Switching Model Predictive Control Research Articles

Variable-Switching Constant-Sampling Frequency Critical Soft Switching MPC for DC/DC Converters

A variable-switching constant-sampling frequency critical soft switching model predictive control (VSCS-MPC) method is proposed in this paper to improve the dynamic behavior, efficiency and power density of the DC/DC power converters. Firstly, this paper analyzes the boundary constraints of critical soft switching that are derived with the key parameters of the interlock time and threshold current for typical SiC and GaN devices. Then, the VSCS-MPC method is proposed for synchronous DC/DC converter. Both the current source load and resistive load converters are validated with the proposed MPC method. VSCS-MPC includes two controlling parts. First is the frequency controller to maintain the critical soft switching operation by adjusting the switching frequency based on the pre-defined boundary conditions with a constant sampling frequency. A discretized frequency controller is developed to improve the stability of the system by maintaining a fixed sampling frequency. Second part is the model predictive controller to track the output voltage/current and maintain critical soft switching during dynamic periods. The explicit optimization and oversampling methods are applied in the MPC controller to meet the high frequency demand. A large current ripple ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\triangle i_L$</tex-math></inline-formula> <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\ge$</tex-math></inline-formula> 200 %) is introduced to achieve the critical soft switching and reduce the inductance. The switching losses are decreased by the frequency controller and the critical soft switching is maintained especially in dynamic periods due to the fast response of MPC.

Computationally Tractable Stability Criteria for Exogenously Switched Model Predictive Control

Although state-based switching is well-studied in the context of model predictive control (MPC), the theory for exogenous switches is less advanced. External switching affects many systems and is of growing importance due to the increasing use of networked control systems where abrupt changes in communication lines induce switched dynamics. In search of practical means to implement MPC for switched systems, this letter examines techniques to ensure their stability. Several existing methods are summarized and a novel approach is suggested to reduce computational costs while unifying the process for both average and minimum dwell time constraints. Two numerical examples contrast current and previous works.

Dynamic modeling and control strategies of organic Rankine cycle systems: Methods and challenges

Organic Rankine cycle systems are suitable technologies for utilization of low/medium-temperature heat sources, especially for small-scale systems. Waste heat from engines in the transportation sector, solar energy, and intermittent industrial waste heat are by nature transient heat sources, making it a challenging task to design and operate the organic Rankine cycle system safely and efficiently for these heat sources. Therefore, it is of crucial importance to investigate the dynamic behavior of the organic Rankine cycle system and develop suitable control strategies. This paper provides a comprehensive review of the previous studies in the area of dynamic modeling and control of the organic Rankine cycle system. The most common dynamic modeling approaches, typical issues during dynamic simulations, and different control strategies are discussed in detail. The most suitable dynamic modeling approaches of each component, solutions to common problems, and optimal control approaches are identified. Directions for future research are provided. The review indicates that the dynamics of the organic Rankine cycle system is mainly governed by the heat exchangers. Depending on the level of accuracy and computational effort, a moving boundary approach, a finite volume method or a two-volume simplification can be used for the modeling of the heat exchangers. From the control perspective, the model predictive controllers, especially improved model predictive controllers (e.g. the multiple model predictive control, switching model predictive control, and non-linear model predictive control approach), provide excellent control performance compared to conventional control strategies (e.g. proportional–integral controller, proportional–derivative controller, and proportional–integral–derivative controllers). We recommend that future research focuses on the integrated design and optimization, especially considering the design of the heat exchangers, the dynamic response of the system and its controllability.

Discrete-time switching MPC with applications to mitigate resistance in viral infections

Many engineering applications can be described as switched linear systems, in which the manipulated control action is the time-dependent switching signal. In such a case, the control strategy must select a linear autonomous system at each time step, among a finite number of them. Even when this selection can be done by solving a Dynamic Programming (DP) problem, the implementation of such a solution is often difficult and state/control constraints cannot be explicitly accounted for. In this paper, a new set-based Model Predictive Control (MPC) strategy is presented to handle switched linear systems in a tractable form. The optimization problem at the core of the MPC formulation consists of an easy-to-solve mixed-integer optimization problem, whose solution is applied in a receding horizon way. The medical application of viral mutation and its respective drug resistance is addressed to acute and chronic infections. The objective is to attenuate the effect of mutations on the total viral load, and the numerical results suggested that the proposed strategy outperforms the schedule for available treatments.

Periodic constraint‐tightening MPC for switched PV battery operation

Charge–discharge schedules of photovoltaic (PV) batteries are planned based on the prediction of solar irradiance, and various methods have been studied to cope with prediction uncertainty. Among them, the switching of charge–discharge schedules could be a measure for keeping supply–demand balance against abrupt weather changes. Those methods motivate a PV battery operation problem to retain the trackability of the secondary reference even when the primary schedule is ongoing. This study addresses the periodic operation of PV battery systems subject to the reference switching, and designs a switching model predictive control (MPC) to track the primary and secondary references with guaranteed feasibility. The proposed MPC design also incorporates the constraint-tightening technique, by which the operational limits are kept under the prediction error associated with each of the schedules. In order to utilise the design freedom in compensating the prediction error, the authors construct the constraint-tightening gain based on economic dispatching control, and discuss the features in terms of design simplicity and fuel cost efficiency.

Design of Switched Model Predictive Control Algorithms for a Dual-Hormone Artificial Pancreas

Abstract In this paper, we evaluate the closed-loop performance of two switching strategies for a dual-hormone artificial pancreas (AP). The dual-hormone AP administers insulin and glucagon subcutaneously. Since insulin and glucagon have opposite effects, we want to avoid simultaneous injections of these two hormones. To handle non-simultaneous injections of insulin and glucagon, we compare model predictive control (MPC) algorithms using a hysteresis switch between insulin and glucagon controllers with a multiple-input single-output (MISO) formulation. Although the closed-loop performance of these two control strategies is similar, the hysteresis switch is preferable due to (i) its greater flexibility in control design and tuning and (ii) a more straightforward way to avoid simultaneous injections of insulin and glucagon.

Soft Switching Model Predictive Control with An Increase in the Security of Calculations and Information

Soft Switching Model Predictive Control with An Increase in the Security of Calculations and Information

Switching predictive control for continuous-time Markovian jump delay systems

This paper is concerned with switching model predictive control (SMPC) for continuous-time Markovian jump delay systems (MJDSs). First, a piecewise constant switching predictive controller, which only depends on the average dwell time (ADT) switching laws rather than the jumping modes, is obtained by employing the ADT approach under the infinite-time predictive control design framework. Such a control strategy is proposed to make a trade-off between robustness and adaptivity when the design complexity of mode-independent and mode-dependent MPC is considered. It is revealed that the SMPC can deal with MJDSs with both time varying and time invariant jump rates and cover the mode-independent MPC as a special cease. Second, the feasibility of the SMPC scheme and the mean square stability of the closed-loop MJDS are discussed by using the stochastic invariance of the ellipsoid set over each sampling period. A numerical example is given to illustrate the main results.

A Switched Predictive Controller for an Electrical Powertrain System With Backlash

In this paper, a switched model predictive control strategy is applied for the speed regulation of a powertrain system composed of an electrical motor connected to a rotating load through an elastic shaft and a gear, which introduces backlash. The system is described through a piecewise-affine model and a different control law is designed for each system dynamics, which allows obtaining very simple controllers, suitable for digital implementation in embedded systems. A switched Kalman filter is employed in order to estimate the unmeasurable system states. The proposed control strategy is tested through both simulations and experiments, and is compared against a traditional proportional-integral controller.

A multiple model approach for predictive control of indoor thermal environment with high resolution

Realtime and precise control strategies for indoor micro-climate are increasingly needed due to the requirements of thermal comfort and energy consumption saving in recent years. In this paper, a precise control scheme with high resolution is proposed for indoor thermal regulation. Finite volume method discretization/linearization are employed to construct the state-space model of the thermal process. Proper orthogonal decomposition method is further applied for model order reduction. On this basis, a multiple model approach is used to treat the thermodynamic coupling effect and a multi-model switching model predictive control (MPC) scheme is proposed for temperature tracking. Two cases including cooling and warming scenarios are designed, respectively, for performance validation. Results show that compared with the classic MPC method, the developed method can alleviate the model mismatch problem, and facilitate an optimal control of the spatial temperature in the considered ventilation room.

Design of a dual-hormone model predictive control for artificial pancreas with exercise model.

The Artificial Pancreas (AP) is a new technology for helping people with type 1 diabetes to better control their glucose levels through automated delivery of insulin and optionally glucagon in response to sensed glucose levels. In a dual hormone AP, insulin and glucagon are delivered automatically to the body based on glucose sensor measurements using a control algorithm that calculates the amount of hormones to be infused. A dual-hormone MPC may deliver insulin continuously; however, it must avoid continuous delivery of glucagon because nausea can occur from too much glucagon. In this paper, we propose a novel dual-hormone (DH) switching model predictive control and compare it with a single-hormone (SH) MPC. We extended both MPCs by integrating an exercise model and compared performance with and without the exercise model included. Results were obtained on a virtual patient population undergoing a simulated exercise event using a mathematical glucoregulatory model that includes exercise. Time spent in hypoglycemia is significantly less with the DH-MPC than the SH-MPC (p=0.0022). Additionally, including the exercise model in the DH-MPC can help prevent hypoglycemia (p <; 0.001).

Enhancing the yaw stability and the manoeuvrability of a heavy vehicle in difficult scenarios by an emergency threat avoidance manoeuvre

This study aims to investigate the switching model predictive control strategy for a heavy-vehicle system in order to coordinate the actuator between active rear steering and differential braking control manoeuvres for emergency threat avoidance in difficult environments. We present the controller performances for the lateral dynamic behaviour, the yaw stability and the manoeuvrability of a vehicle when subjected to a sudden threat or disturbance such as a gust of wind, a road bank angle or a split- μ road surface in order to enable a fast safe lane-change trajectory to be followed. The vehicle was driven at a medium forward speed and a high forward speed in order to investigate the effectiveness of the proposed approach in avoiding the threat, maintaining the stability and enablinge a fast safe lane-change trajectory to be followed. We compared two different controllers (a model predictive controller and a switching model predictive controller) for two different control manoeuvres (active rear steering with differential braking control and active rear steering with direct yaw moment control). The simulation results demonstrate that the proposed switching model predictive control method provides an improved fast safe lane-change manoeuvre in a threat avoidance scenario for both control manoeuvres. It also demonstrated that the proposed active rear steering with differential braking control is more useful for maintaining the stability of the vehicle in a threat avoidance scenario with disturbance effects than is active rear steering with direct yaw moment control.

Switched model predictive control of switched linear systems: Feasibility, stability and robustness

This paper is concerned with the issues of feasibility, stability and robustness on the switched model predictive control (MPC) of a class of discrete-time switched linear systems with mode-dependent dwell time (MDT). The concept of conventional MDT in the literature of switched systems is extended to the stage MDT of lengths that vary with the stages of the switching. By computing the steps over which all the reachable sets of a starting region are contained into a targeting region, the minimum admissible MDT is offline determined so as to guarantee the persistent feasibility of MPC design. Then, conditions stronger than the criteria for persistent feasibility are explored to ensure the asymptotic stability. A concept of the extended controllable set is further proposed, by which the complete feasible region for given constant MDT can be determined such that the switched MPC law can be persistently solved and the resulting closed-loop system is asymptotically stable. The techniques developed for nominal systems lay a foundation for the same issues on systems with bounded additive disturbance, and the switched tube-based MPC methodology is established. A required “switched” tube in the form of mode-dependent cross section is determined by computing a mode-dependent generalized robust positive invariant set for each error subsystem between nominal subsystem and disturbed subsystem. The theoretical results are testified via an illustrative example of a population ecological system.

Thermal-aware Server Provisioning with Switched MPC for HPC Data Centers

Abstract: This paper aims at implementing dynamic server provisioning and thermal-aware server assignment methods to particular high-performance-computing data centers which processing a large portion of “small size” jobs. A hysteresis server provisioning algorithm is proposed to reactively provide extra idle servers to process unexpected incoming “small size” jobs. Then, we use the thermal-aware server assignment to allocate active servers to high cooling efficiency areas. To address discrete characteristics of server provisioning, a switched model predictive control approach is developed for thermal-aware active servers allocation, and for obtaining the optimal temperature set-points of the cooling system. Simulation results show that significant energy saving can be achieved.

Switched Model Predictive Control for Improved Transient and Steady-State Performance

This work presents a novel switched model predictive control (MPC) formulation for power converters. During transients, the proposed method uses horizon-one nonlinear finite control set (FCS) MPC to drive the system toward the desired reference. When the converter state is close to the reference, the controller switches to linear operation using an approximate converter model and a pulse-width modulation modulator. As an illustrative example, the proposed switched MPC is applied to a flying capacitor converter. As evidenced by experimental results, the proposed control strategy provides quick disturbance compensation, while giving excellent steady-state performance.

Switched Model Predictive Control for Energy Dispatching of a Photovoltaic-Diesel-Battery Hybrid Power System

A new adaptive switched model predictive control (MPC) strategy is designed in this brief for energy dispatching of a photovoltaic-diesel-battery hybrid power system, where the battery is unpermitted to charge and discharge simultaneously. The distinguishing feature of the proposed switched MPC is that, new switched constraints are constructed to describe the different modes (charging and discharging) of the battery, such that the burden of using a switched multiple-input-multiple-output state-space model could be circumvented. Parameters of the battery are unknown constants, and are estimated online with an adaptive updating law. In the switched MPC algorithm, predictive horizon and control horizon vary according to the predefined switching schedule. On the basis of optimization with the switched constraints, receding horizon control is used to obtain the dispatching strategy for the hybrid power system. Performances of the closed-loop system with the proposed switched MPC are verified by simulation results.

Modeling and Control of a Parallel Waste Heat Recovery System for Euro-VI Heavy-Duty Diesel Engines

This paper presents the modeling and control of a waste heat recovery systemfor a Euro-VI heavy-duty truck engine. The considered waste heat recovery system consists of two parallel evaporators with expander and pumps mechanically coupled to the engine crankshaft. Compared to previous work, the waste heat recovery system modeling is improved by including evaporator models that combine the finite difference modeling approach with a moving boundary one. Over a specific cycle, the steady-state and dynamic temperature prediction accuracy improved on average by 2% and 7%. From a control design perspective, the objective is to maximize the waste heat recovery system output power.However, for safe system operation, the vapor state needs to be maintained before the expander under highly dynamic engine disturbances. To achieve this, a switching model predictive control strategy is developed. The proposed control strategy performance is demonstrated using the high-fidelity waste heat recovery system model subject to measured disturbances from an Euro-VI heavy-duty diesel engine. Simulations are performed usinga cold-start World Harmonized Transient cycle that covers typical urban, rural and highway driving conditions. The model predictive control strategy provides 15% more time in vaporand recovered thermal energy than a classical proportional-integral (PI) control strategy. In the case that the model is accurately known, the proposed control strategy performance can be improved by 10% in terms of time in vapor and recovered thermal energy. This is demonstrated with an offline nonlinear model predictive control strategy.

Switched model predictive control for networked control systems with time delays and packet disordering

The switched model predictive control (MPC) strategy is investigated in this paper for a class of networked control systems (NCSs) with time delays and packet disordering. A new model is proposed to describe the NCS with packet disordering. State feedback controllers are considered and the resulting closed-loop NCS is modeled as a discrete-time switched system. By using the average dwell-time approach and switching between different cost functions, sufficient conditions for exponential stability of the NCS and design procedures for the stabilizing controllers are presented. Finally, an illustrative example is given to demonstrate the effectiveness of the proposed method.

Switching model predictive control of a pneumatic artificial muscle

In this article, a switching Model Predictive Controller (sMPC) scheme for the position control of a Pneumatic Artificial Muscle (PAM) is being presented. The control scheme is based on a constrained linear and PieceWise Affine (PWA) system model approximation that is able to capture the high nonlinearities of the PAM and improve the overall model accuracy, and is composed of: a) a feed-forward term regulating control input at specific reference set-points, and b) a switching Model Predictive Controller handling any deviations from the system’s equilibrium points. Extended simulation studies were utilized in order to investigate and evaluate the efficacy of the suggested controller in the positioning problem of a PAM.

Pneumatic artificial muscles: A switching Model Predictive Control approach

In this article, a switching Model Predictive Controller (sMPC) for a pneumatic artificial muscle (PAM) is presented. The control scheme is based on a switching PieceWise Affine (PWA) system model approximation that is able to capture the high nonlinearities of the PAM, while improving the overall model accuracy, and is composed of: (a) a feed-forward term regulating control input at specific reference set-points, and (b) a switching Model Predictive Controller handling any deviations from the system's equilibrium points. Extended experimental studies are being presented that prove the overall scheme's efficiency.