Model Predictive Control Technique Research Articles

Modulated model predictive current control technique for triple‐boost seven‐level switched capacitor‐based inverter topology

SummaryTraditionally, the finite control set‐based model predictive current control technique (MPC2T) inherently wrenches from the variable switching frequency of operation. To obtain the fixed switching frequency of operation, different types of predictive control approaches have been reported in the literature. However, these are complex while going to single‐phase higher‐level inverter topologies and are not to be generalized in a simple manner. In this paper, a new generalized modulated MPC2T has been proposed for single‐phase switched capacitor‐based multilevel inverter (SC‐MLI) topologies. In this technique, the optimum inverter output voltage (O‐IOV) has been generated by nullifying the derivative of the cost function. The normalized O‐IOV is fed to a new single modulating floor function (F2) single carrier‐based pulse width modulation technique. Compared to all routine PWM approaches, this specific F2‐based modulation stage is extremely straightforward even for higher‐level inverter topologies. The sensorless voltage balancing of switched capacitor topologies completely eradicates the need for weighting factors in predictive algorithms. To verify the robustness and performance of the perspicuous proposed technique, the conventional triple boost SC‐MLI topology has been considered and elucidated with the different steady and dynamic operation of results.

Innovative model predictive control for HVDC: circulating current mitigation and fault resilience in Modular Multilevel converters

This study presents an advanced Model Predictive Control (MPC) technique designed to mitigate Circulating Current (CC) in HVDC systems equipped with Modular Multilevel Converters (MMC). This MPC strategy eliminates the need for traditional PI regulators and pulse width modulation, improving system dynamics and control accuracy. It excels in managing output currents and mitigating voltage fluctuations in sub-module capacitors. Moreover, the paper introduces a novel communication-free Fault Ride-Through (FRT) method that makes a DC chopper redundant, enabling rapid recovery from disturbances. To reduce the computational burden of standard MPC algorithms, an aggregated MMC model is proposed, significantly decreasing the computational complexity. Simulation studies validate the new MPC algorithm’s capability in regulating AC side current, reducing CC, and ensuring capacitor voltage stability under varying conditions. The findings indicate that the proposed MPC controller outperforms traditional PI and PR-based methods, offering enhanced dynamic response, decreased steady-state error, and lowered converter losses, which contribute to smoother DC link voltages. Future research will focus on system scalability, renewable energy integration, and empirical validation through hardware-in-the-loop testing.

Trajectory prediction for fighter aircraft ground collision avoidance based on the model predictive control technique

Trajectory prediction for fighter aircraft ground collision avoidance based on the model predictive control technique

Enhanced Autonomous Speed Control System for Integrated Cars

The Automated Vehicle Speed Control System (AVSCS) is an embedded framework that utilizes specialized hardware and software components to automatically regulate a vehicle's speed. This system is designed to be implemented in a variety of vehicles, including cars, trucks, and autonomous vehicles, to enhance safe and efficient driving. The AVSCS comprises several essential elements: sensors, a processor or microcontroller, a control calculation algorithm, and a user interface. The sensors gather real-time data about the vehicle's current speed, weather conditions, and surrounding road environment, including wheel speed, GPS, radar, LIDAR, and cameras. The microcontroller or processor processes the sensor data and employs control algorithms, such as PID (Proportional-Integral-Derivative) controllers or advanced Model Predictive Control (MPC) techniques, to calculate the optimal vehicle speed. This information is then used to adjust the throttle or braking mechanisms accordingly. Additionally, the user interface allows drivers to customize the system or set their desired speed preferences, providing a seamless and personalized driving experience. The integration of the AVSCS into various vehicle types can significantly increase security, streamline traffic,, and reduce energy consumption, making it a crucial component of the connected and autonomous vehicle ecosystem. Keywords: vehicle speed regulator, GPS, radar, LIDAR, and cameras

Planar curved path following controller for a small fixed-wing unmanned aerial vehicle with constrained parameters optimized by nonlinear model predictive control

Path following presents a pivotal challenge within the realm of small fixed-wing unmanned aerial vehicles. Firstly, a Lyapunov-stable path guidance law was formulated to follow specific planar curved paths. To ensure differentiability of the guidance law, a modified, smooth saturation function was derived. Secondly, an analysis was conducted to ascertain the interrelationship between control parameters and input constraints, thereby identifying the relevant parameter domains. Thirdly, the nonlinear model predictive control technique was harnessed to optimize both guidance law parameters, enhancing the unmanned aerial vehicle’s capacity to achieve optimal performance in both straight-line and circular path following, hereafter referred to as PFC_NMPC. By leveraging Lyapunov stability arguments for switched systems, the stability of the corresponding nonlinear switched system was guaranteed. In this study, square and circular paths were generated to assess the path-following control of a simulated fixed-wing unmanned aerial vehicle. The performance of various guidance laws, including those with fixed parameters (PFC), those with parameters tuned using fuzzy logic (PFC_FL), PFC_NMPC, vector field, and pure pursuit with line-of-sight, was compared. Notably, the proposed PFC_NMPC method exhibited the ability to expedite the unmanned aerial vehicle’s convergence to the desired path while maximizing the effective flight path length.

DFIG in Wind Energy Applications with High Order Sliding Mode Observer-based Fault-Tolerant Control Scheme using Sea Gull Optimization

This paper describes a new method for maximizing power extraction from a wind energy conversion system (WECS) by using a doubly fed induction generator (DFIG) that operates below nominal wind speed. To maximize the collected power of a wind turbine (WTG) exposed to actuator failure, a fault-tolerant high-order sliding mode observer (HOSMO) and Seagull Optimization Algorithm with a model predictive controller (MPC) technique is proposed. Evaluate both the real state and the sensor error simultaneously using a higher-order sliding-mode observer. Active fault tolerant controllers are designed to regulate wind turbine rotor speed and power in the presence of actuator defects and uncertainty. With the growing interest in employing wind turbines (WTGs) as the primary generators of electrical energy, fault tolerance has been seen as essential to improving efficiency and reliability. This research focuses on optimal fault-tolerant pitch control, which is used to modify the pitch angle of wind turbine blades in the event of sensor, actuator, and system failures. A Seagull Optimization Algorithm (SOA) is proposed to tune controller parameters to improve the performance of WT. The proposed method has achieved 92% of power tracking performance when compared to existing method.

Reactive collision-free motion generation in joint space via dynamical systems and sampling-based MPC

Dynamical system (DS) based motion planning offers collision-free motion, with closed-loop reactivity thanks to their analytical expression. It ensures that obstacles are not penetrated by reshaping a nominal DS through matrix modulation, which is constructed using continuously differentiable obstacle representations. However, state-of-the-art approaches may suffer from local minima induced by non-convex obstacles, thus failing to scale to complex, high-dimensional joint spaces. On the other hand, sampling-based Model Predictive Control (MPC) techniques provide feasible collision-free paths in joint-space, yet are limited to quasi-reactive scenarios due to computational complexity that grows cubically with space dimensionality and horizon length. To control the robot in the cluttered environment with moving obstacles, and to generate feasible and highly reactive collision-free motion in robots’ joint space, we present an approach for modulating joint-space DS using sampling-based MPC. Specifically, a nominal DS representing an unconstrained desired joint space motion to a target is locally deflected with obstacle-tangential velocity components navigating the robot around obstacles and avoiding local minima. Such tangential velocity components are constructed from receding horizon collision-free paths generated asynchronously by the sampling-based MPC. Notably, the MPC is not required to run constantly, but only activated when the local minima is detected. The approach is validated in simulation and real-world experiments on a 7-DoF robot demonstrating the capability of avoiding concave obstacles, while maintaining local attractor stability in both quasi-static and highly dynamic cluttered environments.

Minimum Information Variability in Linear Langevin Systems via Model Predictive Control.

Controlling the time evolution of a probability distribution that describes the dynamics of a given complex system is a challenging problem. Achieving success in this endeavour will benefit multiple practical scenarios, e.g., controlling mesoscopic systems. Here, we propose a control approach blending the model predictive control technique with insights from information geometry theory. Focusing on linear Langevin systems, we use model predictive control online optimisation capabilities to determine the system inputs that minimise deviations from the geodesic of the information length over time, ensuring dynamics with minimum "geometric information variability". We validate our methodology through numerical experimentation on the Ornstein-Uhlenbeck process and Kramers equation, demonstrating its feasibility. Furthermore, in the context of the Ornstein-Uhlenbeck process, we analyse the impact on the entropy production and entropy rate, providing a physical understanding of the effects of minimum information variability control.

Design of a tracking controller for autonomous articulated heavy vehicles using a nonlinear model predictive control technique

This article proposes a design of a tracking controller for autonomous articulated heavy vehicles (AAHVs) using a nonlinear model predictive control (NLMPC) technique. Despite economic and environmental benefits in freight transportation, articulated heavy vehicles (AHVs) exhibit poor directional performance due to their large sizes, multi-unit vehicle configurations, and high centers of gravity (CGs). AHVs represent a 7.5 times higher risk of traffic accidents than single-unit vehicles (e.g. rigid trucks, cars, etc.) in highway operations. Human driver errors cause about 94% of traffic collisions. However, little attention has been paid to autonomous driving control of AHVs. To increase the safety of AHVs, we design a novel NLMPC-based tracking controller for an AHV, that is, a tractor/semi-trailer combination, and this tracking controller is distinguished from others with the feature of controlling both the lateral and longitudinal motions for both the leading and trailing units. To design the tracking controller, a new prediction AHV model is developed, which represents both the lateral and longitudinal dynamics of the vehicle and captures its rearward amplification feature over high-speed evasive maneuvers. With the proposed tracking controller, the AAHV tracks the predefined reference path and follows a planned forward-speed scheme. Co-simulation demonstrates the effectiveness and robustness of the proposed NLMPC tracking controller.

Improving Steady State Accuracy in Field-Weakened Six-Phase Induction Machines with Integrator and Modulated Predictive Control

Finite-control-set model predictive control techniques are considered an exciting option for high-performance control multiphase drives due to their fast dynamic response, ability to handle multiple targets and constraints, and adaptability to different power converters or machine models. However, these techniques have some drawbacks, such as poor current reduction (x−y) and steady-state error (d−q), especially in the field weakening zone. Although some proposals have addressed these issues by adding modulation stages or designing new cost functions, there is still room for improvement, especially in steady-state error reduction. Therefore, this article proposes to include an integrator attached to a modulated predictive current controller applied to a six-phase induction machine to improve its performance throughout the entire speed range regarding steady-state error mitigation. Experimental tests were carried out to validate the effectiveness of the proposed controller. Tests were carried out evaluating the reduction of the steady-state error (d−q), the current tracking, the (x−y) currents reduction and the total harmonic distortion.

Hierarchical Velocity Optimization for Connected Automated Vehicles With Cellular Vehicle-to-Everything Communication at Continuous Signalized Intersections

The rapid development of intelligent connected technologies and cellular vehicle-to-everything communication (C-V2X) provide new opportunities to solve the connected automated vehicle (CAV) traffic problem for eco-driving at continuous signalized intersections. With C-V2X, a hierarchical velocity optimization design based on hybrid model predictive control technique (HVO-HMPC) is presented to reduce the fuel consumption and pollution emission. First, a distance–domain velocity optimization problem, with distance as the independent variable, was constructed. Second, a hybrid MPC scheme was developed by combining the multiple shooting method and MPC technique to calculate the optimal velocity profile of a high-level controller, which acts as the reference velocity in a low-level controller. Then, a car-following model was built, the low-level controller tracked the reference velocity with the predictive control as the backbone, and the optimal velocity was calculated while ensuring that the safety velocity constraint is satisfied. Next, the proposed HVO-HMPC was tested in Prescan, and the effect comparisons with different control methods in terms of fuel consumption, pollution emission, braking time, and number of braking applications were studied under different driving scenarios. Results show that once the maximal speed is limited to 40 km/h under short-period signals and 20 km/h under long-period signals, the HVO-HMPC effectively reduces fuel consumption by 27.21%, 25.89%, and pollution emissions by 25.3%, 25.97%, respectively, while achieving best performance. Finally, an experimental prototype is built to confirm the validity of the HVO-HMPC.

A decentralized optimization approach to the power management of electric vehicles parking lots

To reduce the transportation sector's environmental impact, a transition to electric mobility is being witnessed worldwide. However, to avoid any issues on the grid, the charging process of electric vehicles (EVs) must be carefully managed. One valid solution to this problem is aggregating EVs and managing their collective charge and discharge. Parking lots equipped with charging stations are likely to become more prevalent in the future. Careful management of such infrastructures is essential to avoid local overloads and to provide flexibility to the electrical grid. In this context, this paper formalizes an optimization model for the optimal power management of a smart PL, with the objective of minimizing the power withdrawn from the grid while considering customers’ needs. The main novelties regard how the PL is modeled and how the solution to the optimization problem is achieved. In fact, as far as the modeling is concerned, both single- and three-phase EVs are considered, and batteries are modeled via a non-linear charging profile. As regards the solution method, an augmented Lagrangian-based decentralized model predictive control technique is proposed. The adopted algorithm has been tested on a smart PL in a Genova Municipality (Italy) and its performance has been evaluated by implementing two different scenarios: in the first one only 5 EVs are considered, while the second one regards a scalability analysis where the number of served EVs is increased to 40. The conducted tests confirm that the proposed algorithm can provide a computationally efficient solution, while preserving an accurate model.

A model-based design synthesis method for autonomous articulated vehicles

In conventional autonomous vehicle designs, the mechanical system is first devised, and the automated driving systems are subsequently added. The conventional method can be categorized as a sequential design approach. An autonomous vehicle consists of the subsystems of mechanical assembles, automated driving, etc. There exist dynamic couplings among these subsystems, and the sequential design method may not be the most effective. This observation initiates the motivation of this research, and the objective of this paper is thus to explore a more effective design method for autonomous vehicles and, in particular, autonomous articulated vehicles. To this end, this paper proposes a model-based design synthesis method, i.e., a concurrent design method, for autonomous articulated vehicles. The autonomous articulated vehicle assumes to be composed of the subsystems of a mechanical unit, a trajectory tracking-controller based on a model predictive control technique, and a motion-planner. The design synthesis of the autonomous articulated vehicle is constructed as a bi-level optimization problem. With the proposed concurrent design approach, the subsystems are optimally devised simultaneously. To examine the proposed approach, it is applied to the design of a car-trailer combination with automated steering. The insightful findings attained from the study may be used as guidelines for developing autonomous articulated vehicles.

Motion Planning for Autonomous Driving with Real Traffic Data Validation

Accurate trajectory prediction of surrounding road users is the fundamental input for motion planning, which enables safe autonomous driving on public roads. In this paper, a safe motion planning approach is proposed based on the deep learning-based trajectory prediction method. To begin with, a trajectory prediction model is established based on the graph neural network (GNN) that is trained utilizing the INTERACTION dataset. Then, the validated trajectory prediction model is used to predict the future trajectories of surrounding road users, including pedestrians and vehicles. In addition, a GNN prediction model-enabled motion planner is developed based on the model predictive control technique. Furthermore, two driving scenarios are extracted from the INTERACTION dataset to validate and evaluate the effectiveness of the proposed motion planning approach, i.e., merging and roundabout scenarios. The results demonstrate that the proposed method can lower the risk and improve driving safety compared with the baseline method.

Stability Margin of Data-Driven LQR and Its Application to Consensus Problem

In contrast with traditional control input design techniques based on mathematical models of the system, in data-driven control approaches, which have recently gained substantial attention, the controller is derived directly from the data that are collected from experiments or observations of the target system. In particular, several data-driven optimal control and model predictive control (MPC) techniques have been proposed. In this paper, it is shown that the recently proposed data-driven LQR (Linear Quadratic Regulator) has a stability margin that is the set of the uncertainties in the control input channels maintaining the closed-loop stability. As an application of the proposed stability margin of the data-driven LQR, the consensus problem is considered. Since the control design for the consensus of multi-agent systems can be reformulated into the robust stabilization of a linear system with uncertainty in the input channel, it is demonstrated that the derived stability margin can be used to design a controller for the consensus of multi-agent systems.

Improved Modulated Model Predictive Control Technique for PMSM Drives Using the Vector Projection

Compared to the model predictive control (MPC) technique, the modulated MPC (M2PC) method applies multiple voltage vectors in order within one switching cycle, enabling a fixed switching frequency and better motor control performance. However, the M2PC technique suffers from a significant computational burden and imprecise allocation method of the vector action time, affecting the control performance. To address these two issues, this paper proposes an improved M2PC method that utilizes the information of projection of the reference vector on the selected voltage vectors for efficiently selecting the optimal active vectors and also accurately calculating their action time. Compared to the conventional M2PC, the proposed technique demonstrates both excellent dynamic performance with reduced execution time by 15.3% and superior steady‐state performance in terms of lower torque/speed ripple as well as fewer current harmonics. Those benefits have been proven by the results of a 2.5k rpm permanent magnet synchronous motor drive. © 2024 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC.

Model Predictive Control for Matrix Converters in Grid-Interactive Applications

Model Predictive Control (MPC) is a promising control strategy for matrix converters in grid-interactive applications. MPC optimizes a cost function over a finite time horizon, considering system constraints like voltage and current limits, grid codes, and power factor requirements. This enables effective grid synchronization, power quality regulation, and efficient power conversion. Advancements in MPC include predictive modelling techniques, adaptive algorithms, optimization algorithm integration, hybrid control strategies, and multi-objective optimization formulations. Benefits include fast response to changes in grid conditions, robustness in handling uncertainties, and extended time-horizon optimization. This study provides insights into MPC techniques and highlights the potential for further research.

Performance Evaluation of Per-Phase Model Predictive Control Schemes for Extending Lifespan of Voltage Source Converters

Unequal thermal stress among the phase legs of a multiphase converter leads to a reduction in the useful lifespan and reliability of that converter in general. Increasing the converter’s lifespan by relieving the stressed phase leg, which suffers excessive thermal stress due to aging, is crucial. This paper evaluates two control concepts, including two per-phase model predictive control methods for extending the lifespan of a voltage source inverter. These two per-phase techniques alter the switching pattern to reduce the losses of the most aged phase leg. Hence, the loss and the corresponding thermal stress of the leg that has aged the most are reduced. In such a way, the lifespan and reliability of the converter are prolonged. Two per-phase model predictive control techniques are executed in both simulation and experiment environments, where the corresponding results are provided to evaluate the behavior of these control strategies, considering several operational aspects both in steady state and transient operation. In addition to static load conditions, two per-phase techniques are verified for the correct operation under dynamic load (induction motor) conditions.

An optimization approach to extend control period for dynamics control of Autonomous Underwater Vehicles with X-form rudders

This paper addresses the challenge of dynamics control with a long control period for an Autonomous Underwater Vehicle (AUV) equipped with four independent stern control planes. The goal is to minimize frequent actuator operations. To tackle the difficulties posed by the extended control period, the paper proposes a hierarchy controller architecture comprising high-level dynamics control and low-level control allocation. The dynamics controller is developed using the Nonlinear Model Predictive Control (NMPC) technique. It aims to generate optimal virtual steering moments while considering the differences in the periods of model updating, state prediction, and control output. Additionally, a constrained optimization problem is formulated for control allocation, taking into account the characteristics of the control planes. To validate the proposed approach, a test AUV is designed for experimental validation. The hydrodynamic parameters required for state prediction are identified through Computational Fluid Dynamics (CFD) analysis and verified by comparing characteristic indexes from maneuvering trials. Comparison with the Linear Quadratic Regulator (LQR) in dynamics control highlights that the proposed control methods offer a stable and effective control framework, particularly when dealing with a long control period.

Model Predictive Control of a PUC5-Based Dual-Output Electric Vehicle Battery Charger

In this study, a model predictive control (MPC) technique is applied to a packed-u-cell (PUC)-based dual-output bidirectional electric vehicle (EV) battery charger. The investigated topology is a 5-level PUC-based power factor correction (PFC) rectifier allowing the generation of two levels of DC output voltages. The optimization of the MPC cost function is performed by reducing the errors on the capacitors’ voltages (DC output voltages) and the grid (input) current. Moreover, the desired capacitors’ voltages and peak value of the input current are considered within the designed cost function to normalize the errors. In addition, an external PI controller is used to generate the amplitude of the grid current reference based on the computed errors on the capacitors’ voltages. The presented simulation and experimental results recorded using a 1 kW laboratory prototype demonstrate the high performance of the proposed approach in rectifying the AC source at different levels (dual rectifier), while drawing a sinusoidal current from the grid with low THD (around 4%) and ensuring a unity power factor operation.