Predictive Control Algorithm Research Articles

Implementation of Generalized Model Predictive Control Algorithm for DC-DC Boost Converter on STM32

Implementation of Generalized Model Predictive Control Algorithm for DC-DC Boost Converter on STM32

Improved intelligent model predictive controller for the nuclear power reactor system

Abstract Over the last two decades, extensive research has been conducted to enhance the control performance of reactor power. Various methodologies have been suggested and implemented to achieve optimal power control in nuclear reactors. However, due to the diverse characteristics and inherent uncertainties in the models, devising optimal controllers for nuclear systems remains a complex task. To address this, numerous approaches have been adopted to ensure controllability and resilience, aiming for an optimal nuclear power reactor controller. The Model Predictive Control (MPC) algorithm has garnered significant attention as a viable approach to boost operational efficiency and overall system utility. In this research, Particle Swarm Optimization (PSO) method and MPC controller are combined together to form a novel algorithm termed PSO-MPC, aiming to amplify the system’s performance and overcomes the local minima problem that basically happens when using MPC controller. First, MPC controller is applied to the nuclear reactor model, then the suggested technique PSO-MPC also is applied to the system and a comparison of the outcomes using both techniques is done. The results demonstrate enhanced system response using the innovative technique.

Optimized multi-stage constant current fast charging protocol suppressing lithium plating for lithium-ion batteries using reduced order electrochemical-thermal-life model

Multi-stage Constant Current (MCC) is a state-of-the-art fast-charging protocol considering battery aging. It divides the charging process into multiple stages, each with a different current amplitude based on specific transition criteria, significantly influencing battery performance, such as charging time, degradation rate, and thermal effects. A key challenge in designing MCC protocols is addressing the lithium plating (LiP), which can accelerate degradation and pose a severe risk of thermal runaway. Since the LiP onset conditions vary between fresh and aged cells, this paper proposes an optimized MCC (O-MCC) charging protocol suppressing LiP based on the battery's state of health. To accurately simulate LiP conditions, different platforms of reduced-order electrochemical-thermal-life models are designed, compared, and optimized for speed and accuracy using a genetic algorithm, resulting in a 36.4 % reduction in computational time while maintaining the accuracy of the Pseudo Two-Dimensional model. The Nonlinear Model Predictive Control algorithm is then used to optimize the MCC protocol, minimizing charging time while preventing LiP throughout life. Experimental results show that O-MCC reduces charging time by 11.7 % and capacity loss by 59.4 %, enhancing battery safety. Additionally, O-MCCs with varying constraints are developed to meet specific demands and simulated at the battery pack level.

A NEW HYBRID OPTIMIZATION METHOD USED IN PREDICTIVE CONTROL OF A NONLINEAR FRACTIONAL MODEL BASED ON FRACTIONAL HAMMERSTEIN STRUCTURE

Fractional Hammerstein models represent various nonlinear processes, such as thermal and mechanical. Their major drawback is the non-convex optimization problem in a nonlinear model predictive control scheme due to its static nonlinearity. Indeed, an efficient optimization algorithm is needed. This work proposes a hybrid optimization algorithm combining the Nelder Mead optimization method and the Honey Badger one to synthesize a predictive control algorithm based on fractional Hammerstein models. As illustrated through simulation results, the proposed method offers clear improvements in convergence and tracking performances.

Research on MPC Algorithm of Human Simulating Predictive Control in Energy Storage and Modeling of Stepping Motor

Research on MPC Algorithm of Human Simulating Predictive Control in Energy Storage and Modeling of Stepping Motor

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.

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.

Modeling the Dynamics of Prosthetic Fingers for the Development of Predictive Control Algorithms

In the field of biomechanical modeling, the development of a prosthetic hand with dexterity comparable to the human hand is a multidisciplinary challenge involving complex mechatronic systems, intuitive control schemes, and effective body interfaces. Most current commercial prostheses offer limited functionality, typically only one or two degrees of freedom (DoF), resulting in reduced user adoption due to discomfort and lack of functionality. This research aims to design a computationally efficient low-level control algorithm for prosthetic hand fingers to be able to (a) accurately manage finger positions, (b) anticipate future information, and (c) minimize power consumption. The methodology employed is known as model-based predictive control (MBPC) and starts with the application of linear identification techniques to model the system dynamics. Then, the identified model is used to implement a generalized predictive control (GPC) algorithm, which optimizes the control effort and system performance. A test bench is used for experimental validation, and the results demonstrate that the proposed control scheme significantly improves the prosthesis’ dexterity and energy efficiency, enhancing its potential for daily use by people with hand loss.

Optimized Trajectory Tracking for ROVs Using DNN + ENMPC Strategy

This study introduces an innovative double closed-loop 3D trajectory tracking approach, integrating deep neural networks (DNN) with event-triggered nonlinear model predictive control (ENMPC), specifically designed for remotely operated vehicles (ROVs) under external disturbance conditions. In contrast to single-loop model predictive control, the proposed double closed-loop control system operates in two distinct phases: (1) The outer loop controller uses a DNN controller to replace the LMPC controller, overcoming the uncertainties in the kinematic model while reducing the computational burden. (2) The inner loop velocity controller is designed using a nonlinear model predictive control (NMPC) algorithm with its closed-loop stability proven. A DNN + ENMPC 3D trajectory tracking method is proposed, integrating a velocity threshold-triggered mechanism into the inner-loop NMPC controller to reduce computational iterations while sacrificing only a small amount of tracking control performance. Finally, simulation results indicate that compared with the ENMPC algorithm, NMPC + ENMPC can better track the desired trajectory, reduce thruster oscillations, and further minimize the computational load.

Trajectory tracking multi-constraint model predictive control of unmanned vehicles based on sideslip stiffness estimation with XGBoost algorithm

When the vehicle is driving at high speed on a slippery road, the sideslip stiffness of the tire which is closely related to the lateral force of the tire, will vary with the change of road conditions, tire inflation pressure, vertical load, and other factors. If the tire sideslip stiffness is set to a fixed value in the control system based on the model design, the uncertainty of the sideslip stiffness will cause a large error with the actual application. Therefore, in order to improve the trajectory tracking accuracy of the vehicle on the slippery road surface under the premise of ensuring the stability of the vehicle, an intelligent vehicle trajectory tracking control strategy considering the influence of sideslip stiffness is designed in this paper. In order to solve the multiple constraints of the vehicle driving on the low-adhesion road surface, a trajectory tracking controller based on the multi-constraint model predictive control algorithm is designed, and then the tire sideslip stiffness estimation strategy is designed based on the XGBoost algorithm, and the estimated sideslip deviation stiffness is added to the trajectory tracking control model. Carsim and MATLAB/Simulink are used to perform co-simulation to verify the accuracy of the proposed tire sideslip stiffness estimation and the tracking performance of the trajectory tracking controller. In order to further verify the reliability of the research content, the relevant working condition tests are carried out on the hardware-in-the-loop platform based on Carsim and LabVIEW, and the effectiveness of the trajectory tracking controller considering the influence of sideslip stiffness is verified.

A learning‐based hierarchical energy management control strategy for hybrid electric vehicles

AbstractIn this work, a novel energy management control framework is developed for hybrid electric vehicles (HEVs) driving in car‐following scenarios. In order to enhance the energy efficiency while maintaining the driving safety, a hierarchical control approach consisting of an upper level speed tracking control scheme and a lower level energy management control strategy is proposed. For the upper level tracking control system, an iterative learning model predictive control (ILMPC) scheme is developed to guarantee the tracking performance and the driving safety simultaneously. Additionally, a model predictive control (MPC) algorithm is adopted at the lower level to optimize the torque distribution in real‐time based on the driving cycles generated by the upper level control system. With the proposed hierarchical control framework, HEVs are able to improve the energy efficiency significantly by taking the advantages of the operational repeatability. The convergence of the proposed control strategy is analyzed rigorously, and its effectiveness is illustrated through numerical simulations.

New stability theory of model predictive control: modified stage cost approach

This paper presents a promising new approach to establish the stability of finite receding horizon control with a terminal cost. Departing from the traditional approaches of using the property of the terminal cost or relaxed Lyapunov inequalities, this paper establishes its stability based on the property of a modified stage cost. First, we rotate the stage cost with the terminal cost. Then a one-step optimisation problem is defined based on this augmented stage cost. It is shown that a slightly modified Model Predictive Control (MPC) algorithm is stable if the value function of the augmented one-step cost (OSVF) is a Control Lyapunov Function (CLF). Stability for MPC algorithms with zero terminal cost or even negative terminal cost can be unified with this new approach. Combining it with the existing MPC stability theories, we are able to significantly relax the stability requirement on MPC and extend the stabilising MPC design space to the region that no existing MPC stability theories can cover. The proposed stage cost-based approach will help to further reduce the gap between stability theory and practical applications of MPC and other optimisation-based control methods.

Self-triggered MPC with adaptive prediction horizon for nano-satellite attitude control system

Nano-satellites are essential tools for various applications, including scientific experiments, deep space exploration and astronomical observation. Achieving precise model predictions is crucial for their successful operation. To address the intricate constraints of nano-satellites and enhance control performance, the Model Predictive Control (MPC) algorithm is an effective solution. However, implementing an MPC-based attitude control system in actual engineering scenarios presents significant challenges, primarily due to the substantial computational burden, especially given the limited onboard computing resources of nano-satellites. In this paper, we introduce a modified adaptive self-triggered model predictive control (ST-MPC) algorithm designed to stabilize the attitude of nano-satellites, while simultaneously reducing communication and computational overhead compared to traditional MPC methods. The proposed self-triggered mechanism dynamically determines the next trigger time according to the system state. Moreover, we incorporate considerations for the efficiency of actuators to address the constraints imposed by the magnetic torque characteristics within the modified self-triggered mechanism. Additionally, a strategy for adaptive prediction horizon is proposed to balance computation load and control accuracy. The results of our simulations demonstrate the effectiveness of the modified ST-MPC algorithm in comparison to both traditional MPC and standard ST-MPC approaches. This algorithm may have the potential to significantly impact attitude control applications for nano-satellites.

Designing of Ecological Model Predictive Control Algorithm for Electric Trucks Platoons with Optimal Energy Consumption

Abstract In response to the problem of motor vehicle exhaust emissions pollution, the development of new energy in China’s truck industry will be an inevitable trend. At present, the penetration rate and market share of pure electric trucks are constantly increasing. The research on electric truck platoons has strong practical significance. This article focuses on the ecological control strategy of pure electric truck platoons. Firstly, a platoon model of four electric trucks is established in Trucksim. The leading vehicle designs an ecological optimal speed dynamic programming algorithm, and in terms of following vehicle tracking control, the battery SOC state motor speed is considered to be improved, Designed an ecological model predictive control algorithm for electric truck platoon with optimal energy consumption. Using real road slope data in the mining area, a Trucksim/Matlab/Simulink joint simulation was conducted. The simulation results showed that compared with traditional constant speed cruise control, the proposed ecological predictive cruise control algorithm for pure electric commercial vehicle platoons saved 9.2577% of the overall battery SOC.

Multi-Horizon Model Predictive Control for Energy Management in Zero-Emission High-Speed Passenger Vessels

Abstract High-speed passenger vessels (HSPVs) play an important role in coastal regions due to their high energy consumption and emissions. This study develops an optimal energy management system (EMS) for a zero-emission hybrid power system, powered by a fuel cell (FC) and a battery energy storage system (BESS). The global optimal solution for loading the FC and operating the BESS is computed using a linear programming (LP) approach, assuming a priori knowledge of the complete operational profile. Since the global solution is not realizable in an onboard EMS, the main objective of this paper is to develop an algorithm for real-time application. A multi-horizon model predictive control (MH-MPC) algorithm is applied to a model of the power system to solve the optimization problem with a prediction mechanism over the entire route. This approach is benchmarked against the global LP solution to evaluate the effectiveness of the proposed EMS. The results highlight the potential of the MH-MPC approach for achieving a near-optimal EMS in a zero-emission HSPV.

Data-Driven Model Predictive Control for Redundant Manipulators With Unknown Model.

The tracking control of redundant manipulators plays a crucial role in robotics research and generally requires accurate knowledge of models of redundant manipulators. When the model information of a redundant manipulator is unknown, the trajectory-tracking control with model-based methods may fail to complete a given task. To this end, this article proposes a data-driven neural dynamics-based model predictive control (NDMPC) algorithm, which consists of a model predictive control (MPC) scheme, a neural dynamics (ND) solver, and a discrete-time Jacobian matrix (DTJM) updating law. With the help of the DTJM updating law, the future output of the model-unknown redundant manipulator is predicted, and the MPC scheme for trajectory tracking is constructed. The ND solver is designed to solve the MPC scheme to generate control input driving the redundant manipulator. The convergence of the proposed data-driven NDMPC algorithm is proven via theoretical analyses, and its feasibility and superiority are demonstrated via simulations and experiments on a redundant manipulator. Under the drive of the proposed algorithm, the redundant manipulator successfully carries out the trajectory-tracking task without the need for its kinematics model.

Manoeuvring in shallow and confined water with model predictive track controllers

Abstract Autonomous and computer-assisted navigation, for example through portable pilot units and data-driven decision supportive systems, is in full development. Remote-controlled vessels (manned but without a captain steering continuously on board) are used in Belgium (by Seafar in inland shipping) but also in other countries. A model predictive controller has been used to investigate environmental and operational parameters for the passage of the river Seine in Paris by different ship types. These fast-time simulations have been compared with simulations in real time executed by skippers on a ship manoeuvring simulator or measured real life tracks. A good predictability of the real situation requires accurate manoeuvring models. But what if this accuracy cannot be guaranteed? An illustration is made by comparing the tracks if, in the prescience prediction phase for the controller, wind or bank effects are neglected or doubled. The development of model predictive deep reinforcement learning control algorithms for navigation will be one of the challenges of the Data-driven Smart Shipping project that started May 2024.

MPC Optimization Algorithm and Strategy for HVAC System under Smart City Construction

As a giant in energy consumption, buildings urgently need to optimize control strategies for the main energy consuming equipment inside buildings. It is of great significance to design advanced control algorithms to improve the efficiency of the main energy consuming equipment in buildings, namely air conditioning, in the current energy shortage. The model predictive control algorithms and strategies were used in this study to control the HVAC system to improve the energy utilization of the city. Then linear matrix inequality with robust model predictive feedback controller was used to optimize and get the model predictive control optimization algorithm. The research results showed that, under the influence of different factors, the three regions controlled by the model predictive control optimization algorithm showed a little overshooting in the initial state. But it was quickly corrected after adjustment. Meanwhile, the average tracking error of temperature and humidity in each region was 0.139◦ C and 0.13g/kg dry air, respectively. The average predicted mean vote was 0.32. In actual office buildings, the proposed algorithm controlled the temperature within the reference value range of 0.1◦ C throughout the entire process. The total electricity consumption and electricity price costs were reduced by 12.11% and 22.54%, respectively. In summary, the proposed method has good performance for HVAC system application, which can effectively realize energy saving and emission reduction and improve human comfort. This method makes important contributions to promote the construction of smart cities and the development of green buildings.

Motion planning of multiple unmanned vehicles using sequential convex programming‐based distributed model predictive control

AbstractThis article tackles the complex challenge of multi‐vehicle motion planning by presenting the novel Sequential Convex Programming‐based Distributed Model Predictive Control (SCP‐DMPC) algorithm. Existing methods often struggle with the nonconvex nature of optimization in multi‐vehicle motion planning, frequently compromising on computational efficiency. SCP‐DMPC innovatively combines the predictive control of DMPC with the optimization prowess of SCP to address these issues efficiently, while adhering to both physical and operational constraints. To ensure precise attainment of target poses, a three‐stage control strategy is introduced, with theoretical analysis on its recursive feasibility and asymptotic stability, enhancing the reliability of the algorithm. Moreover, a heuristic‐based deadlock resolution scheme is devised to prevent vehicle stalling, a common issue in cooperative motion. Validated through simulations in challenging scenarios, including symmetric position swapping and obstacle‐laden formation transition, SCP‐DMPC demonstrates superior adaptability, precision, and efficiency. These results underscore its potential for robust, real‐time unmanned vehicle applications, suggesting new directions for future research and development in the field.

An iterative learning-based integrated motion planning and control method for autonomous patrolling of unmanned surface vehicles

Abstract The minimization of maritime patrol durations and the optimization of patrol trajectories are still a prevailing challenge for autonomous navigation of unmanned surface vehicles (USVs). In this paper, we propose an integrated trajectory planning and control method to achieve time-optimal autonomous patrol of USVs by a data-efficient iterative learning-based predictive control algorithm. We build the optimization problem of the algorithm by introducing a local cost and a local safe constraint set for closed-loop efficient data-driven learning. To guarantee recursive feasibility, stability and convergence properties of the optimization algorithm at each iteration, the local cost and the local constraint set are designed and update iteratively using the historical vehicle states at previous iterations as a dataset. Different from traditional optimization methods, the proposed method does not require a reference path. The cost function of the optimization method decreases monotonically and converge to obtain a time-optimal control law for trajectory planning and tracking of USVs only after several iterations. The proposed approach is validated on the robotic operation system in typical maritime patrol scenarios. The results show that the proposed method implement a 5% reduction of patrol time, fewer lateral track errors, and smoother trajectories compared to the traditional MPC algorithm under water flow conditions. In addition, Enhancements in patrol operation efficiency of USVs are achieved by the algorithm, even under the constraints of varied patrol paths, attesting to its versatility.