

Owing to their superior modeling capabilities, gated Recurrent Neural Networks, such as Gated Recurrent Units (GRUs) and Long Short-Term Memory networks (LSTMs), have become popular tools for learning dynamical systems. This paper aims to discuss how these networks can be adopted for the synthesis of Internal Model Control (IMC) architectures. To this end, first a gated recurrent network is used to learn a model of the unknown input-output stable plant. Then, a controller gated recurrent network is trained to approximate the model inverse. The stability of these networks, ensured by means of a suitable training procedure, allows to guarantee the input-output closed-loop stability. The proposed scheme is able to cope with the saturation of the control variables, and can be deployed on low-power embedded controllers, as it requires limited online computations. The approach is then tested on the Quadruple Tank benchmark system and compared to alternative control laws, resulting in remarkable closed-loop performances.

Modeling and robust structural control design for hybrid AC/DC microgrids with general topology

Hybrid AC/DC microgrids are designed to utilize different types of renewable energy sources, meeting the requirements of numerous kinds of loads. A hybrid microgrid consists of a DC sub-grid, an AC sub-grid, and an interfaced sub-grid. In this paper, it is assumed that the DC and AC sub-grids contain several distributed generation (DG) units with arbitrary topology, and the interfaced region consists of several bidirectional interlink converter (IC) units. In the islanded mode of hybrid microgrid operation, one of the key control objectives is to stabilize and regulate the point of common coupling (PCC) voltages of both DC and AC sub-grids and also manage the proportional power-sharing between the sub-grids. This paper proposes a modeling framework and an optimal decentralized output-feedback-based control strategy for the voltage regulation of both DC and AC sub-grids as well as the current adjustment of IC units in a hybrid AC/DC microgrid with general topology. The proposed scheme guarantees robust stability and performance of hybrid microgrids subject to different uncertainty sources. In our proposed modeling setting, the uncertainty sources including plug-and-play (PnP) functionality of DG and IC units, microgrid topology changes, loads variations, and uncertain filter parameters of IC units are modeled as a convex polytope. The control design problem for the polytopic model is converted into a multi-objective convex optimization problem with structural constraints on decision variables. Several scenarios are performed in MATLAB/Simulink to validate the effectiveness of the proposed control mechanism for hybrid AC/DC microgrids.