비선형 타이어모델 기반 MPC를 이용한 차량 안정화

Advanced vehicle dynamics control systems, such as integrated chassis control systems, are generally designed in a two layers structure: a tire force determination layer, and a tire force generation layer. The tire force generation layer works as a servo function that follows the force commands from the force determination layer. Therefore, the tire force generation layer should not require a lot of computing resources. This paper presents an inverse tire model that calculates required slip ratios and slip angles when tire forces are commanded. The inverse tire model is based on a nonlinear tire model that is sufficiently complex and, thus, can capture the nonlinear characteristics of tires. Because mathematical inversion of the nonlinear tire model is challenging, recursive methods can be considered for an inverse model. However, recursive methods, such as Newton Raphson method, do not guarantee the fixed time span required for finishing the computation. This paper presents a method for inverse tire model that does not require recursive computations.

This paper presents a review of the design and application of model predictive control strategies for Micro Aerial Vehicles and specifically multirotor configurations such as quadrotors. The diverse set of works in the domain is organized based on the control law being optimized over linear or nonlinear dynamics, the integration of state and input constraints, possible fault-tolerant design, if reinforcement learning methods have been utilized and if the controller refers to free-flight or other tasks such as physical interaction or load transportation. A selected set of comparison results are also presented and serve to provide insight for the selection between linear and nonlinear schemes, the tuning of the prediction horizon, the importance of disturbance observer-based offset-free tracking and the intrinsic robustness of such methods to parameter uncertainty. Furthermore, an overview of recent research trends on the combined application of modern deep reinforcement learning techniques and model predictive control for multirotor vehicles is presented. Finally, this review concludes with explicit discussion regarding selected open-source software packages that deliver off-the-shelf model predictive control functionality applicable to a wide variety of Micro Aerial Vehicle configurations.

Model Predictive Control In Solar Trough Plants: A Review

In this paper, a centralized model predictive control (MPC) is applied on a group of interconnected microgrids (MGs) with the main grid. The objective is to maximize the benefits for all the elements constituting the MGs in addition to the benefits of the main grid. The application of MPC in our study needs a forecasting information about energy prices, production power, and loads. The algorithm is tested on five interconnected MGs connected to the main grid. Results have shown the performance of the proposed algorithm, especially for the benefits of MG owners, the coordination between MGs while respecting of the constraints related to each one of them.

The tire forces of vehicles will fall into the non-linear region under extreme handling conditions, which cause poor path tracking performance. In this paper, a model predictive controller based on a nonlinear tire model is designed. The tire forces are characterized with nonlinear composite functions of the magic formula instead of a simple linear relation model. Taylor expansion is used to linearize the controller, the first-order difference quotient method is used for discretization, and the partial derivative of the composite function is used for matrix transformation. Constant velocity and variable velocity conditions are selected to compare the designed controller with the conventional controller in Carsim/Simulink. The results show that when the tire forces fall in the nonlinear region, two controllers have good stability, and the tracking accuracy of the controller designed in this paper is slightly better. However, after the tire forces become nonlinear, the controller with linear tire force becomes worse, the tracking accuracy is far worse than the controller with the nonlinear tire model, and the vehicle stability is also degraded. In addition, an active steering test platform based on LabVIEW-RT is established, and hardware-in-the-loop tests are carried out. The effectiveness of the designed controller is verified.

Japan’s rapidly aging population and shrinking workforce are creating serious challenges, especially in jobs that require long hours outdoors. To solve this, Japan urgently needs innovative solutions, including automation technologies for outdoor work such as farming, construction, and maintenance. One promising approach is the application of model predictive control (MPC) to outdoor mobile robots. Although MPC has been widely studied in the context of mobile robotics, there remains a paucity of practical research specifically targeting cleaning robots operating in outdoor environments. Alternative approaches, such as geometric path-following methods like pure pursuit, are frequently employed in simpler applications, but often encounter limitations in achieving high-precision trajectory tracking. This study proposes a model predictive contouring control (MPCC) framework for trajectory tracking in outdoor cleaning robots. The proposed method offers the capability to balance the trade-off between execution time and trajectory accuracy. Both simulation and experimental results validate the effectiveness of the proposed MPCC approach.

Energy efficient model predictive building temperature control

Hybrid actuators can provide better performances than hydraulic or electromechanical actuators alone; however, control of these hybrid systems is more challenging. This paper presents the application of model predictive control (MPC) on a hybrid actuator system to coordinate multiple control objectives. MPC has advantages over traditional controllers such as a PID in controlling non-linear multi-input, multi-output systems. MPC proactively updates the control inputs in a specific time interval by observing the current state of the system, predicting the future behavior, and solving an optimization problem repeatedly with consideration of pre-defined control objectives. In this paper, a high-fidelity dynamic model of the hybrid actuator system will be developed and a simplified, reduced-order model will be derived for use in the MPC formulation process. The detailed process of designing an MPC controller, which may consist of mathematical formulation of an optimization problem and numerical methods to find the optimal solution, will be discussed in detail. Simulation results will be presented to compare the performances of the MPC with PID controllers, and the effects of different controller parameters on the system performances will be discussed.

Model predictive control (MPC) is an efficient and growing approach to power converter control. This paper proposes an improved and simplified model predictive current control (MPCC) technique for a four-level nested neutral point clamped (4L-NNPC) converter. Conventional MPCC exhibits better performances as compared to the conventional linear control system such as fast dynamic response, consideration of the system constraints, and nonlinearities. However, the application of the conventional model predictive current control (MPCC) approach on complex systems provokes a significant number of calculations, which is the main hurdle to its practical implementation. To fix this flaw, this paper proposes an effective algorithm to shorten the execution time of the conventional MPCC. In this proposed technique, 216 current predictions of the conventional MPCC are skipped and converted into one required voltage vector (RVV) prediction. With this equivalent reference voltage transformation, the calculation burden of MPCC is significantly reduced, while the output performance is not influenced. The results of the simplified MPCC for the 4L-NNPC converter are analyzed and compared with the conventional MPCC. The computational time is reduced by 19.56% using the simplified MPCC, while keeping an approximately similar error of output currents. The switching frequency and total harmonic distortion (THD) of the proposed method are reduced by 8.16% and 0.07%, respectively, as compared to the conventional technique. These results demonstrate the fact that that the performance of a conventional MPCC is enhanced with the proposed MPCC. The proposed algorithm can be applied to several inverter topologies.

Optimisation algorithms offer a valuable tool for enhancing the energy efficiency of buildings by fine-tuning specific design parameters. Model Predictive Control (MPC) emerges as a compelling technology to address the rising need for improved efficiency and adaptable operation within building energy systems. A simulation study using MATLAB and EnergyPlus was conducted to examine the influence of MPC and building design optimisation (BDO) on wall insulation thickness and internal mass area. The focus was on their impact on heating energy use and indoor thermal comfort in an office room located in Brussels, Belgium. The study revealed that the sole implementation of MPC led to a 7.6% decrease in heating energy use, while the application of BDO resulted in a more significant reduction of 12.8%. Remarkably, the fusion of MPC and BDO yielded the highest energy savings, cutting heating electricity usage by 23.4% compared to the baseline model. Moreover, MPC effectively maintained indoor temperature within the desired thermal comfort boundaries. The optimal wall insulation thickness and internal mass area were also ascertained through BDO, both of which exceeded the levels set by the baseline model. BDO, in conjunction with MPC, demanded the maximum permissible insulation thickness of 320 mm for the external north and south walls. Interestingly, when BDO was combined with MPC, the requirement for the internal mass area reduced by 11.7 m2 compared to utilising BDO alone. The study’s results underscore the potential of integrating MPC with BDO to elevate building energy efficiency. Furthermore, this strategy may be adaptable to optimising other building parameters at the early design stage, thereby augmenting overall building energy efficiency.

This research presents the application of model predictive control (MPC) to control a proton exchange membrane fuel cell (PEMFC). Firstly, the steady state analysis of PEMFC is considered to select its suitable operating conditions based on cell electrical characteristics. Then, the effect of input parameters on cell voltage and temperature is analyzed to investigate the dynamic behavior of PEMFC that is important for control design. It is found that the cell voltage and cell temperature depend on the inlet molar flow rates and temperature of hydrogen and air, and operating current density. To obtain an efficient control system, the control structure design of the PEMFC is considered to specify a good choice of the controlled and manipulated variables. An analysis of the steady-state relative gain array (RGA) is used for pairing of the controlled and manipulated variables. The result shows that the inlet molar flow rates of hydrogen and air are manipulated variables to regulate the cell temperature and partial pressure of hydrogen, respectively. Finally, a model predictive control (MPC) is developed and designed for controlling the cell temperature and partial pressure of hydrogen. Basically, MPC requires the process model used in its control algorithm. The PEMFC model is known to be complicated and involves uncertain parameters. Thus, an offline robust model predictive control (robust MPC) based on a linear time-varying (LTV) model is proposed for PEMFC control. The simulation results show that the robust MPC shows better control performance than conventional MPC because the robust MPC can guarantee robust stability.

This paper presents a case study concerning the application of model predictive control (MPC) to the long term hydrothermal scheduling of the Brazilian power system. According to MPC, the hydro and thermal generation decisions at each stage are provided by a deterministic nonlinear optimization model considering predicted inflows. The model, which is solved by interior point method, also takes into account tie line constraints between interconnected areas. In order to evaluate the performance of the approach several simulations over historical inflow scenarios were performed, and statistics about operation costs, hydro and thermal generation, power flow interchange, reservoir storage, load shortage, among others, are obtained. General results are compared to those from the stochastic model in use in Brazil and the results have shown substantial decrease in expected operation costs and load shortages, as well as an increase on water storage, both cause and effect of the better management of water resources.

In model predictive control, the control action is found at each sampling time by solving an online optimization problem. Computationally, this step is very demanding, especially if compared to the evaluation of traditional control laws. This has limited the application of model predictive control to systems with slow dynamics for many years. Recently, several methods have been proposed in the literature which promise a substantial reduction of the computation time by either running the computation in parallel (distributed model predictive control) or exploiting the problem structure (fast model predictive control). A combination of these methods has not yet been considered in the literature. To achieve this goal, different optimization techniques need to be employed at once. The order of how these methods are applied matters. This paper considers fast distributed model predictive control combining the alternating direction method of multipliers (ADMM), the interior point method (IPM) and the Riccati iteration for a particular class of multi-agent systems for which the order of the methods can be arbitrarily changed. This leads to two different solver schemes where a trade-off arises between computation time and number of communications required to reach consensus. A simplified problem involving the formation control of a fleet of vehicles is considered at the end.

In this paper the application of model predictive control (MPC) to a two-mode model of the dynamics of the combustion process is considered. It is shown that the MPC by itself does not stabilize the combustor and the control gains obtained by applying the MPC algorithms need to be optimized further to ensure that the phase difference between the two modes is also stable. The results of applying the algorithm are compared with the open loop model amplitude responses and to the closed loop responses obtained by the application of a direct adaptive control algorithm. It is shown that the MPC coupled with the cost parameter optimisation proposed in the paper, always guarantees the closed loop stability, a feature that may not always be possible with an adaptive implementations.

This paper presents a preliminary assessment of the potential economic benefit resulting from the use of smart energy management systems in buildings. The proposed approach relies on the application of model predictive control (MPC) and a self-consumption oriented tariff scheme. In order to evaluate the advantages of MPC within the context of future energy scenarios, an e-constraint multi-objective optimization of different energy system configurations and their respective operation strategies is performed, prior comparing the former performance to a conventional control method. The problem formulation is expressed using a mixed-integer linear programming (MILP) technique featuring grid interaction constraints and heat integration. The generated simulation results are finally extended to the domestic building stock of Switzerland using a systematic approach, hence providing a first estimate of the large-scale impact of MPC though national performance maps. © 2017 IEEE.