Hybrid Model Predictive Control 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 MLD Modeling and Predictive Control of Magnetically Coupled Resonant Bidirectional WPT System

The recent studies on magnetically coupled resonant bidirectional wireless power transfer (MCR-BWPT) systems disregard the challenges posed by nonlinearity, discrete switching action, and hybrid properties within the system. This research focuses on the D-LCL resonant compensation topology MCR-BWPT system. The switch tube’s switching state dictates the division of various working modes and the determination of the switching conditions between them. The coupling relationship between the continuous dynamic characteristics of the system and discrete events and the constrained conditions of the system are derived. The Hybrid System Description Language (HYSDEL) is used to build the Mixed Logic Dynamic (MLD) model of the system. The MLD model is employed as the prediction model, and the hybrid model predictive controller of the MCR-BWPT system is constructed according to the quadratic performance index. Finally, to verify the accuracy of the MLD model and the feasibility of the control strategy, the simulation model of the MLD model is established in MATLAB/Simulink. The study’s findings show that, in terms of response time at system startup and power fluctuation suppression, the approach put forward in this research performs better than both the conventional bilateral dual-phase-shift control strategy and the PQ-based bilateral power control strategy. The MCR-BWPT system can operate more steadily now that PQ’s bidirectional power control technique is in place. The system’s forward and reverse transmission efficiency is increased by 0.29% and 0.32% compared to the conventional bilateral dual-phase-shift control approach; the increases are 0.28% and 0.09%, each compared to the bilateral power control strategy based on PQ.

Networked explicit hybrid predictive control of an industrial benchmark process

Hybrid systems have acquired deep attention in research as many systems in industries and laboratories manifest hybrid dynamic behaviors. Optimal control design for hybrid systems exhibits challenges for controlling the interacting continuous and discrete dynamics. A double-tank hybrid system (DTHS) is proposed in this paper with a mixed-integer actuation mechanism. The proposed DTHS aims to control the liquid level of the two tanks simultaneously by a networked hybrid predictive controller. Hybrid frameworks are provided to investigate the system through reachability analysis. An explicit hybrid model predictive control (EHMPC) has been developed to control the levels of the DTHS. Hardware-In-the-Loop (HIL) simulation is performed for assessing the performance of the EHMPC with a hardware real-time networked controller. The networked real-time controller is constructed and implemented using the Node.js frameworks. The simulation and realtime results are presented and discussed to evaluate the hybrid controller performance.

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.

Developing cyber-physical system and digital twin for smart manufacturing: Methodology and case study of continuous clarification

Biopharmaceutical production has recently begun transitioning toward the construction of highly advanced, digitalised production facilities, as per Industry Revolution 4.0 or smart manufacturing. Clarification is the bridging step in biotherapeutics continuous production, and this study proposes a Digital twin (DT) and a cyber-physical system (CPS) for the Cadence™ Acoustic Wave Separator (CAS), that uses the novel acoustic wave separation (AWS) technology for clarification of Chinese hamster ovary (CHO) cells. While the CPS - DT serves as the core, a real-time model based on the empirical and mechanistic relationship has been implemented in tandem to present a highly reliable hybrid model so as to improve the adaptability of the model. The framework employs a distributed control system (DCS), the heart of the CPS architecture, that updates the parameters of the physical system in real-time, thereby simplifying process control. To evaluate the hybrid-model predictive control strategy in real time, three case studies were conducted, which involved introducing abrupt turbidity pulses of high, low, and multi values. The sudden deviation in the turbidity of the feed sample was controlled by adjusting the acoustic power of the chamber (the control variable). The use of the proposed controller resulted in a 5 % mean deviation from the setpoint in the first chamber, while a > 33 % mean deviation resulted in the absence of the controller. The study's outcome demonstrates the high effectiveness of hybrid model-based CPS - DT control in consistently achieving high cell separation efficiency (> 90 %) and integrating the unit operation in a continuous processing train to establish a smart manufacturing prototype for the biopharmaceutical industry.

Explicit hybrid MPC for the lateral stabilization of electric vehicle system

This paper presents a hybrid Model Predictive Control (hMPC) approach to improve Electric Vehicle (EV) stability when cornering or in high-risk driving conditions. By using approximations of tire force characteristics, the EV system proposed in this study is considered as a Piecewise Affine (PWA) system which is a class of Hybrid Dynamical Systems (HDSs). First, a hybrid modeling of the studied system is developed within the PWA framework. Then, the synthesis of an explicit hybrid MPC law, based on the integration of the Active Front-wheel Steering control method (AFS) and Direct Yaw moment Control method (DYC), can be established. The purpose of the PWA-MPC is to force the yaw rate and sideslip angle of the EV system to follow the given references to guarantee lateral stability. The calculation of the control laws of this proposed approach is performed offline using multiparametric programming. Finally, this paper investigates the effectiveness of the designed controller by performing a set of simulations to analyze the efficiency and robustness of the proposed approach. A comparative study is addressed in this work to demonstrate the efficiency of the proposed hybrid MPC controller compared to the conventional MPC approach which considers the linear model of the system. Another simulations are carried out to assess the robustness of the proposed control, important disturbances and uncertainties were incorporated such as the change of the additional mass, the change of terrain and the high velocity. The simulation results show that the PWA EV system model with the designed controller is able to ensure lateral stability by keeping the sideslip angle and yaw rate tracking the ideal values.

Distributed Hybrid Model Predictive Secondary Control of DC Microgrids with Random Communication Disorders

Distributed Hybrid Model Predictive Secondary Control of DC Microgrids with Random Communication Disorders

Hybrid Model Predictive Control for Unmanned Ground Vehicles

Hybrid Model Predictive Control for Unmanned Ground Vehicles

Software-defined control of an emulated hydrogen energy storage for energy internet ecosystems

The increasing heterogeneity and scalability of the Internet of everything, especially, the Energy Internet (EI), is a prompt for novel engineering paradigms. The current infrastructure of hardware technologies besides the available software stack is becoming obsolete for handling the advanced requirements of the smart energy ecosystem. Accordingly, software definitions of the Internet of Things (IoT) stack functions are gaining interest to provide more flexible and scalable implementations. This paper investigates a sophisticated softwarization explicit hybrid model predictive control strategy for energy storage facility systems through the accessor design pattern. This strategy is presumed to support and facilitate the integration of higher-level analytic and control functionalities within the IoT stack at edges and nodes. Simulation is conducted with the Ptolemy II software to evaluate the execution semantics of the proposed strategy. In addition, real-time experiments on a lab-scale hydraulic tank process with both abrupt and gradual commands test scenarios showed the efficiency and reliability of the accessor-based controllers for achieving the control objectives for the benchmark storage facility with an abstract implementation. The analysis shows that this process can serve as a benchmark to mimic the charge and discharge cycles of storage elements in EI ecosystems.

Hybrid Model Predictive Control of a Two-Body Wave Energy Converter with Mechanically Driven Power Take-Off

In this paper, a variable damper is proposed to regulate the efficiency of a two-body wave energy converter (WEC) with mechanically driven power take-off (PTO). The variable damper introduces logic constraints into the WEC system, which can be translated into a mixed logical dynamical form with the dynamics of real-valued variables, the dynamics of logic variables, and their interactions. A hybrid model predictive control (MPC) method is used to determine the control inputs, which has the capacity to handle various constraints. The performance is assessed through simulations to evaluate the effectiveness of the proposed method. The achievable performance improvements of the proposed hybrid MPC are shown by means of comparative analysis with uncontrolled WEC devices. The results show that the proposed hybrid MPC has a high requirement on the lower bound of the variable damper and the maximum damping is used only at low relative velocities to achieve the optimum phase, like latching control. The hybrid MPC performs exceptionally well under wave conditions with a small significant wave height and long wave period, improving the power generation of the uncontrolled system up to 22.5%. And, the prediction error has a significant effect on hybrid MPC performance, especially for long prediction horizon.

Energy-efficient hybrid model predictive control of mobile refrigeration systems

Efficiency and temperature compliance of last-mile refrigerated transport suffer from more severe disturbances than stationary applications. The significant energy expenditure and diminished quality or loss of goods have been tackled recently by improved hardware design. However, standard operating strategies only partly use accompanying intrinsic potential. This article introduces an enhanced model predictive control scheme for mobile refrigeration systems with secondary loop cooling units, aiming toward energy-efficient operation complying with temperature restrictions. Its real-time capable optimization covers operational flexibilities and dynamics (storage capacities, door openings, permissible temperature range) more extensively than state-of-the-art solutions. Experimental validation on a specifically designed test rig uses a representative test run with expected and unexpected door openings and a pull-down sequence. A proportional–integral and a standard predictive controller serve for comparison. Performance assessment shows energy savings of 16.4% and improvement in temperature compliance of 3.4% of the advanced predictive over the proportional–integral scheme. If door openings are known upfront, energy consumption decreases by even 29.6%. The standard predictive algorithm shows a similar energy performance but substantially declined temperature compliance as it cannot harness the entire flexibility. Thus, the proposed advanced strategy significantly contributes to more efficient refrigerated transport with reduced environmental impact and loss of goods.

Real-time coordination of multiple shuttle-conveyor-belts for inventory control of multi-quality stockpiles

A hybrid model predictive control formulation with continuous and discrete variables that models multiple crushed-ore stockpiles per conveyor-belt is proposed for better conveying and stockpiling performance. The aim is to minimize the inventory level or holdup squared deviations from targets by varying the fill-time, idle-time, or run-length of the shuttle-conveyor-belts’ tripper-cars over multiple stockpiles’ positions (the discrete actuation or manipulated variable). In the problem, amounts of crushed-ore dropped by gravity from conveyor-belts fill up the upper-level of the stockpiles considering quality balances to specify blended ore by the mixtures of the material dropped by synchronized shuttle-conveyor-belts’ tripper-cars. The problem is solved as a mixed-integer quadratic programming (MIQP) that uses amount-of-quality balances as a linear (LP) approximation, tightening the MIQP-solutions to schedules of simultaneous blending of different quality shuttle-conveyor-belts’ raw materials. Results of the initialization and steady-state stages of the inventory-control and logistics-quality optimization highlight the modeling and solution strategies.

Applications of hybrid model predictive control with computational burden reduction for electric drives fed by 3-phase inverter

Model predictive control (MPC) is recently emerging as an efficient and promising technique for the control of power converters. In the conventional MPC algorithm, the control objectives are usually estimated and evaluated for a large/definite number of switching states. Since prediction and evaluation are done for all possible states, massive amounts of estimations are needed, moreover, the computational burden is more challenging with the increase of control objectives. In this paper, a computationally efficient version of the finite control set-MPC (FCS-MPC) is proposed to decrease the calculation effort of the MPC algorithm likewise minimizing its execution time to enforce its vast application for the control of three-phase power converters. The suggested procedure is to eliminate the current predictions as well as reduce the number of available switching states that need to be estimated by the algorithm which reduces considerably the amount of time consumed by these computations. The studied techniques achieved nearly the same performance with an interesting reduction in the algorithm execution time accomplished by the proposed modified FCS-MPC algorithms.

Hybrid Model Predictive Control with Penalty Factor Based on Image-Based Visual Servoing for Constrained Mobile Robots

For the constrained mobile robot automatic parking system, the hybrid model predictive control with a penalty factor based on image-based visual servoing (IBVS) is proposed to address the problem of feature point loss and emergency braking in dynamic obstacle scenarios caused by excessive target bias gain when using traditional IBVS control methods. The traditional IBVS control is transformed into an optimization problem with constraints in the finite time domain, by defining the optimization function based on the mobile robot’s positional deviation and image feature point deviation, while using actuator saturation and speed limit as constraints. Based on this, a convex optimization function with penalty factors is defined and combined with incremental model predictive control. This control strategy could ensure the emergency braking performance of the mobile robot when the image feature points are massively obscured by obstacles in dynamic scenes, while improving the accuracy and real-time of its trajectory tracking control. Finally, simulation comparisons are conducted to verify the effectiveness of the proposed control method.

Hybrid model predictive control of renewable microgrids and seasonal hydrogen storage

Optimal energy management of microgrids enables efficient integration of renewable energies by considering all system flexibilities. For systems with significant seasonal imbalance between energy production and demand, it may be necessary to integrate seasonal storage in order to achieve fully decarbonized operation. This paper develops a novel model predictive control strategy for a renewable microgrid with seasonal hydrogen storage. The strategy relies on data-based prediction of the energy production and consumption of an industrial power plant and finds optimized energy flows using a digital twin optimizer. To enable seasonal operation, incentives for long-term energy shifts are provided by assigning a cost value to the storage charge and adding it to the optimization target function. A hybrid control scheme based on rule-based heuristics compensates for imperfect predictions. With only 6% oversizing compared to the optimal system layout, the strategy manages to deliver enough energy to meet all demand while achieving balanced hydrogen production and consumption throughout the year.

Practical hybrid model predictive control for electric pneumatic braking system with on-off solenoid valves

Accurate and rapid braking pressure regulation in electric pneumatic braking systems (EPBS) is vital to vehicle safety. Due to the switching behaviors of the on-off solenoid valves, the operation of the EPBS shows a hybrid nature with both continuous variables and discrete events, which raises the hybrid control problem. One of the possible solutions is to employ the hybrid model predictive controller with the mixed logical dynamical (MLD) model based on the linear approximation of the system dynamics. However, the nonlinearity and complexity of the EPBS make the MLD model obtained by linearizing the system equations directly require high storage and computing capacity. To address these issues, this article presents a practical hybrid model predictive controller based on the system dynamics simplified expressions considering the EPBS pressure variations caused by on-off solenoid valve states at the current sampling time and the last sampling time. The relationship between the pressure variations and the on-off solenoid valve states is first studied by the system mathematical model, followed by applying the mixed logic dynamical modeling approach to establish the hybrid model of the pressure continuous dynamics with discrete features of on-off solenoid valves. Based on these, a hybrid model predictive controller is formulated to solve the EPBS pressure control problem. The simulations and bench experiments are carried out to verify the controller. Besides, an existing model predictive control (MPC) controller is compared with the proposed controller. All the results demonstrate the effectiveness of the hybrid model predictive controller.

System Identification and Hybrid Model Predictive Control in Personalized mHealth Interventions for Physical Activity.

The application of control systems principles in behavioral medicine includes developing interventions that can be individualized to promote healthy behaviors, such as sustained engagement in adequate levels of physical activity (PA). This paper presents the use of system identification and control engineering methods in the design of behavioral interventions through the novel formalism of a control-optimization trial (COT). The multiple stages of a COT, from experimental design in system identification through controller implementation, are illustrated using participant data from Just Walk, an intervention to promote walking behavior in sedentary adults. ARX models for individual participants are estimated using multiple estimation and validation data combinations, with the model leading to the best performance over a weighted norm being selected. This model serves as the internal model in a hybrid MPC controller formulated with three degree-of-freedom (3DoF) tuning that properly balances the requirements of physical activity interventions. Its performance in a realistic closed-loop setting is evaluated via simulation. These results serve as proof of concept for the COT approach, which is currently being evaluated with human participants in the clinical trial YourMove.

Hybrid model predictive control for premixed natural gas engine as distributed generator

The access to electricity is increasing worldwide with a considerable growing rate per year. Unfortunately, traditional large fossil power technologies based on coal and oil lead to a major concern in environmental pollution, so the distributed power generation utilizing natural gas engine is taken into consideration. In this paper, a hybrid model predictive control (HMPC) strategy is proposed for natural gas engines in distributed power generation. The original fourth-order double-input double-output (DIDO) nonlinear engine model is transformed into two simpler models, one is a first-order model for air–fuel ratio (AFR) control, and a linear model predictive control (LMPC) strategy is designed. The other is a second-order nonlinear model for speed control, and the AFR is used as one of its two inputs to design nonlinear model predictive control (NMPC) strategy. The advantage of this is that each model is simpler than the original fourth-order model, and prediction horizon can be lengthened to achieve better control performance. For the optimization problem of NMPC, a switching mechanism strategy is proposed. It can effectively reduce the number of SQP iterations in both transient and steady-state conditions. Both simulations and experiments verify the feasibility of the HMPC and the effectiveness of the switching mechanism.

Platoon-centered control for eco-driving at signalized intersection built upon hybrid MPC system, online learning and distributed optimization part I: Modeling and solution algorithm design

Inspired by connected and autonomous vehicle (CAV) technologies, extensive studies have developed open-loop vehicle-level trajectory planning or speed advisory to promote eco-driving at traffic intersections. But few studies work on platoon-level closed-loop trajectory control, which can better sustain stream traffic smoothness and efficiency. Motivated by this research gap, this study developed a system optimal platoon-centered control for eco-driving (PCC-eDriving), which can guide a platoon mixed with connected and autonomous vehicles (CAVs) and human-driven vehicles (HDVs) to smoothly approach, split as needed, and then sequentially pass signalized intersections, while reducing or even avoiding sharp deceleration and red idling. The effort is separated to Part I and Part II to prevent a lengthy article. Specifically, Part I of this study modeled the PCC-eDriving as a hybrid Model Predictive Control (MPC) system. It involves three MPC controllers for platoon trajectory control and a mixed-integer nonlinear program (MINLP) for optimal splitting decisions. Each MPC controller is integrated with robust vehicle dynamics and an online adaptive curve learning algorithm to factor control and vehicle driving uncertainties. An active-set-based optimal condition decomposition algorithm (AS-OCD) was developed to efficiently solve the MPC controllers' large-scale optimizers in a distributed manner. The numerical experiments built upon the field and simulated data indicated that the PCC-eDriving could significantly improve traffic smoothness and efficiency while reducing energy consumption and emission at urban signalized intersections. Part II will analyze and prove the sequential feasibility and the Input-to-State stability of the hybrid MPC system, as well as the convergence of the AS-OCD solution approach to theoretically sustain the performance of the hybrid MPC system.

Platoon-centered control for eco-driving at signalized intersection built upon hybrid MPC system, online learning and distributed optimization part II: Theoretical analysis

Extensive studies developed eco-driving strategies to smooth traffic and reduce energy consumption and emission at signalized intersections. Part I (Zhang and Du, 2022) of this study developed a novel platoon-centered control for eco-driving (PCC-eDriving), considering a mixed flow involving Connected and Autonomous Vehicles (CAVs) and Human-Driven Vehicles (HDVs). This PCC-eDriving is mathematically implemented by a hybrid Model Predictive Control (MPC) system and solved by an active-set based optimal condition decomposition algorithm (AS-OCD). It generates discrete control laws for a platoon to approach, split as sub-platoons as needed, and then pass the intersections smoothly and efficiently. Though the numerical experiments validated the effectiveness, the theoretical properties of the hybrid MPC system and the solution algorithms were not investigated. Part II of this study thus focused on these theoretical analyses. Mainly, we first analyzed and proved the MPC sequential feasibility and hybrid system switching feasibility to guarantee the control continuity of the hybrid MPC system. Next, we factored CAV control uncertainties and proved the Input-to-state stability of the robust MPC controller. These proofs theoretically ensured the effectiveness and robustness of the hybrid MPC system. Last, we proved the solution optimality and convergence of the AS-OCD algorithm. It confirmed that the AS-OCD algorithm could find the global optimal solutions for the MPC optimizers with a linear convergence rate.