Nonlinear Model Predictive Control Research Articles

A Symmetry-Inspired Hierarchical Control Strategy for Preventing Rollover in Articulated Rollers

In off-road environments, the lateral rollover stability of articulated unmanned rollers (URs) is critical to ensure operational safety and efficiency. This paper introduces the concept of a rollover energy barrier (REB), a symmetry-based metric that quantifies the energy margin between the current state and the critical rollover threshold of articulated rollers. URs exhibit dynamic asymmetry due to their hydraulic steering systems, which differ significantly from traditional passenger vehicles. To address these challenges, we propose a hierarchical control framework inspired by the principles of dynamic symmetry. This framework integrates Nonlinear Model Predictive Control (NMPC) and Active Disturbance Rejection Control (ADRC): NMPC is used for trajectory planning by incorporating the REB into the cost function, ensuring rollover stability, while ADRC compensates for dynamic asymmetries, model uncertainties, and external disturbances during trajectory tracking. Simulation and experimental results validate the effectiveness of the proposed control strategy in enhancing the rollover stability and tracking performance of the URs under off-road conditions.

An adaptive NMPC for ROVs trajectory tracking with environmental disturbances and model uncertainties

A novel hybrid adaptive control method is presented for trajectory tracking of remotely operated underwater vehicles (ROVs) that addresses unknown disturbances and model uncertainties in this paper. Traditional nonlinear control methods struggle to handle external disturbances and uncertainty in system model. To address the trajectory tracking control needs of ROVs in complex underwater environments, a kinematic and dynamic model is first developed for a fully actuated ROV with six degrees of freedom (6-DOF). The trajectory tracking problem is formulated as an online, nonlinear receding horizon optimization process. Control increments are computed as inputs to this nonlinear optimization problem. An L1 adaptive control method (L1AC) is then developed, incorporating a state observer, adaptive control law, and time filter. The framework retains the rolling optimization process of nonlinear model predictive control (NMPC) while integrating the L1 adaptive component for instant compensation of unknown disturbances and model parameter mismatches. Numerical simulations were conducted to compare the trajectory tracking performance of the proposed hybrid adaptive method with the NMPC method under various disturbances, including ocean currents, waves, random forces, and model uncertainties. The results confirm that the proposed hybrid adaptive control scheme is more effective and robust than the standalone NMPC approach across various scenarios.

Extended Kalman Filtering-Based Nonlinear Model Predictive Control for Underactuated Systems With Multiple Constraints and Obstacle Avoidance

Extended Kalman Filtering-Based Nonlinear Model Predictive Control for Underactuated Systems With Multiple Constraints and Obstacle Avoidance

HPA-MPC: Hybrid Perception-Aware Nonlinear Model Predictive Control for Quadrotors With Suspended Loads

HPA-MPC: Hybrid Perception-Aware Nonlinear Model Predictive Control for Quadrotors With Suspended Loads

Distributed coordinated motion control of multiple UAVs oriented to optimization of air-ground relay network

A novel adaptive model-based motion control method for multi-UAV communication relay is proposed, which aims at improving the networks connectivity and the communications performance among a fleet of ground unmanned vehicles. The method addresses the challenge of relay UAVs motion control through joint consideration with unknown multi-user mobility, environmental effects on channel characteristics, unavailable angle-of-arrival data of received signals, and coordination among multiple UAVs. The method consists of two parts: (1) Network connectivity is constructed and communication performance index is defined using the minimum spanning tree in graph theory, which considers both the communication link between ground node and UAV, and the communication link between ground nodes. (2) A multi-UAV motion control strategy is proposed that combines Improved Particle Swarm Optimization (IPSO) and Distributed Nonlinear Model Predictive Control (DNMPC), where the Kalman filter is utilised to estimate future positions of the mobile nodes. Simulation results in both single and complex environments show that the presented method can drive the UAVs to reach or track the optimal relay positions and improve network performance, while demonstrating the benefits of considering the impact of environments on channel characteristics.

Straight-Line Trajectory Tracking Control of Unmanned Sailboat Based on NMPC Velocity and Heading Joint Control

This study proposes a trajectory tracking approach for unmanned sailboats that integrates velocity and heading control using nonlinear model predictive control (NMPC). Unlike conventional methods, which typically rely on separate control strategies for maximum velocity and heading, this study employs a joint control framework based on NMPC. This approach allows for constrained control, ensuring that the sailboat operates safely at the desired velocity, forming a foundation for trajectory tracking. The trajectory tracking strategy, built on the joint control of velocity and heading, is further categorized into upwind and non-upwind tracking based on the wind direction. For non-upwind tracking, the desired heading is determined using the Line-of-Sight (LOS) navigation method, while the desired velocity is calculated through a backstepping method grounded in the Lyapunov stability theorem. For upwind tracking, a zigzag strategy is introduced, using the maximum lateral error to switch headings and ensure that the sailboat remains close to the trajectory. The simulation results show that the proposed method can effectively control the velocity and heading and realize accurate trajectory tracking of the unmanned sailboat under different wind conditions. The average error of velocity and heading control is close to zero. During trajectory tracking, the maximum control error is less than 0.35%.

NMPC-Based Path Tracking Control Through Cascaded Discretization Method Considering Handling Stability for 4WS Autonomous Vehicles Under Extreme Conditions

In the realm of autonomous driving, motion control generally involves two critical aspects—path following and stability control—and an inevitable mutual interference exists between them under extreme conditions. To tackle this challenge, this study proposes a collaborative approach of path tracking and stability control. When designing the path tracking control module, the effects of vertical tire load transfer and road surface adhesion coefficients on tire force calculations were taken into account to mitigate vehicle dynamics model mismatch. Leveraging the receding horizon optimization characteristic of nonlinear model predictive control (NMPC), a cascaded discretization approach was utilized to realize a balance between precision and real-time performance in numerical solutions. Then, a stability controller, which employs rear wheel steering, was designed to prevent excessive increases in the vehicle’s sideslip angle, thereby ensuring the vehicle’s lateral stability. The effectiveness of the proposed strategy is validated through CarSim 8.0/Simulink cosimulation. The outcomes demonstrate that the stability controller significantly enhances vehicle stability under high-speed and low-adhesion conditions. On the premise of stability, the proposed path tracking controller has exhibited significant enhancements in real-time performance, without compromising the accuracy of path tracking.

Enhanced Modeling and Control of Organic Rankine Cycle Systems via AM‐LSTM Networks Based Nonlinear MPC

ABSTRACTThe organic Rankine cycle (ORC) serves as an effective means of converting low‐grade heat sources into power, playing a pivotal role in environmentally friendly production and energy recovery. However, the inherent complexity, strong and unidentified nonlinearity, and control constraints pose significant challenges to designing an optimal controller for ORC systems. To address these issues, this research introduces a novel modeling and control framework for ORC systems. Leveraging an attention mechanism‐based long short‐term memory (AM‐LSTM) network, the dynamic characteristics of ORC systems, which are subject to non‐Gaussian disturbances, are accurately modeled. A performance metric based on survival information potential (SIP) is developed to optimize the network parameters. Furthermore, a multi‐objective optimization approach that integrates nonlinear model predictive control (NMPC) with the multiverse optimizer (MVO) algorithm is implemented to ensure effective control under varying operating conditions and constraints. Through extensive simulations, the proposed framework demonstrates superior accuracy, robustness, and control performance for ORC systems.

Robust Nonlinear Model Predictive Control for the Trajectory Tracking of Skid-Steer Mobile Manipulators with Wheel–Ground Interactions

This paper presents a robust control strategy for trajectory-tracking control of Skid-Steer Mobile Manipulators (SSMMs) using a Robust Nonlinear Model Predictive Control (R-NMPC) approach that minimises trajectory-tracking errors while overcoming model uncertainties and terra-mechanical disturbances. The proposed strategy is aimed at counteracting the effects of disturbances caused by the slip phenomena through the wheel–terrain contact and bidirectional interactions propagated by mechanical coupling between the SSMM base and arm. These interactions are modelled using a coupled nonlinear dynamic framework that integrates bounded uncertainties for the mobile base and arm joints. The model is developed based on principles of full-body energy balance and link torques. Then, a centralized control architecture integrates a nominal NMPC (disturbance-free) and ancillary controller based on Active Disturbance-Rejection Control (ADRC) to strengthen control robustness, operating the full system dynamics as a single robotic body. While the NMPC strategy is responsible for the trajectory-tracking control task, the ADRC leverages an Extended State Observer (ESO) to quantify the impact of external disturbances. Then, the ADRC is devoted to compensating for external disturbances and uncertainties stemming from the model mismatch between the nominal representation and the actual system response. Simulation and field experiments conducted on an assembled Pioneer 3P-AT base and Katana 6M180 robotic arm under terrain constraints demonstrate the effectiveness of the proposed method. Compared to non-robust controllers, the R-NMPC approach significantly reduced trajectory-tracking errors by 79.5% for mobile bases and 42.3% for robot arms. These results highlight the potential to enhance robust performance and resource efficiency in complex navigation conditions.

Towards nonlinear model predictive control of flexible structures using Gaussian Processes

Abstract In recent years, there is a growing interest of using and implementing data driven control in structural dynamics. This study considers applying Nonlinear Model Predictive Control (NMPC) to flexible structures by utilising recent developments in models which have been learnt from example data, i.e. machine learning approaches. The Gaussian process (GP) is a Bayesian machine learning algorithm identified for use as a black-box model in NMPC; it provides both the prediction of the system output and the associated confidence. In a control context, a GP can be utilised as a discrepancy model for linear or nonlinear flexible dynamic structures within MPC or even as the nonlinear model of the system itself. The Nonlinear Output Error model (GP-NOE) is a popular GP structure for dynamic systems that is utilised in predictive control strategies and requires predictions to be propagated to the control horizon. This novel framework is evaluated on a cantilever beam with light damping, and the results demonstrate robust control performance in both tracking and regulator tasks. The positive results inspire additional investigation into the proposed technique, particularly in the setting of a fully nonlinear system with unknown dynamics, such as an actuator within the flexible structure.

Learning-Based Nonlinear Model Predictive Control of Articulated Soft Robots Using Recurrent Neural Networks

Learning-Based Nonlinear Model Predictive Control of Articulated Soft Robots Using Recurrent Neural Networks

Model Predictive Control With Guaranteed Feasibility of Inequality Path Constraints.

This article concerns nonlinear model predictive control (MPC) with guaranteed feasibility of inequality path constraints (PCs). For MPC with PCs, the existing methods, such as direct multiple shooting, cannot guarantee feasibility of PCs because the PCs are enforced at finitely many time points only. Therefore, this article presents a novel MPC framework that is capable of not only achieving stability control but also guaranteeing feasibility of PCs during the rolling optimization stages of MPC. Under the above MPC framework, an algorithm is first proposed by applying the semi-infinite programming technique to the rolling optimization of MPC. However, it takes heavy computational time to achieve guaranteed feasibility of PCs. Therefore, to guarantee feasibility of PCs meanwhile effectively reducing the computation burden of the closed-loop system, an event-triggered sampling mechanism is constructed in the above path-constrained MPC algorithm. Moreover, sufficient conditions are given for asymptotic convergence of the closed-loop systems. Finally, the effectiveness of the proposed results is illustrated via a cart-damper-spring system.

Multi-objective hierarchical energy management strategy for fuel cell/battery hybrid power ships

The energy management strategy and the local controller in the ship energy management system are interconnected, impacting the performance of the hybrid propulsion system. To achieve the efficient operation of the hydrogen fuel cell (FC) and battery hybrid power system, based on the modelling and analysis of the hybrid power system, a nonlinear model predictive control (NMPC) based energy management strategy is proposed, and a dynamic virtual impedance droop controller and a classical proportional-integral (PI) controller are designed as local controllers. By simulating the designed random load conditions, pulse load conditions, and actual sailing conditions using hardware-in-the-loop (HiLs) technology, six different energy management strategies and their comprehensive performance with local controllers are compared and analysed. Comparing performance in terms of energy consumption, operating pressure, control accuracy, real-time performance, and robustness, it has been proven that the energy management strategy based on NMPC, coupled with a PI controller, is superior to other strategies overall. It can balance hydrogen consumption and the stable operation of the hybrid power system. Compared to existing energy management strategies, the proposed NMPC+PI strategy can reduce hydrogen consumption by 7.00 % and 40.29 %, and FC operating pressure by 44.96 % and 49.88 %, respectively, under both designed navigation conditions and actual navigation conditions.

The Path Tracking Control of Unmanned Surface Vehicles Based on an Improved Non-Dominated Sorting Genetic Algorithm II-Based Multi-Objective Nonlinear Model Predictive Control Method

This paper proposes a multi-objective nonlinear model predictive control (MOMPC) method based on an improved non-dominated sorting genetic algorithm II (NSGAII) for the path tracking problem of unmanned surface vehicles (USVs). To enhance performance in cross-track error, a varying look-ahead distance is utilized in the line of sight (LOS) algorithm, which allows the MPC control algorithm to compute the look-ahead distance and desired speed rather than directly calculating the control input. Since the cost function of the MPC algorithm includes multiple objective terms, a multi-objective model predictive control algorithm is employed to improve overall control performance. Additionally, an adaptive rotation-based simulated binary crossover (ARSBX) is integrated into the NSGAII algorithm, and the non-dominated sorting method is optimized to reduce computation time. These enhancements increase diversity and exploration in the solution space, enabling the algorithm to find the optimal solution more efficiently. Simulations conducted in two different scenarios demonstrate that the nonlinear MPC method based on the improved NSGAII successfully tracks the desired path; it achieved an improvement of approximately 41% in time performance and about 5% in path-tracking error performance, exhibiting strong control performance and robustness.

Multirotor nonlinear model predictive control based on visual servoing of evolving features

Multirotor nonlinear model predictive control based on visual servoing of evolving features

Isolated microgrid frequency stabilization through nonlinear model predictive control of a gas engine generator set

Gas engine generator sets are applied in microgrids due to their capability to provide reliable, responsive and efficient distributed power generation, enhancing grid resilience and enabling the integration of renewable energy sources. This article proposes a methodology for stabilizing the frequency of an isolated microgrid subjected to a measurable disturbance by controlling the power of a premixed turbocharged natural gas engine. Control difficulties of this task include strongly nonlinear dynamics, time delay in the air-fuel path and constrained operating conditions. Conventional control methods like proportional-integral control have difficulties solving these problems, and require extensive tuning efforts and gain scheduling to deliver acceptable performance, prompting adoption of advanced control strategies. The presented approach utilizes a cascaded control architecture with a nonlinear model predictive controller (NMPC) for high level control and engine specific low-level controllers for controlling the intake manifold pressure and air-fuel ratio. The NMPC captures the inherent nonlinear dynamics, accounts for an input delay and handles state-dependent constraints. The cascaded control architecture enables the usage of a comparably simple model for the NMPC design, reducing the computational cost and the parametrization effort. This additionally enables application of the high level controller for different gas engine types and allows design of the controller in a simple simulation environment prior to the experiments on the real engine. Experimental results highlight the NMPC’s ability to control the engine at its physical limits over the entire load range without inducing frequency oscillations using just one parameter set. This emphasizes the robustness of the presented approach, making it a promising solution for real-world applications.

Highly Safe and Fast Charing Method Based on Electrochemical Thermal Life Model for Lithium Ion Battery

Long charging time is one of the technical barriers that should be overcome for the wide acceptance of electric vehicles (EVs) in the market. The charging time can be simply reduced by an increased charging current that adversely reduces lifespan and deteriorates the safety of batteries. Therefore, the design of an appropriate charging protocol is a challenging issue. We have developed a fast charging method based on a reduced order electrochemical life model (ROM) considering side reactions and lithium plating. Among different fast charging profiles, the negative pulse (NP) charging method is selected. The pulse discharging current between high charging currents can promote lithium stripping so that lithium ions can be recovered from the plated lithium. The fast charging currents are optimized using a nonlinear model predictive control (NMPC) algorithm. The objectives are set to reduce the charging time with minimized degradation speed. Multiple constraints are considered in NMPC such as state of charge, cutoff voltage, side reaction rate, anode potential, and ion concentrations. The proposed fast charging algorithm is tested in real-time using a Battery-In-the-Loop (BIL) system. The results have shown that the charging time can be reduced while degradation rate can be maintained compared with the normal charging method. Reference [1] Yin Y., Bi Y., Hu Y., Choe, S. Y. (2021). Optimal Fast Charging Method for a Large-Format Lithium-Ion Battery Based on Nonlinear Model Predictive Control and Reduced Order Electrochemical Model. Journal of The Electrochemical Society, 167, 160559 [2] Yin Y., and Choe, S. Y. (2020). Actively temperature controlled health-aware fast charging method for lithium-ion battery using nonlinear model predictive control. Applied Energy, 271, 115232.[3] Song M., Choi M., and Choe, S. Y. (2023). Start-Up Charging Strategy for a Large-Format NMCA/Graphite Pouch Cell from Subzero Temperature Using an Electrochemical, Thermal, and Mechanical Life Model. Journal of The Electrochemical Society, 170, 060533[4] Song M., and Choe, S. Y. (2019). Fast and safe charging method suppressing side reaction and lithium deposition reaction in lithium ion battery. Journal of Power Sources, 436, 226835

PPO-based resilient control framework for safer operation of exothermic CSTR

The safety of chemical reactor is one of the major problems in process safety that has caused numerous incidents and has had detrimental effects on individual health and environment. The susceptibility of the Continuously Stirred Tank Reactor (CSTR) to loss of control is typically due to either human error or controller failure. In this paper, a state-of-art resilient control framework utilizing Proximal Policy Optimization (PPO) is proposed to effectively control the CSTR operation and maintain the statue at a desired steady state. The purpose of this work is to improve system resilience through implementation of resilience calculation and integration with reinforcement learning to finally reach optimal resilience. Comparative analysis is then performed to evaluate the control performance under the guidance of proposed framework compared with Proportional Integral Derivative controller (PID) and Nonlinear Model Predictive Control (NMPC). The results prove that the proposed framework is applicable to develop resilient system and considerably outperforms the PID controller and the NMPC in terms of efficiency and stability.

Optimal Driving Torque Control Strategy for Front and Rear Independently Driven Electric Vehicles Based on Online Real-Time Model Predictive Control

This paper presents a novel driving torque control strategy for the front and rear independently driven electric vehicle (FRIDEV) to reduce energy consumption and enhance vehicle stability. The strategy is built on a comprehensive vehicle model that integrates vertical load transfer, tire slip dynamics, and an electric system model that accounts for losses in induction motors (IMs), permanent magnet synchronous motors (PMSMs), inverters, and batteries. The torque control problem is framed with a nonlinear model predictive control (MPC) method, utilizing state-space equations as representations of vehicle dynamics. The optimization targets adjust in real-time based on road traction conditions, with the slip rate of front and rear wheels determining the torque control strategy. Active slip control is applied when slip rates exceed critical thresholds, while under normal conditions, torque distribution is optimized to minimize energy losses. To enable online real-time implementation, an improved sparrow search algorithm (SSA) is designed. Simulations in MATLAB/Simulink confirm that the proposed online strategy reduces energy consumption by 2.3% under the China light-duty vehicle test cycle-passenger cars (CLTC-P) compared to a rule-based strategy. Under low-adhesion conditions, the proposed online strategy effectively manages slip ratios, ensuring stability and performance. Improved SSA also enhances computational efficiency by approximately 44%–52%, making the online strategy viable for real-time applications.

PSO-NMPC control strategy based path tracking control of mining LHD (scraper)

The automation of underground articulated vehicles is a critical step in advancing digital and smart mining. Current nonlinear model predictive control (NMPC) controllers face challenges such as delays in turning on large curvature paths and correction lags during the control of underground the Load-Haul-Dump (LHD). To address these issues, this paper proposes a PSO-NMPC control strategy that integrates a particle swarm optimization algorithm (PSO) into the NMPC controller to enhance path tracking for LHDs. To verify the effectiveness of the proposed PSO-NMPC control strategy, the local path of the tunnels is selected as the simulation path, comparing it with the pure NMPC controller based on the path characteristics of the actual tunnel. The results demonstrate that the improved NMPC controller significantly enhances the trajectory tracking performance of the LHD, with maximum absolute lateral deviations for experimental paths 2, 3, and 5 improved by 89.7%, 72.2%, and 68.9%, respectively. Additionally, the improved NMPC controller exhibits superior performance in paths with large curvature compared to those with very small curvature and straight-line paths, effectively addressing the challenges of turn delay and backward lag in LHD operation, thus providing practical significance.