Model Predictive Control Theory Research Articles

Hybrid model predictive control for vehicle trajectory tracking with integrated attitude adjustment

This paper presents a vehicle trajectory tracking method that integrates attitude adjustment. The method utilizes the asymmetric adjustment of the damping of the adjustable damping shock absorber to lift the left and right vehicle body and carry out the attitude adjustment of the vehicle. Additionally, it actively tilts toward the inside of the curve during turning. In order to address the nonlinear constraints of damping asymmetry regulation and the mixed logic relationship between suspension asymmetry damping switching and left and right body lifting during body attitude regulation, a mixed logic dynamic system (MLD) for vehicle attitude adjustment is established. Subsequently, the vehicle trajectory tracking controller is designed using the hybrid model predictive control theory (HMPC), which integrates active steering control and attitude adjustment. The optimal control problem of hybrid systems is transformed into a hybrid integer quadratic programming problem and solved through finite time domain rolling optimization. The effectiveness of the proposed integrated controller is confirmed through Simulink simulation. Compared with pure tracking control and roll tracking control with zero roll angle as the control target, this controller demonstrates its ability to effectively track the reference trajectory and enhance the vehicle’s dynamic performance.

Research on Trajectory Tracking Control of a Semi-Trailer Train Based on Differential Braking

How to improve the driving performance of the vehicle while carrying out path tracking control has become a hot issue in current research. In this paper, an MPC (Model predictive control) path tracking control algorithm incorporating differential braking control is proposed. By establishing a vehicle dynamics model of a semi-trailer train, the model predictive control theory is adopted for path tracking. Then, the vehicle dynamics model, considering the additional yaw moment, is established to design the differential braking control strategy. Under low-speed working conditions, the PID (Proportional Integral Derivative) algorithm is used to solve the additional yaw moment with the yaw rate of the tractor traveling alone as the desired value. Under high-speed working conditions, the Fuzzy PID algorithm is used to solve the additional yaw moment with the control objective of reducing the articulation angle. Simulation models are built using MATLAB/Simulink, and TruckSim for numerical experimental validation. The numerical experimental results show that the differential braking control method proposed in this paper can improve the maneuverability of vehicles driving in low-speed conditions and the stability of vehicles driving in high-speed conditions without decreasing the precision of path tracking control.

Feedback delay of sports intelligent learning system based on model predictive control and artificial intelligence

At present, in terms of model predictive control theory and model applications, two-layer segmented linear grid models are dominant. The two-layer piecewise linear lattice model is more effective, does not require too many parameters and is easy to calculate. Since the multi-layer model predictive control is based on a two-layer model, it is very important to study the mechanism and optimization methods of the two-layer model. Artificial intelligence comes from computer science. It tries to understand the nature of intelligence and create new intelligent machines that can react like human intelligence. Research in this field involves robotics, speech recognition, image recognition, natural language processing, etc. Since the emergence of artificial intelligence, theory and technology have gradually matured, and its application fields have also increased. The technological products created by artificial intelligence will embody the development of human wisdom in the future. Sports intelligence learning was first proposed by Lins. Linsley found that under laboratory conditions, by observing and recording the learner's behavior frequency and reaction speed, and adjusting the learning activities based on this, a better learning effect can be obtained. The combination of sports intelligence learning and behaviorist learning theory forms a systematic and accurate teaching method. Video system feedback delayed learning is a part of multimedia education technology. In recent years, multimedia technology has been widely used in various educational fields. By reviewing the application research of multimedia teaching technology in education, we have determined the areas related to the delay of the video system feedback, and guided the application research of the video feedback system in education.

Optimal allocation of hybrid energy storage capacity of DC microgrid based on model predictive control algorithm

To effectively enhance the safety, stability, and economic operation capability of DC microgrids, an optimized control strategy for DC microgrid hybrid energy storage system (HESS)(The abbreviation table is shown in Table 2) based on model predictive control theory is proposed. Based on the characteristics of supercapacitors and batteries, system safety requirements, and various constraints, a predictive model for a hybrid energy storage DC microgrid is established. By defining its optimization indicators, designing an energy optimization management strategy, and transforming it into a quadratic programming problem for solution, the reasonable scheduling of power in the DC microgrid has been achieved. In addition, a power control method was proposed for the system without constraints. The simulation experiment results show that at the initial sampling time, the system operates normally, and the MPC algorithm allocates two types of energy storage devices to discharge to meet the net load demand, without absorbing electricity from the external network. At the 30th sampling point, the net load increases, and the MPC controller obtains the optimal solution of the control problem based on the known net load prediction data at the previous sampling time. It outputs the operating reference values of each output unit at the next time. Starting from the 100th to 199th sampling points, SOC UC falls below the lower limit of the safety interval, and the system enters situation 4 mode. The external network output assists the battery in working. At the 131st sampling point, the net load decreases, the system enters Situation 3 mode, and the battery operates independently. Until the 179th point, SOC B was also below the lower limit of its safety interval, and the system entered situation 5 mode, completely maintaining system power balance by external network power. Starting from point 201, the net load becomes negative, and the system charges the HESS according to instructions and stops the external power grid energy transmission. Conclusion: The feasibility and effectiveness of the proposed optimization management strategy have been verified.

MPC-based LFC for interconnected power systems with PVA and ESS under model uncertainty and communication delay

In this paper, a cloud-edge-end collaboration-based control architecture is established for frequency regulation in interconnected power systems (IPS). A model predictive control (MPC)-based load frequency control strategy for the IPS with photovoltaic aggregation and energy storage systems under model uncertainty and communication delay is proposed. This can effectively overcome the issues of model uncertainty, random load perturbation and communication delay. First, a state space model for the IPS is constructed. To coordinate the frequency and contact line power fluctuation of the IPS, a robust controller based on the theory of MPC is then designed. Then, considering the communication delay of frequency response commands during transmission, a predictive compensation mechanism is introduced to eliminate the effect of delay while considering model uncertainty. Finally, simulation results verify the effectiveness and robustness of the proposed control strategy.

Distributed Dual Closed-Loop Model Predictive Formation Control for Collision-Free Multi-AUV System Subject to Compound Disturbances

This paper focuses on the collision-free formation tracking of autonomous underwater vehicles (AUVs) with compound disturbances in complex ocean environments. We propose a novel finite-time extended state observer (FTESO)-based distributed dual closed-loop model predictive control scheme. Initially, a fast FTESO is designed to accurately estimate both model uncertainties and external disturbances for each subsystem. Subsequently, the outer-loop and inner-loop formation controllers are developed by integrating disturbance compensation with distributed model predictive control (DMPC) theory. With full consideration of the input and state constraints, we resolve the local information-based DMPC optimization problem to obtain the control inputs for each AUV, thereby preventing actuator saturation and collisions among AUVs. Moreover, to mitigate the increased computation caused by the control structure, the Laguerre orthogonal function is applied to alleviate the computational burden in time intervals. We also demonstrate the stability of the closed-loop system by applying the terminal state constraint. Finally, based on a connected directed topology, comparative simulations are performed under various control schemes to verify the robustness and superior performance of the proposed scheme.

Front wheel angle control for steering system of intelligent commercial vehicle based on model predictive control

AbstractAccurate control of front wheel angle is essential in intelligent vehicle electric‐hydraulic power steering (EHPS) system, because it has a significant impact on the safety, comfort, and reliability of vehicle steering. However, the accurate control of front wheel angle is a challenging problem due to the non‐linearity of hydraulic power in steering system and the uncertainty of steering resistance. In this paper, a model predictive control (MPC) method for front wheel angle based on steering resistance estimation is proposed. Firstly, through theoretical analysis and practical experiment of EHPS system model, the relationship between front wheel angle and steering wheel angle and the non‐linear characteristics of hydraulic power are obtained. Secondly, a sliding mode observer is established to estimate the front axle lateral tyre force and steering resistance, which is verified by MATLAB/TruckSim co‐simulation. Then a controller based on the MPC theory is designed and applied to the accurate control unit of front wheel angle in the EHPS system for real‐time control. Finally, the simulation and test platform results show that the control algorithm proposed in this paper can precisely control the vehicle's front wheel turning angle within a certain frequency range.

Vibration analysis and adaptive model predictive control of active suspension for vehicles equipped with non-pneumatic wheels

In this paper, an adaptive controller is proposed for an active suspension system to achieve optimal compromise performance for vehicles equipped with non-pneumatic wheels under different road conditions. Firstly, the effective vertical stiffness of the non-pneumatic wheel (NPW) was identified through the static force-deflection tests. Then, the effect of the variations in NPW stiffness and mass on the vibration responses was investigated using a quarter-vehicle model. In order to coordinate the ride comfort and handling performance of the vehicle for different road excitations, an adaptive controller was synthesized using the model predictive control (MPC) theory together with an H ∞ state observer. The control gains for different control objectives were determined using a genetic algorithm (GA). Simulations indicate that the proposed controller can adapt to different road excitations and effectively enhance the dynamic performance of the vehicle. Specifically, by applying adaptive control, the root-mean-square (RMS) value of sprung mass acceleration (SMA) and the dynamic wheel load (DWL) coefficient are reduced by 19.4% and −9.3% on Class B roads and 12.4% and 3.8% on Class C roads, respectively, which is superior to the modified skyhook control (19.4% and −11.8% on Class B roads, and 19.3% and −12.3% on Class C roads). The effectiveness of simulation results was subsequently verified through hardware-in-the-loop experiments.

A decentralized peer-to-peer energy trading strategy considering flexible resource involvement and renewable energy uncertainty

With the widespread application of distributed power generation, traditional energy consumers are becoming active prosumers. Giving full play to the role of the electricity market in resource allocation, peer-to-peer (P2P) energy trading between multiple prosumers and consumers helps increase the revenue of electricity sales and reduce the cost of electricity consumption. The participation of flexible resources in P2P energy trading achieves local power complementarity, improves benefits, and reduces the operating pressure of lines. To ensure the benefits of prosumers and stimulate their enthusiasm, it is necessary to propose an effective trading strategy to fully allow flexible resources to participate in P2P energy trading. Therefore, this paper establishes a P2P energy market based on the continuous double auction (CDA) mechanism. Based on the model predictive control (MPC) theory, we propose a minimum cost rolling optimization model to optimize the bidding quantity of flexible resources. Moreover, the Automatic Learning (AL) pricing strategy with privacy and learning is proposed. The AL bidding price strategy enables interactive learning among prosumers. In addition, the influence of bidding price on bidding quantity during the trading process is also considered. Furthermore, this paper considers the impact of renewable energy source (RES) uncertainty on the bidding decisions of prosumers. Finally, the effectiveness of the proposed strategy is verified by the simulation results of cases. The simulation results demonstrate that the proposed strategy can improve the benefits for prosumers, enhance the local balance ability, achieve price learning, and increase the transaction rate.

High-Efficiency Navigation of Nonholonomic Mobile Robots Based on Improved Hybrid A* Algorithm

With the development of automation technologies, autonomous robots are increasingly used in many important applications. However, precise self-navigation and accurate path planning remain a significant challenge, particularly for the robots operating in complex circumstances such as city centers. In this paper, a nonholonomically constrained robot with high-precision navigation and path planning capability was designed based on the Robot Operating System (ROS), and an improved hybrid A* algorithm was developed to increase the processing efficiency and accuracy of the global path planning and navigation of the robot. The performance and effectiveness of the algorithm were evaluated by using randomly constructed maps in MATLAB and validated in a practical circumstance. Local path planning and obstacle avoidance were carried out based on the model predictive control (MPC) theory. Compared with the conventional A* + DWA (dynamic window approach) method, the average searching time was reduced by 12.62~24.5%, and the average search length was reduced by 9.25~9.5%. In practical navigating tests, the average search time was reduced by 18~24%, and the average search length was reduced by 10.3~12%, while the overall path was smoother. The results demonstrate that the improved algorithm can enable precise and efficient navigation and path planning of the robot in complex circumstances.

A novel engine and battery coupled thermal management strategy for connected HEVs based on switched model predictive control under low temperature

Under a low-temperature environment, electric vehicles face serious environmental adaptability problems, and efficient vehicle thermal management strategies are urgently needed. This paper presents a novel engine–battery coupled thermal management strategy for connected hybrid electric vehicles (HEVs). An improved system structure for an engine–battery coupled thermal management system (engine–battery CTMS) is designed to avoid unnecessary heat loss. The control requirements of the engine–battery CTMS include minimum engine fuel consumption, minimum power battery aging damage and minimum system energy consumption, which constitutes a multi-objective optimal control problem in a finite time domain. Based on model predictive control (MPC) theory, a switched nonlinear MPC (NMPC) control strategy is proposed to solve the optimal control problem of the complex coupled multi-input multi-output system. To verify the effectiveness of the proposed strategy, three comparative experiments of the centralized NMPC-based and rule-based methods combined with the improved system structure and the unimproved system structure are designed. The results of the cosimulation experiment between MATLAB/Simulink and AMEsim under various driving cycles and different ambient temperatures show that the improved structure and switched control strategy confer great advantages in reducing the controller computation burden, engine fuel consumption, and power battery aging damage.

Model predictive control of non-holonomic systems: Beyond differential-drive vehicles

Non-holonomic systems are of immense practical value and increasingly subject to automation. However, controlling them accurately, e.g., when parking vehicles, is challenging for automatic control methods, including model predictive control (MPC). Combining results from MPC theory and sub-Riemannian geometry in the form of homogeneous nilpotent system approximations, this paper proposes the first comprehensive, ready-to-apply design procedure for MPC controllers to steer controllable, driftless non-holonomic systems into given setpoints. It can be ascertained that the resulting controllers nominally asymptotically stabilize the setpoint for a large-enough prediction horizon. The design procedure is exemplarily applied to four systems, including the kinematic car and a differentially driven mobile robot with up to two trailers. The controllers use a non-quadratic cost function tailored to the non-holonomic kinematics. Novelly, it is proven that a quadratic cost employed in an otherwise similar controller is insufficient to reliably asymptotically stabilize the closed loop for all considered vehicles. Since quadratic costs are the conventional choice in control, this highlights the relevance of the findings. To the knowledge of the authors, it is the first time that MPC controllers of the proposed structure are formulated and applied to non-holonomic systems beyond very simple ones, in particular (partly) on hardware.

PreSR: Neural-Enhanced Adaptive Streaming of VBR-Encoded Videos With Selective Prefetching

Variable Bitrate (VBR) video encoding can significantly improve the quality-of-experience (QoE) of viewing users due to its capability to provide much higher quality-to-bits ratio compared to Constant Bitrate (CBR) video encoding. However, the streaming of VBR-encoded videos suffers from large variance of video chunk size, which may directly result in frequent rebuffering if not properly handled. In this paper, we propose a novel neural-enhanced adaptive streaming framework for VBR-encoded videos called PreSR, which performs selective prefetching of video chunks to achieve a higher QoE for viewers. The design of PreSR is motivated by an important observation obtained from our measurement, namely, the video quality improvement and bandwidth savings brought by neural enhancement are more pronounced for low-resolution video chunks with complex scenes. By taking the above fact and the time required for neural enhancement into account, we formulate the problem into an optimization problem. Given that the problem is NP-hard, we design the PreSR framework, which is based on the model predictive control theory and also considers key features of VBR-encoded videos. PreSR parallelizes the download of video chunks and model inference processes to fully utilize the available compute resources. Finally, we conduct extensive experiments with real traces, and the results show that PreSR outperforms the state-of-the-art algorithms with an improvement up to 11.25% in terms of the average QoE.

Cooperative Strategy of Trajectory Tracking and Stability Control for 4WID Autonomous Vehicles Under Extreme Conditions

Trajectory tracking and stability control are two essential functions of autonomous vehicles, and there is inevitable mutual interference between them, especially under extreme conditions. Towards addressing this challenge, this paper proposes a novel cooperative strategy of trajectory tracking and stability control for four-wheel independent drive (4WID) autonomous vehicles. An adaptive trajectory tracking controller is designed based on the model predictive control (MPC) theory, in which the tire cornering stiffness is modified in real-time leverages the lateral force estimated by the square-root cubature Kalman filter (SCKF). Then, the vehicle stability controller is designed based on the sliding mode control (SMC), in which the tire force saturation constraint and the relative weight of yaw rate and sideslip angle are considered. Furthermore, a weight adaptive criterion with normalized stability index is defined to develop the cooperative strategy of trajectory tracking and stability control. The feasibility and adaptability of the proposed method to different extreme conditions are verified by hardware-in-the-loop (HIL) test and CarSim-Simulink co-simulation. Finally, an experimental case is presented to demonstrate the realizability of the proposed method.

Intelligent electric vehicle trajectory tracking control algorithm based on weight coefficient adaptive optimal control

The track tracking effect of intelligent vehicle directly affects the safety of vehicle and passengers. In the process of intelligent vehicle track tracking, the track tracking accuracy is related to many factors such as track curvature, road friction coefficient, and longitudinal speed change. In this paper, a new lateral and longitudinal coupling control algorithm is proposed. Based on the vehicle dynamics model and the optimal control theory, combined with the fuzzy control theory, the Fuzzy Linear Quadratic Regulator (FLQR) lateral optimal controller is designed, and feed-forward control and predictive controller are added. According to the real-time tracking lateral error and fuzzy control algorithm fed back by the system, the weight coefficient of the lateral displacement deviation in the cost function is dynamically adjusted; considering the coupling effect of lateral and longitudinal controllers of vehicle trajectory tracking control, a model predictive control (MPC) longitudinal speed controller is designed based on MPC theory, considering acceleration constraint and acceleration variation constraint, and taking lateral stability as evaluation index. A joint simulation platform is built based on CarSim and Simulink. The simulation results show that the designed lateral and longitudinal coupling controller of FLQR + MPC has better track tracking accuracy and can improve the driving stability of the vehicle; finally, the tracking effect of the designed algorithm is verified by real vehicle experiments. The maximum error of the designed controller algorithm in real vehicle tracking is 0.56 m, and the tracking effect is good.

Integrated application strategy of large-scale intelligent building based on renewable energy technology

ABSTRACT The integrated system of intelligent buildings constructed by incorporating renewable energy can meet the requirements of zero-carbon green buildings. In this paper, the photovoltaic network is used as the source of multiple energy sources, and hydrogen fuel cells are also incorporated. Considering the difference in cooling and heating loads between winter and summer, a smart building integrated system based on renewable energy is constructed. Model Predictive Control (MPC) theory is introduced to coordinately control and manage multi-energy building systems. The simulation results show that the ground source heat pump equipment is in a shutdown state in summer, while the maximum operating powers of the electrolyzer, hydrogen fuel cell and battery are 30, 10.1 and 10 kW, respectively. In winter, the refrigerators do not work, while the ground source heat pump operates at a maximum power of 10.8 kW, while the hydrogen fuel cell and battery operate at a maximum power of 13.28 and 10 kW, respectively. After the optimal management of MPC, the state of charge of the battery in winter and summer is controllable, ranging from 20% to 80%, and the hydrogen level content curve can also track the reference value curve well.

Hybrid model predictive control on lifting and dropping vehicle body via asymmetric damping adjustment

For suspension systems with asymmetric damping during the extension and compression strokes, the equilibrium position of the vehicle body will lift or drop. Inspired by this phenomenon, a novel idea of control the vehicle height by adjusting asymmetric damping via adjustable-damping semi-active suspensions is proposed. Meanwhile, a methodology to implement this idea is also presented. By accounting for nonlinear factors, such as the nonlinear constraints of the output damping force of an adjustable-damper and the logic control conditions of the asymmetric damping adjustment, this control system was designed as a hybrid system. Hybrid model predictive control theory was used to design this controller, and the switching control problem was transformed into a continuous-rolling finite-time-domain optimisation control problem. The feasibility of this idea was proven by simulations for single-wheel vehicle height control. The proposed idea makes it possible to use semi-active actuators for vehicle height adjustment to realise the control effect achieved by active actuators. Moreover, it has lower energy consumption, lower cost and faster response than active suspension, and provides a new method for short-term vehicle height and attitude control under some specific vehicle conditions, such as high-speed steering, and emergency obstacle avoidance, etc.

Cognitive Granular-Based Path Planning and Tracking for Intelligent Vehicle with Multi-Segment Bezier Curve Stitching

Unmanned vehicles are currently facing many difficulties and challenges in improving safety performance when running in complex urban road traffic environments, such as low intelligence and poor comfort performance in the driving process. The real-time performance of vehicles and the comfort requirements of passengers in path planning and tracking control of unmanned vehicles have attracted more and more attentions. In this paper, in order to improve the real-time performance of the autonomous vehicle planning module and the comfort requirements of passengers that a local granular-based path planning method and tracking control based on multi-segment Bezier curve splicing and model predictive control theory are proposed. Especially, the maximum trajectory curvature satisfying ride comfort is regarded as an important constraint condition, and the corresponding curvature threshold is utilized to calculate the control points of Bezier curve. By using low-order interpolation curve splicing, the planning computation is reduced, and the real-time performance of planning is improved, compared with one-segment curve fitting method. Furthermore, the comfort performance of the planned path is reflected intuitively by the curvature information of the path. Finally, the effectiveness of the proposed control method is verified by the co-simulation platform built by MATLAB/Simulink and Carsim. The simulation results show that the path tracking effect of multi-segment Bezier curve fitting is better than that of high-order curve planning in terms of real-time performance and comfort.

Trajectory tracking control of emergency supplies transport robots based on fuzzy MPC

'Contactless' distribution plays an important role in emergency supplies transportation, and the trajectory tracking control of emergency supplies transport robots is the key technology to achieving 'contactless' distribution precision. In this paper, a trajectory tracking control method for emergency supplies transport robots was proposed based on fuzzy MPC. Firstly, a kinematic model of the emergency supplies transport robots was established, and the motion parameters were identified by the recursive least square algorithm. Secondly, aiming at the velocity problem of trajectory tracking of emergency supplies transport robots, a fuzzy control method was proposed to optimize the velocity error dynamically. Thirdly, the model predictive control theory was introduced, and an expanded state observer was designed for the total disturbances to optimize the control of the tracking trajectory of the emergency supplies transport robots. Finally, a simulation model was built in Simulink, and the performance of the trajectory tracking controller was verified by co-simulation using CarSim/ Simulink. The simulation results show that, compared with the classical MPC trajectory tracking controller, the optimized velocity error MPC trajectory tracking controller improves the operational stability of the robots and the trajectory tracking accuracy by 13.18%.

Phasor measurement units‐based fast voltage control strategy for distribution network with high‐penetration distributed photovoltaic

AbstractAs the penetration of distributed photovoltaic (PV) in the distribution network continues to increase, the problem of voltage violation is becoming increasingly prominent. PV output has strong volatility and uncertainty, leading to higher requirements on speed and accuracy of voltage control. The voltage qualification rate of the distribution network with PV is difficult to guarantee if relying on traditional measuring installation and discrete regulators. Thus, it is necessary to introduce high‐frequency sampling devices and various continuous regulators in real‐time voltage control. In this paper, a fast voltage control strategy by real‐time coordination of dynamic voltage regulators (DVRs) and PV inverters based on phasor measurement units (PMU) and remote terminal unit (RTU) information is proposed. The voltage sensitivity is calculated online based on the fast measurement information of PMU. The equivalent models of rapid voltage regulators including DVR and PV inverter are established for the online extraction of voltage optimal control gradients. According to the model predictive control (MPC) theory, a feeder‐level global voltage control strategy is proposed comprehensively considering the objectives of voltage qualification rate, control cost, and network loss, which can realize real‐time voltage control in the distribution network at the millisecond scale. The effectiveness and rapidity of the proposed global voltage control strategy are verified in the IEEE 33‐bus system and a real representative distribution system for preventing voltage violation.