Model Predictive Control Research Articles

Artificial Neural Networks for Energy Demand Prediction in an Economic MPC‐Based Energy Management System

ABSTRACTMicrogrids are a development trend and have attracted a lot of attention worldwide. The control system plays a crucial role in implementing these systems and, due to their complexity, artificial intelligence techniques represent some enabling technologies for their future development and success. In this paper, we propose a novel formulation of an economic model predictive control (economic MPC) applied to a microgrid designed for a faculty building with the inclusion of a predictive model to deal with the energy demand disturbance using a recurrent neural network of the long short‐term memory (RNN‐LSTM). First, we develop a framework to identify an RNN‐LSTM using historical data registered by a smart three‐phase power quality analyzer to provide feedforward power demand predictions. Next, we present an economic MPC formulation that includes the prediction model for the disturbance within the optimization problem to be solved by the MPC strategy. We carried out simulations with different scenarios of energy consumption, available resources, and simulation times to highlight the results obtained and analyze the performance of the energy management system. In all cases, we observed the correct operation of the proposed control scheme, complying at all times with the objectives and operational restrictions imposed on the system.

Landing Guidance Control Combining Powered Descending with Nonlinear Optimization and Vertical Descending with Model Prediction

In this study, we propose a landing guidance control method for the Moon using two control methods. The landing of a spacecraft on the Moon is divided into two phases: the powered descending phase and the vertical descending phase. The powered descending phase aims to optimize the entire trajectory to satisfy the termination constraint, instead of allowing for a relatively long control period. This phase performs feedback control using trajectory updates via nonlinear optimization. The vertical-descending phase aims to achieve accurate control over a short control period. This phase applies nonlinear model predictive control for fast optimization on finite time intervals. First, the landing of a spacecraft on the Moon is modeled as a two-body problem of the Moon and spacecraft, and equations of motion are derived. Subsequently, we formulated an optimization problem for each phase and developed the proposed landing guidance method by combining the two control methods. Furthermore, numerical simulations based on the derived equations of motion were performed to confirm the effectiveness of the proposed method and to compare its performance with another optimization method, the successive convexification (SCvx) algorithm.

Unit prediction horizon [formula omitted] based model predictive control for the fuel cell based plug-in hybrid electric vehicle with rule-based energy management system

Plug-in hybrid electric vehicles (PHEVs) provide a good alternative in achieving better performance and in the reduction of harmful gas emissions. The hybrid energy storage system (HESS) and the integrated charging unit constitute the PHEV under consideration. To meet the load demands, the proposed HESS is a coupled system comprising a fuel cell, high energy density battery, and high-power density super-capacitor. On-board charging involves the utilization of a DC–DC buck converter and an uncontrolled rectifier and two buck-boost converters are utilized to facilitate a smooth transfer of energy. A rule-based supervisory controller has been implemented for different load conditions which takes into account the state of charge of energy sources and also the total power inflow of the power sources. A model predictive control (MPC) technique is implemented to ensure that PHEVs function smoothly in terms of regulation of DC bus voltage and tracking of current. Unit prediction horizon i.e. only one step ahead looking into the future is considered for the MPC to make it computationally less expensive. MPC is designed in such a way that its performance is close to our favorite linear controller which in our case is the H∞ controller. The inverse optimal control technique is used for determining the weight matrices of the cost function. The proposed controller has been simulated using MATLAB. Also, the performance comparison is made between the designed MPC and the non-linear control approach i.e. integral sliding mode control and integral backstepping-based control to show that it achieves comparable or superior performance to the non-linear controller. The real-time performance of H∞ based MPC is verified using controller hardware in the loop experimental setup.

MPC-Based Dynamic Trajectory Spoofing for UAVs

Navigation spoofing has been widely utilized in unmanned aircraft vehicle (UAV) countermeasures, due to its advantages of covertness, effectiveness, and dynamic trajectory control ability. However, existing research faces two primary challenges. Firstly, sudden changes in the target UAV’s trajectory can result in a significant degradation in the spoofing performance, which may enable the onboard inertial components to detect and identify the ongoing spoofing attempts. Secondly, gradual accumulation of control errors over time degenerates the spoofing effect. To address these problems, we propose a dynamic trajectory spoofing approach for UAVs based on model predictive control (MPC), which progressively steers the UAVs towards the predetermined trajectory of the spoofer. Simulation results demonstrate a substantial enhancement in dynamic trajectory control performance and decrease in accumulation error compared to the existing methods.

Disturbance‐rejection adjacent vector model predictive control strategy based on extended state observer for EV converter

AbstractConventional single‐vector model predictive control (MPC) can suffer from low control accuracy, while multi‐vector MPC is often criticized for its complexity and heavy computational burden. In order to address these issues, an adjacent vector‐based MPC is investigated in this paper for an electric vehicle battery charging and discharging converter. The voltage vector selection table based on the principle of using adjacent vectors has been designed and this reduces the number of iterations and thus the computational burden. A threshold is used in the adjacent vector‐based MPC to coordinate the use of the single and multi‐vector MPCs considering a balance between the control accuracy and computational burden. In addition, to enhance the robustness of MPC to parameter changes, an extended state observer for active disturbance rejection control has been used to derive the predictive model, and an adjacent vector‐based MPC using extended state observer is studied. The method does not need accurate system parameters. Instead, it only requires the system input and output measurements to calculate the predicted current. The robustness of the controller against the parameter mismatch is enhanced compared to alternative approaches and the experimental results verify the feasibility and effectiveness of the proposed strategy.

A model predictive control algorithm for double-hormone artificial pancreas based on optimal three-branch decision switching rules

At present, double-hormone artificial pancreas is a hotspot in the research of regulating blood glucose level; however, there are few studies on switching mechanism of the subsystems for double-hormone artificial pancreas, and most of the existing studies rely on the prior knowledge of application fields. A model predictive control algorithm for double-hormone artificial pancreas based on optimal three-branch decision switching rules is proposed to solve this problem. The blood glucose controllers of insulin subsystems, zero infusion subsystems, and glucagon subsystems are constructed using model predictive control algorithms. Then, using the three-branch decision theory, switching rules between the three subsystems are designed, and the optimal switching threshold value can be solved by the mixed fruit fly algorithm. The algorithm can realize automatic acquisition of the threshold in the three-branch decision switching rules and gets rid of the dependence on the prior knowledge in the field of diabetes research. By the end, the UVa simulation platform is used to conduct experiments on 33 virtual patients and the simulation results verify that the proposed algorithm can control blood glucose effectively within the normal range and can achieve satisfactory performabce in error of tracking set point, safety, operation efficiency, group control quality, and system stability.

Real-time robust nonlinear model predictive control with monotonically increasing weight for quadruped locomotion

Real-time robust nonlinear model predictive control with monotonically increasing weight for quadruped locomotion

Control of an integrated first and second-generation continuous alcoholic fermentation process with cell recycling using model predictive control

This study explores controlling a first- and second-generation alcoholic fermentation process with cell recycling, modeled through algebraic differential equations (DAE) with constraints. It evaluates seven controller types: PI, PID, Model Predictive Control (MPC), Neural Network Model Predictive Control (NNMPC), Mixed Model Predictive Control (MMPC), Internal Model Control (IMC), and Linear Quadratic Regulator (LQR). Results show that, while PI and PID controllers can track setpoints, they exhibit slow responses and oscillations. In contrast, MPC controllers respond faster, with both MPC and MMPC demonstrating more robust dynamics, achieving a significant reduction in Integral of the Absolute Error (IAE) across various disturbances, notably an 87% reduction in regulatory scenarios. NNMPC outperforms PI and PID but exhibits overshoot and oscillations, and lacks robustness for servo-type problems. However, MMPC showcases comparable or superior performance to MPC, surpassing other controllers in robustness, especially the IMC and LQR in the regulatory problems. NNMPC maintains a simulation time of under 4 seconds, whereas MPC incurs a computational cost 1,000 times higher. Integrating NNMPC’s optimal increment as an initial estimate in MPC reduces computational time by up to 79.7%. These findings highlight the ANN’s effectiveness in addressing complex control challenges, especially when integrated with MPC. MMPC offers a superior balance between accuracy, robustness, and computational efficiency, serving as a promising solution for reducing computational costs in MPC-type controllers.

A Fractional-Order Model Predictive Control Strategy with Takagi–Sugeno Fuzzy Optimization for Vehicle Active Suspension System

Aiming at the problem of system controller performance failure caused by improperly setting the value of each weighting coefficient of the model predictive control (MPC), a fractional-order MPC strategy with Takagi–Sugeno fuzzy optimization (T–SFO MPC) is proposed for a vehicle active suspension system. Firstly, the fractional-order model predictive control framework for active suspension systems is designed based on a 1/4 vehicle model. Then, we analyze the influence of different weighting coefficients on the suspension performance and introduce the Takagi–Sugeno fuzzy optimization theory to adaptively adjust the weighting coefficients of the fractional-order MPC controller. Finally, the system responses of the T–SFO MPC, traditional MPC, linear quadratic regulator (LQR), and passive suspension control are numerically analyzed under various road conditions. Simulation results show that suspension response with the T–SFO MPC is significantly improved compared with passive suspension control, traditional MPC control, and LQR control, and the weight coefficients of the T–SFO MPC can be adaptively adjusted according to the dynamic changes of suspension response. Compared with passive suspension, the root mean square (RMS) value of the vertical acceleration of the T–SFO MPC under various roads decreased by a maximum of 37.97%, and the RMS value of suspension dynamic deflection and tire dynamic load decreased by a maximum of 32.94% and 37.8%, respectively. These results validate that the proposed control method can achieve coordinated optimization of vehicle comfort and handling stability.

Efficient and Lightweight Model-Predictive IoT Energy Management

Multi-sensor IoT devices often rely on renewable energy and batteries to support diverse field deployments. A device’s sensors use significant energy, so careful energy management is needed to maximize its operating time while meeting application requirements. Model Predictive Control (MPC), a successful energy management technique for other application domains, requires significant computational resources usually not available in typical IoT sensing devices. We develop and evaluate a low-complexity approximation to MPC for IoT device operations powered by renewable (solar) energy, where the approximation is guided by the charging characteristics of real batteries. The complex MPC optimization problem is solved by decomposing it into a time-dependent energy allocation problem, and a task-dependent sensor scheduling problem, each of which is solvable with cubic time complexity. We utilize a novel combination of an incremental max-min fair allocation method and a recursive dynamic programming-like procedure. This low-complexity predictive optimization step is integrated with a very simple parabola-based adaptive solar prediction to provide a full system solution, termed Predictive EneRgy Management for IoT (PERMIT) , that can be implemented in lightweight IoT devices. PERMIT is evaluated through experiments on a solar-powered Raspberry Pi device with multiple sensors. We also complement our evaluations with simulations based on real device data traces. Results show that PERMIT’s low-complexity algorithm approximates the exact MPC solution very closely. PERMIT also performs significantly better than Signpost, a comparable IoT energy management solution.

Research on an improved deadbeat model predictive current control of double three-phase open-winding permanent magnet motor

Model predictive current control has high control accuracy and good control performance, and has been widely applied in the control strategy of dual three-phase permanent magnet synchronous motors. This article proposes an improved deadbeat model predictive current control (DMPCC) method by analyzing the mathematical model of a dual three-phase open-winding permanent magnet synchronous motor. One inverter predicts and calculates the optimal voltage vector through the deadbeat model, while the other inverter calculates the optimal voltage vector through the value function rolling optimization. This method reduces the computational workload of the controller, avoids the delay problem of the traditional model predictive current control (MPCC) method, improves the system's response speed, effectively suppresses the current in the harmonic sub-plane, and reduces torque ripple. The simulation results have verified the accuracy and speed of the above control method.

Model predictive control for space robot manipulator modeled by switched systems: A data‐driven approach

AbstractThis article investigates the data‐driven based model predictive control (MPC) problem for a class of space robot manipulator (SRM). First, the SRM system is modeled as a class of discrete‐time switched system to reflect more system characteristics. Second, only the input‐state data are used for end‐to‐end controller design, and the system identification process is avoided. In order to meet the real‐time requirements of the manipulator arm angles, a kind of constrained predictive control method is designed to prevent the overheating or damage of SRM motor, optimizing the cost function for each decision cycle under the safe control input constraints. Then, based on the average dwell time method, the asymptotic stability is assured for the presented SRM system. The design conditions for data‐driven based MPC are provided in the form of linear matrix inequalities. Finally, the effectiveness and advantage for the developed data‐driven based MPC strategy are validated via a case study of SRM system.

Non-Periodic Quantized Model Predictive Control Method for Underwater Dynamic Docking

This study proposed an event-triggered quantized model predictive control (ETQMPC) method for the dynamic docking of unmanned underwater vehicles (UUVs) and human-occupied vehicles (HOVs). The proposed strategy employed a non-periodic control approach that initiated the non-linear model predictive control (NMPC) optimization and state sampling based on tracking errors and deviations from the predicted optimal state, thereby enhancing computing performance and system efficiency without compromising the control quality. To further conserve communication resources and improve information transfer efficiency, a quantitative feedback mechanism was employed for sampling and state quantification. The simulation experiments were performed to verify the effectiveness of the method, demonstrating excellent docking trajectory tracking performance, robustness against bounded current interference, and significant reductions in computational and communication burdens. The experimental results demonstrated that the method outperformed in the docking trajectory tracking control performance significantly improved the computational and communication performance, and comprehensively improved the system efficiency.

Obstacle avoidance control of UAV formation based on distributed model prediction

Aiming at the formation and maintenance problem of UAVs in an obstacle environment, a distributed model predictive control (DMPC) algorithm with no reference trajectory considering system constraints is proposed. In order to deal with the constraint coupling and cost coupling existing in model predictive control (MPC), the assumed trajectory is introduced to design a low conservative compatibility constraint and a cost function of no reference trajectory, so that the algorithm can be executed in a distributed and synchronous manner. Secondly, the terminal constraint is designed based on the speed barrier method to ensure the safety of the terminal domain, and a feasible terminal control input is given. The cost function is taken as a Lyapunov function, combined with the constructed stability constraint, and the iterative feasibility and system stability of the algorithm are analyzed. In addition, in order to take into account the real-time performance, a non-strictly stable DMPC algorithm that can better meet the requirements of formation obstacle avoidance is given on the basis of the proposed algorithm. The effectiveness and superiority of the proposed algorithm are verified by numerical simulation.

Gust load alleviation of a flexible flying wing with linear parameter-varying modeling and model predictive control

This paper presents a practical model predictive control (MPC) framework for gust load alleviation of a flexible flying wing. Both the controller solving and state estimation are based on a reduced-order model, which features a linear parameter-varying (LPV) form, avoiding online linearization and reducing the scale of the corresponding quadratic programming problem. An improved modeling and model reduction process is used to enhance modeling efficiency and ensure that the reduced-order model can accurately capture the rigid-flexible coupled characteristics of the flexible flying wing under arbitrary gusts. By reconstructing the output of the control-oriented model to include both rigid-body motion and flexible vibrations, the rigid-flexible coupled multi-objective control is established as an MPC problem for reference tracking. The online optimization is formulated in a sparse fashion and combined with an iterative algorithm based on predicted trajectories, describing the variation of model dynamics within the prediction horizon more accurately. With a time-varying Kalman estimator for state updating, the closed-loop simulations are performed for gust alleviation performance validation. Additionally, the real-time potential of the proposed MPC framework is demonstrated through Monte Carlo simulations.

Adaptive MPC path-tracking controller based on reinforcement learning and preview-based PID controller

Path-tracking control is a crucial process for autonomous vehicles, ensuring that the vehicle drives safely along the reference path, and the suitable controller parameters ensure the accuracy and stability of this process. To enhance the adaptability of traditional path-tracking controller parameters, this paper proposes an adaptive model predictive control (MPC) controller based on a preview-based PID controller and deep deterministic policy gradient (DDPG) algorithm to achieve adaptive tuning of the controller parameters. Starting with the design of the dynamics tracking error model of the vehicle and the MPC controller. Based on the actor-critic reinforcement learning architecture, the DDPG agent is designed to tune the prediction horizon and weight matrix of the MPC controller. A preview-based PID controller is proposed to improve the efficiency and stability of reinforcement learning and compensate for the error in vehicle modeling. The improved algorithm performance is verified through the simulation scenarios of high-speed lane changing and accelerated overtaking scenarios constructed by MATLAB/Simulink. The results show that the improved algorithm significantly improves the adaptive ability of the traditional MPC controller to time-varying conditions and achieves higher tracking accuracy and stability.

Control system design for azimuth position of earth station antennas

Smart earth station antennas have been used for several decades in many applications, from satellite communications to space object detection and tracking. The accuracy of the azimuth position for such antennas plays a crucial role in most steerable ground station antennas. Satellite tracking and space object detection demand precise tracking capabilities from the Earth. Several methods and techniques have been developed and used in industry to control the directions of ground station antennas, including the azimuth position. The challenge of azimuth tracking is increasing with the demand for full-sky coverage and with the exponential increase in space objects, including man-made satellites and operational and nonoperational objects; thus, providing accurate tracking is a key technology that demands continuous enhancement and development. This article presents the use of a PID-proportional-integral-derivative controller, a slide mode controller and a fractional order PID controller. It also introduces a new methodology based on model predictive control (MPC). The manuscript provides the core design for each of these controllers and provides insight into the performance of each controller even in the presence of disturbance. The camel optimization algorithm (COA) was used to obtain the optimal design parameters of each controller in the considered scenarios.

An energy trade-off management strategy for hybrid ships based on event-triggered model predictive control

This paper addresses the energy management problem of hybrid ships by proposing an event-triggered model predictive control (ET-MPC) method. The novelty in this work lies in the establishment of an event-triggered mechanism and a state prediction model for energy management of hybrid ships. First, torque models of the internal combustion engine (ICE) and electric machine (EM) are developed using a data-driven approach, followed by the construction of fuel consumption and carbon emission models. Second, an event-triggered mechanism, dependent on state prediction error, is introduced and updated at each time step based on the system’s current state. Additionally, a cubature Kalman filter (CKF) is employed to estimate and correct the state prediction error, minimizing inaccuracies. A trade-off coefficient is incorporated to optimize the balance between fuel consumption and carbon emissions. The ET-MPC method results in a 0.68% difference in fuel consumption and 3.43% increase emissions compared to the traditional MPC method. However, ET-MPC significantly reduces computational overhead by 56.66. The ET-MPC method effectively allocates the ship’s energy according to the varying trade-off coefficient, achieving optimal energy management under different constraints.

An Overview of Systems-Theoretic Guarantees in Data-Driven Model Predictive Control

The development of control methods based on data has seen a surge of interest in recent years. When applying data-driven controllers in real-world applications, providing theoretical guarantees for the closed-loop system is of crucial importance to ensure reliable operation. In this review, we provide an overview of data-driven model predictive control (MPC) methods for controlling unknown systems with guarantees on systems-theoretic properties such as stability, robustness, and constraint satisfaction. The considered approaches rely on the fundamental lemma from behavioral theory in order to predict input–output trajectories directly from data. We cover various setups, ranging from linear systems and noise-free data to more realistic formulations with noise and nonlinearities, and we provide an overview of different techniques to ensure guarantees for the closed-loop system. Moreover, we discuss avenues for future research that may further improve the theoretical understanding and practical applicability of data-driven MPC.

Tuning of modern speed drives using IFOC: A case study for a five-phase induction machine

Electric machines have existed since the beginning of the 19th century. Although variable-speed drive technology has evolved considerably during the last years, the indirect field-oriented control technique has been in use as a standard control method since the 1960s, using an outer speed loop and an inner current loop. This paper deals with the non-trivial problem of tuning the outer speed controller of modern variable-speed drives, where experimental results are provided to show the need for new tuning methods. The influence of non-modeled effects on the performance of the drive is illustrated considering the dynamical effect of the mechanical speed sensing procedure using optical devices. This effect has not been generally considered. However, it is shown that it has a notable effect on tuning for high performance drives. This is especially true when considering some figures of merits of relevance for drives, such as the torque ripple. A Pareto analysis is proposed to reveal trade-offs between typical figures of merit to establish new tuning methods. To focus our contribution, a five-phase induction drive is considered as the case study, using a finite-state model predictive controller for the stator current control and PI regulators for the outer speed control loop.