From Linear to Nonlinear Model Predictive Control of a Building

Pčolka, Matej; Žáčeková, Eva; Robinett, Rush D.; Čelikovský, Sergej; Šebek, Michael

doi:10.3182/20140824-6-za-1003.02783

Cited by 5 publications

(4 citation statements)

References 7 publications

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“…However, in the previously published studies [25]- [28], linearized models remain the same within the prediction horizon, referred to as linear-time-invariant (LTI) MPC. In transient cases where state and control trajectories vary significantly, the prediction accuracy can be compromised [29]. Therefore, a linear-timevarying (LTV) MPC based on successive online linearizations within the prediction horizon can be used to enhance the quality of the prediction and the performance of QP-based algorithms.…”

Section: B Literature Reviewmentioning

confidence: 99%

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Integrated Thermal Management of Electric Vehicles Based on Model Predictive Control With Approximated Value Function

Chen,

Kwak,

Kim

et al. 2024

IEEE Access

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The thermal management system (TMS) in electric vehicles (EVs), including climate control and battery thermal regulation, consumes more energy than any other auxiliary components. Therefore, optimizing TMS control is crucial for enhancing EV driving range. However, the complexity of the TMS, described by a differential algebraic system, poses challenges for real-time optimal control. This study proposes model predictive control (MPC)-based solutions for integrated TMS operation in EVs. An optimal thermal management problem is formulated using economic nonlinear MPC (NMPC), and its performance is evaluated. To reduce computational load, an approximated value function (VF) is introduced based on the economic NMPC results. A linear-time-varying MPC (LTV-MPC) with the approximated VF is proposed for real-time implementation using quadratic programming, and through simulations it is compared with the baseline NMPC controller and a rule-based (RB) controller. Results reveal that the LTV-MPC with an approximated VF performs similarly to NMPC while offering slightly compromised cooling performance. It also significantly reduces the computational time by a factor of 10 4 compared with NMPC owing to the short prediction horizon enabled by the approximated VF. Furthermore, when compared with the RB controller, the proposed LTV-MPC achieves energy savings in the range of 22.3% to 29.8%.

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Section: B Literature Reviewmentioning

confidence: 99%

“…It is worth noting that the effectiveness of introducing a first-order filter was demonstrated in our previous work [30]- [32]. The state equations are then expressed as described in (29) to (32).…”

Section: E Nonlinear Prediction Model For Mpc Designmentioning

confidence: 99%

Integrated Thermal Management of Electric Vehicles Based on Model Predictive Control With Approximated Value Function

Chen,

Kwak,

Kim

et al. 2024

IEEE Access

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“…Thus, we speak about a linear model predictive control (LMPC) instead of a nonlinear model predictive control (NLMPC) for the case where a nonlinear process is used. For instance, linearized models were used within an MPC strategy to control a dentrification reactor, building climate, and a counter-current heat exchanger . Laguerre functions can also be used to parametrize NLMPC, to reduce the simulation time and improve the optimization conditions. , …”

Section: Introductionmentioning

confidence: 99%

Model Predictive Control with Integrated Model Reduction for a Continuous Lactide Ring-Opening Polymerization Process

et al. 2022

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Poly(lactic acid) production has received increasing attention, mainly due to its inherent biodegradable thermoplastic properties and to its renewable-resource-based composition. This process is affected by changes in the operating conditions and by raw material impurities which influence the reaction rate and degrade the polymer properties. As the system model is multivariable with coupled dynamics and constraints, linear model predictive control (LMPC) is employed here. A model reduction technique is proposed to obtain an approximate linear representation of the nonlinear system around the operating point to minimize the calculation cost of the controller. The proposed LMPC approach is validated by simulation and is compared to a proportional-integral controller and a nonlinear model predictive control. It is found that LMPC has a superior performance in terms of off-spec time when a disturbance occurs in the feed, and it can restore the target conditions better and faster.

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“…However, in the previous studies, 8,20‐22 the linearized model remains the same within the prediction horizon. In transient cases where state and control trajectories could significantly vary, the performance of the LTI‐MPC can be degraded due to the inaccurate prediction of the LTI model 23 . Thus, a linear‐time‐varying (LTV)‐MPC, which constructs the linearized prediction model multiple times within the prediction horizon, is utilized in this article to enhance the quality of the prediction and the performance of the QP‐based optimal cabin climate control of EVs.…”

Section: Introductionmentioning

confidence: 99%

Energy‐efficient cabin climate control of electric vehicles using linear time‐varying model predictive control

Chen

Kwak

Kim

et al. 2021

Optim Control Appl Methods

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A cabin climate control system, often referred to as a heating, ventilation, and air conditioning (HVAC) system, is one of the largest auxiliary loads of an electric vehicle (EV), and the real-time optimal control of HVAC brings a significant energy-saving potential. In this article, a linear-time-varying (LTV) model predictive control (MPC)-based approach is presented for energy-efficient cabin climate control of EVs. A modification is made to the cost function in the considered MPC problem to simplify the Hessian matrix in utilizing quadratic programming for real-time computation. A rigorous parametric study is conducted to determine optimal weighting factors that work robustly under various operating conditions. Then, the performance of the proposed LTV-MPC controller is compared against a rule-based (RB) controller and a nonlinear economic MPC (NEMPC) benchmark. Compared with the RB controller benchmark, the LTV-MPC reaches the target cabin temperature at least 69 s faster with 3.2% to 15% less HVAC system energy consumption, and the averaged cabin temperature difference is 0.7 • C at most. Compared with the NEMPC, the LTV-MPC controller can achieve comparable performance in temperature regulation and energy consumption with fast computation time: the maximum differences in temperature and energy consumption are 0.4 • C and 2.6%, respectively, and the computational time is reduced 72.4% on average with the LTV-MPC.

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From Linear to Nonlinear Model Predictive Control of a Building

Cited by 5 publications

References 7 publications

Integrated Thermal Management of Electric Vehicles Based on Model Predictive Control With Approximated Value Function

Integrated Thermal Management of Electric Vehicles Based on Model Predictive Control With Approximated Value Function

Model Predictive Control with Integrated Model Reduction for a Continuous Lactide Ring-Opening Polymerization Process

Energy‐efficient cabin climate control of electric vehicles using linear time‐varying model predictive control

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