Automatic landing system for spaceplane based on model predictive control using state mapping

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.

Discussion on: “Improved MPC Design based on Saturating Control Laws”

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.

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

Lithium-ion battery cell-level control using constrained model predictive control and equivalent circuit models

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.

Simulations for the quadratically-constrained model predictive control (qc-MPC) with power system linear models are studied in this work. In qc-MPC, the optimization is imposed with two additional constraints to achieve the closed-loop system stability and the recursive-feasibility simultaneously. Instead of engaging the traditional terminal constraint for MPC, both constraints in qc-MPC are imposed on the first control vector of the MPC control sequence. As a result, qc-MPC has the potential for further extension to the control of network centric power systems. The algorithm of qc-MPC has been developed in a previous paper. Here, simulation studies with small-signal linear models of three typical power systems are presented to demonstrate its efficacy. We also develop a computational strategy for the decentralized static state-feedback control using the same quadratic dissipativity constraint as of the qc-MPC. Only state constraints are considered in the state feedback design. A comparison is then provided in the simulation study of qc-MPC relatively to the constrained-state feedback control.

Further improvements in computational efficiency of numerical optimization algorithms is a promising venue to extend the applicability of model predictive control (MPC) to broader classes of embedded systems with fast dynamics and limited computing resources. Along these lines, we develop a novel numerical optimization algorithm based on integrated perturbation analysis and sequential quadratic programming (IPA-SQP), which exploits special structure of the optimization problem and complementary features of perturbation analysis and SQP methods, to improve computational efficiency in general MPC problems with mixed state and input constraints. An example is reported to illustrate the reduction in on-line computing time achieved with IPA-SQP approach.

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.

Random sub-sampling imaging methods in Atomic Force Microscopy require the piezo X-Y stage to track a sequence of step inputs. Control slew-rate limits combined with linear feedback methods have been shown to limit achievable performance in this scenario. Due to its natural ability to account for actuation constraints, we consider the application of Model Predictive Control. By recasting the problem in an incremental form, the arising quadratic program takes a form that can be solved efficiently. Specifically, we solve an input constrained Model Predictive Control problem with 50 states, a control horizon of 12 samples, and a sample frequency of 25 kHz using the Fast Gradient Method. We present experimental results using the method applied to a nano-positioning stage.