Upper-layer Controller Research Articles

A Coordinated Control Strategy for Efficiency Improvement of Multistack Fuel Cell Systems in Electric–Hydrogen Hybrid Energy Storage System

A two-layer coordinated control strategy is proposed to solve the power allocation problem faced by electric–hydrogen hybrid energy storage systems (HESSs) when compensating for the fluctuating power of the DC microgrid. The upper-layer control strategy is the system-level control. Considering the energy storage margin of each energy storage system, fuzzy logic control (FLC) is used to make the initial power allocation between the battery energy storage system (BESS) and the multistack fuel cell system (MFCS). The lower-layer control strategy is the device-level control. Considering the individual differences and energy-storage margin differences of the single-stack fuel cell (FC) in an MFCS, FLC is used to make the initial power allocation of the FC. To improve the hydrogen-to-electricity conversion efficiency of the MFCS, a strategy for optimization by perturbation (OP) is used to adjust the power allocation of the FC. The output difference of the MFCS before and after the adjustment is compensated for by the BESS. The simulation and experiment results show that the mentioned coordinated control strategy can enable the HESS to achieve adaptive power allocation based on the energy storage margin and obtain an improvement in the hydrogen-to-electricity conversion efficiency of the MFCS.

Observer-based hierarchical distributed model predictive control for multi-linear motor traction systems

This paper proposes an observer-based hierarchical distributed model predictive control (MPC) strategy for ensuring speed consistency in multi-linear motor traction systems. First, a communication topology is considered to ensure information exchange. Secondly, the control architecture of each agent is divided into upper layers and lower layers. The upper layer utilizes a distributed MPC method to track the leader’s speed. The lower layer uses a decentralized MPC method to track the command signals sent by its upper layer controller. In addition, to eliminate the negative impact of disturbance, a nonlinear disturbance observer is designed. We then prove the asymptotic stability of the entire system by properly designing the Lyapunov equation. Finally, the feasibility of the proposed strategy is verified based on several simulations.

Minimum decision of collision injury based on drivable area in unavoidable scene

The existing technology for assisted driving collision avoidance has improved the active safety of vehicles, but it is primarily designed for scenarios where collisions can be avoided. It is not suitable for situations where pedestrians suddenly intrude. Therefore, there is a need to develop a control strategy that can minimize collision injuries in unavoidable situations. This study focuses on typical collision scenarios where pedestrians suddenly intrude. It proposes a real-time online double-layer optimal pre-collision control strategy based on drivable area, using the Head Injury Criteria ( HIC) as a quantifiable measure of collision injury. The upper-layer controller designs a cost function based on a three Degrees of Freedom (DOF) collision solving model to minimize collision injuries. It takes the constraints of vehicle dynamics, maximum front wheel steering angle, pedestrian avoidance into account, and the optimal pre-impact state point with the minimum HIC value is calculated in real-time. Meanwhile, the lower-layer controller is designed to track the optimal pre-impact state point using Model Predict Control (MPC) algorithm. The effectiveness of the proposed control strategy is verified through Simulink simulation, achieving the dual objectives of avoiding pedestrians suddenly intruding and minimizing passenger collision injuries.

Yaw Stability Control of Unmanned Emergency Supplies Transportation Vehicle Considering Two-Layer Model Predictive Control

The transportation of emergency supplies is characterized by real-time, urgent, and non-contact, which constitute the basic guarantee for emergency rescue and disposal. To improve the yaw stability of the four-wheel-drive unmanned emergency supplies transportation vehicle (ESTV) during operation, a two-layer model predictive controller (MPC) method based on a Kalman filter is proposed in this paper. Firstly, the dynamics model of the ESTV is established. Secondly, the improved Sage–Husa adaptive extended Kalman filter (SHAEKF) is used to decrease the impact of noise on the ESTV system. Thirdly, a two-layer MPC is designed for the yaw stability control of the ESTV. The upper-layer controller solves the yaw moment and the front wheel steering angle of the ESTV. The lower-layer controller optimizes the torque distribution of the four tires of the ESTV to ensure the self-stabilization of the ESTV operation. Finally, analysis and verification are carried out. The simulation results have verified that the improved SHAEKF can decrease the state estimation error by more than 78% and achieve the noise reduction of the ESTV state. Under extreme conditions of high velocity and low adhesion, the average relative error is within 6.77%. The proposed control method can effectively prevent the instability of the ESTV and maintain good yaw stability.

A real-time hierarchical control method for safe human–robot coexistence

In modern industrial environments, robots are expected to work close to human operators collaborating with them in completing various tasks, e.g., collaborative assembly or delivery of objects. However, it is difficult to take task optimization into account under the premise of ensuring safety in a human-involved dynamic environment. Therefore, a real-time hierarchical control method containing two hierarchical optimal controllers with complementary functions is proposed to tackle the above problems. The upper-layer Model Predictive Controller aims at performing primary tasks, such as end-effector pose tracking, singularity, and joint limit avoidance. It is formulated as a Bayesian Inference problem with a Gaussian process prior and an exponential likelihood function. The resulting maximum a posteriori estimation problem can be solved efficiently using the Matrix-Scaled Stein Variational Gradient Descent and GPU. The upper-layer optimal controller aims at performing primary tasks, such as end-effector pose tracking, singularity, and joint limit avoidance. The lower-layer safety-critical controller, formulated as a constrained quadratic programming problem, is responsible for tracking the output interpolation of the higher-layer controller while respecting the safety constraints constructed in the form of Stochastic Control Barrier Functions. Both of the optimal controllers run repeatedly but with different frequencies (upper-layer controller: 20Hz, lower-layer controller: 40Hz). The proposed method provides a solution to deal with both collision avoidance and task constraints. Lastly, we conducted real-time simulation and experimental tests on a Doosan robot, and the test results validated the effectiveness of the proposed method.

Coordinated Frequency and Voltage Optimal Control of Wind Farm With Nonlinear Power Constraints

In the high-voltage direct current transmission system, if converter blocking occurs at the sending-end converter station, a serious power imbalance takes place in the sending-end grid, which causes severe over-frequency and over-voltage problems. To address this issue, a double-layer coordinated optimal control strategy based on model predictive control (MPC) is proposed in this article. By cooperatively regulating the active and reactive power of the wind farm at the sending end, it achieves coordinated frequency and voltage control. On the basis of MPC and moving horizon estimation, the upper-layer controller computes the optimal active and reactive power references of the wind farm. Subsequently, the optimized power references are fed into the lower-layer wind farm local controller for execution. A key feature of this strategy lies in its handling of nonlinear power constraints. Specifically, for a wind farm, coordinated frequency and voltage regulation faces a critical challenge of coupled nonlinear power constraints, which are formed by the coupled relationship between the active and reactive power outputs of the power converters. To overcome this challenge, the double description method is introduced to approximate and linearize the coupled nonlinear power constraints. Simulation results verify the effectiveness of the proposed strategy.

Single-Wheel Failure Stability Control for Vehicle Equipped with Brake-by-Wire System

In order to solve the problem of vehicle stability control after a single-wheel brake failure in the brake-by-wire system, a control strategy of braking force redistribution with a yaw moment is proposed to ensure the braking efficiency and stability of vehicles. In this strategy, a two-layer architecture is adopted. In the upper layer control, a fault factor is introduced to represent the real-time failure degree of the wheel, and the driver’s braking intention is perceived through the pedal travel and pedal speed of the driver. The braking force redistribution algorithm of the remaining three wheels is designed based on the wheel failure degree and braking intensity. In the lower control, according to the state parameters of the vehicle, the additional yaw moment, which controls the yaw rate and the sideslip angle of the vehicle, is calculated by using the sliding mode control theory, and the yaw moment is reasonably allocated to the normal wheel. By using MATLAB/Simulink and Carsim co-simulation, different braking strength and failure types are selected for simulation analysis. The simulation results show that the proposed control strategy can improve the braking efficiency and stability of the vehicle under different braking conditions.

Hierarchical Hybrid Control for Scaled Consensus and Its Application to Secondary Control for DC Microgrid.

This article addresses the scaled consensus problem for a class of heterogeneous multiagent systems (MASs) with a cascade-type two-layer structure. It is assumed that the information of the upper layer state components is intermittently exchangeable through a strongly connected communication network among the agents. A distributed hierarchical hybrid control framework is proposed, which consists of a lower layer controller and an upper layer one. The lower layer controller is a decentralized continuous feedback controller, which makes the lower layer state components converge to their target values. The upper layer controller is a distributed impulsive controller, which enforces a scaled consensus for the upper layer state components. It is proved that the two layer controllers can be designed separately. By considering the dwell-time condition of impulses and the feature of the strongly connected Laplacian matrix, a novel weighted discontinuous function is constructed for scaled consensus analysis. By using the Lyapunov function, a sufficient condition for scaled consensus of the MAS is derived in terms of linear matrix inequalities. As an application of the proposed distributed hybrid control strategy, a relaxed distributed hybrid secondary control algorithm for dc microgrid is obtained, by which the balance requirement on the communication digraph is removed, and an improved current sharing condition is obtained.

A Hierarchical Control Scheme for Adaptive Cruise Control System Based on Model Predictive Control

An adaptive cruise control (ACC) system can improve safety and comfort during driving by taking over longitudinal control of the vehicle. It requires the coordination between the upper-layer controller and the lower-layer actuators. In this paper, a hierarchical anti-disturbance cruise control architecture based on electronic stability control (ESC) system is proposed. The upper-layer controller outputs the desired longitudinal acceleration or deceleration to the lower-layer actuators. In order to improve the accuracy of model prediction and achieve the coordinated control of multiple objectives, an upper-layer model prediction cruise controller is established based on feedback control and disturbance compensation. In addition, based on the hydraulic control unit (HCU) model and the vehicle longitudinal dynamics model, a lower-layer nonlinear model predictive deceleration controller is proposed in order to solve the problems of pressure fluctuations and the low accuracy of small decelerations when ESC is used as the actuator for the ACC system. Finally, the simulation and experimental tests were carried out. The results show that the proposed control architecture can improve the stability and comfort of the cruise control process. Moreover, compared with the traditional PID deceleration controller, it effectively improves the deceleration control accuracy.

Hierarchical control of hybrid energy storage system in shipboard gas turbine power system with multiple pulsed power loads

Transient power fluctuations may occur in shipboard DC microgrids when switching a pulsed power load (PPL). However, shipboard power systems (SPSs) are isolated power grids with relatively low inertia and capacity, making them unable to respond immediately to sudden changes in pulsed loads, thereby resulting in considerable disturbances and even instability of the system. To mitigate such disturbances effectively, it is recommended to introduce energy storage devices to SPSs. In order to better leverage the buffering characteristics of energy storage devices, this paper establishes a simulation model of the SPS, which includes a micro gas turbine generator, a hybrid energy storage system (HESS), multiple PPLs and an energy management system (EMS). The HESS comprises a battery and a flywheel. Furthermore, a hierarchical coordinated control strategy for HESS in shipboard DC microgrids under multiple pulsed load conditions is proposed, which includes both an upper-layer control and a lower-layer control. The upper layer makes logical judgments through voltage difference and selects the appropriate energy storage mode for power supply. The lower layer adopts a series fuzzy logic control. To validate the effectiveness of the proposed control strategy, two scenarios of transient power mutation and total power demand exceeding the rated power of the generator were simulated by connecting different PPLs to the DC bus at different times. The results indicate that the control strategy effectively realizes the coordinated operation of HESS and gas turbine, as well as power allocation between flywheel and battery within HESS. Moreover, it reduces the frequency of charging and discharging of battery, improves battery life, and prevents the generator from running beyond the rated power.

Coordinated Longitudinal and Lateral Stability Improvement for Electric Vehicles Based on a Real-Time NMPC Strategy

Under extreme conditions, such as low friction surfaces, violent steering and urgent acceleration/deceleration, vehicle states change rapidly and are influenced by nonlinear and coupled vehicle dynamics. To improve vehicle stability under extreme conditions, a hierarchical control strategy is proposed for DDEVs. In the upper layer controller, a combined-slip tire model is adopted to improve the model accuracy under extreme conditions. A nonlinear model predictive control based controller is then designed to generate the desired tire slip ratios with the main objectives of tracking the desired yaw rate and suppressing the lateral velocity and tire slip ratios. In the lower layer controller, the disturbance on the driver’s torque requirement, which is disregarded by existing studies, is taken into account. Next, a linear predictive controller is designed to track the desired tire slip ratios by adjusting the motor torques. To improve the computational efficiency of the nonlinear predictive controller, a PMP-based, fast solving algorithm is proposed. The effectiveness of the proposed solving algorithm is checked by comparing the control performance with IPOPT. The proposed control strategy is evaluated by a series of HIL experiments. The HIL results show better performance in overall stability improvement and minimize the disturbance on the driver’s torque requirement.

Backstepping sliding mode control for an active hydraulically interconnected suspension

This paper presents a backstepping sliding mode control (BSMC) method for an active anti-roll hydraulically interconnected suspension (HIS) system. The proposed control strategy enables the active HIS system tracking desired anti-roll moment within the working area. Based on the tracking error, the BSMC controller calculates the flowrate of the control cylinder. This flowrate successively changes the pressure in the accumulators and cylinder chambers, and finally determines the output anti-roll moment of the HIS. The HIS system parameters uncertainty and the system’s nonlinear characteristic are both considered in the proposed BSMC controller. To comprehensively design and validate the proposed control method, a 4-DOF half car model and a corresponding nonlinear active anti-roll HIS model are firstly established. Next, a linear quadratic regulator (LQR) upper layer controller is designed to optimize the vehicle’s roll motion, and the result of the LQR controller is set as a tracking target. Thereafter, the proposed BSMC bottom layer controller is designed to track the target moment. Finally, numerical simulation and experiments on the test bench are conducted to validate the proposed control method. The simulation and bench test results indicate that the proposed active HIS performs well in tracking target and tolerating disturbance. The proposed active HIS expands about additional ±1000 N/m dynamic anti-roll moment compared to a passive HIS at each roll angle, and the response time is less than 70 ms. The field test results show that the active HIS with proposed control method improves anti-roll property compared with a passive HIS by 23.3% in the double lane change test.

Model predictive control allocation based on adaptive sliding mode control strategy for enhancing the lateral stability of four-wheel-drive electric vehicles

A novel hierarchical direct yaw moment controller is designed to enhance the lateral stability of the four-wheel-drive electric vehicle. The adaptive sliding mode control (ASMC) technique in the upper-layer controller is employed to compute an additional yaw moment. The lower-layer controller distributes this yaw moment into each independent wheel by utilizing model predictive control allocation (MPCA). The proposed MPCA aims to mitigate the performance deterioration induced by in-wheel motor dynamics and optimize the power consumption stemming from the additional yaw moment. Co-simulation and hardware-in-the-loop (HIL) test is conducted to verify the performance of the proposed controller. Validation results show that the proposed hierarchical ASMC-MPCA controller outperforms the sliding mode control MPCA (SMC-MPCA) and the integrated nonlinear model predictive control (NMPC) with the lowest root-mean-square errors [Formula: see text] of yaw rate, sideslip angle, lateral deviation, and lowest power consumption. Additionally, the chattering phenomenon in SMC-MPCA can be suppressed effectively by adaptively estimating the parameter uncertainties. The proposed ASMC-MPCA controller also consumes less computational resources than the NMPC and SMC-MPCA, which indicates that the ASMC-MPCA is more suitable for an automotive onboard controller. The comparison between hierarchical and integrated controller frameworks also shows that the hierarchical framework is more suitable for production vehicles under non-powerful vehicle control units.

An Enabling Trajectory Planning Scheme for Lane Change Collision Avoidance on Highways

This paper presents a hierarchical three-layer trajectory planning framework to realize real-time collision avoidance under complex driving conditions. This is mainly ascribed to the generation of a collision-free trajectory cluster based on the speed and the path re-planning. The upper-layer controller is to generate a reference quintic polynomial trajectory based on the Sequential Quadratic Programming by assuming mild speed and acceleration variations of the surrounding vehicles. The waypoints and time stamps can be obtained via the reference trajectory. When the assumption is invalid under complex driving conditions, the middle-layer controller would generate a Quadratic Programming-based trajectory cluster to assign different time stamps to each waypoint through time-based sampling methods. The lower-layer controller would be triggered to create a new feasible trajectory based on the path sampling if the collision avoidance requirements are not satisfied. The host vehicle will return to its original lane if no feasible time window is available to perform a lane change maneuver under the vehicle kinematics and lane change time/displacement constraints. The effectiveness of the proposed scheme is verified under various scenarios through comprehensive simulations.

Hierarchical eco‐driving control strategy for hybrid electric vehicle platoon at signalized intersections under partially connected and automated vehicle environment

AbstractIn recent years, eco‐driving for hybrid electric vehicles (HEVs) has been studied with the emerging connected and automated vehicle (CAV) technologies to improve the mobility, fuel economy and safety of HEVs. This paper develops a hierarchical eco‐driving control strategy for HEV platoons consisting of CAVs and human‐driven vehicles (HDVs) to improve fuel economy at signalized intersections. For each platoon, the speed trajectories of CAVs are optimized in the upper layer controller using model predictive control (MPC) to minimize the total fuel consumption of the whole platoon. For each HEV, the optimal power split between the engine and the battery is obtained in the lower layer controller using adaptive equivalent consumption minimization strategy (A‐ECMS). The time‐varying powertrain efficiencies of HEVs are explicitly considered in the speed trajectory optimization. At last, simulation studies are conducted using MATLAB and VISSIM to evaluate the performances of the strategy in mixed traffic scenarios and different CAV penetration rates. Simulation results indicate that compared with the single vehicle control strategy, the proposed strategy can improve the average fuel economy by up to 8.34% and considering the time‐varying powertrain efficiencies of HEVs in the optimization can further reduce the fuel consumption by up to 1.23%.

Linear quadratic regulator based on extended state observer–based active disturbance rejection control of autonomous vehicle path following control

This article studies the control problem of autonomous vehicle path following with coordination of active front steering and differential steering. A hierarchical control scheme including upper layer and lower layer is proposed. In the upper layer controller, a linear quadratic regulator based on extended state observer is proposed to generate the front-wheel steering angle and external yaw moment, where extended state observer is used to estimate and compensate for the system uncertainty and external disturbance which enhances the capability of the vehicle to suppress the disturbance. A brake force distribution scheme based on the theory of control allocation is proposed in the lower layer controller to optimize and coordinate each wheel brake force to achieve differential steering. Finally, the effectiveness of proposed control scheme is verified in a co-simulation platform based on CarSim/Simulink; it can be concluded that the linear quadratic regulator based on extended state observer scheme not only has a few parameters need to be tuned, but also has the capability of active disturbance rejection.

A Two-Layer Real-Time Optimization Control Strategy for Integrated Battery Thermal Management and HVAC System in Connected and Automated HEVs

With the increase in the capacity and power rating of batteries in today's HEVs and EVs, the battery thermal management (BTM) system bears growing importance in vehicle safety and efficiency. A practical BTM system is commonly coupled with the passenger cabin heating-ventilation-air-conditioning (HVAC) system, which makes a major energy consumer and a challenging control object. Thanks to the connected and automated vehicle (CAV) technology, predictions of the vehicle speed profile and power trajectory can be obtained, providing the possibility for predictive control of the BTM and HVAC coupled system in order to maintain battery safety, passenger comfort, and to reduce energy consumption. However, the tradeoff among higher energy saving potential and wider control range from a long and sparse horizon, and higher accuracy from a short and dense horizon is inevitable in the conventional predictive control. In this paper, a two-layer predictive control strategy for warm/hot weather is proposed to address the aforementioned tradeoff. The upper layer controller firstly plans the optimized battery temperature trajectory according to the look-ahead speed preview and battery power profiles provided by the CAV network and the integrated BTM and HVAC system efficiency surface from off-line data. Then the lower layer model predictive controller tracks the planned trajectory and cabin temperature reference while enforcing the energy consumption optimization. Simulation results demonstrate that the proposed strategy exhibits accurate battery and passenger cabin temperature reference tracking while achieving up to 10.47% HVAC energy saving comparing to a baseline control strategy with UDDS profile and typical driving conditions. Implementation of the proposed strategy on a real-time vehicle emulator based on rapid control prototyping (RCP) and hardware-in-the-loop (HIL) platforms demonstrates the real-world implementation capability of the proposed two-layer framework.

A Two-Layer Controller for Lateral Path Tracking Control of Autonomous Vehicles.

This paper presents a two-layer controller for accurate and robust lateral path tracking control of highly automated vehicles. The upper-layer controller, which produces the front wheel steering angle, is implemented with a Linear Time-Varying MPC (LTV-MPC) whose prediction and control horizon are both optimized offline with particle swarm optimization (PSO) under varying working conditions. A constraint on the slip angle is imposed to prevent lateral forces from saturation to guarantee vehicle stability. The lower layer is a radial basis function neural network proportion-integral-derivative (RBFNN-PID) controller that generates electric current control signals executable by the steering motor to rapidly track the target steering angle. The nonlinear characteristics of the steering system are modeled and are identified on-line with the RBFNN so that the PID controller’s control parameters can be adjusted adaptively. The results of CarSim-Matlab/Simulink joint simulations show that the proposed hierarchical controller achieves a good level of path tracking accuracy while maintaining vehicle stability throughout the path tracking process, and is robust to dynamic changes in vehicle velocities and road adhesion coefficients.

Glocal Traction Control for In-wheel-motor Electric Vehicles - A Passivity Approach -

Although traction control of in-wheel-motor electric vehicles (IWM-EVs) has been studied for years, we still face the essential theoretical issue: How to assure the stability of the overall system while attaining both global and local control performances? This issue has been neglected by almost the works in the literature. The main reason is due to the nonlinearity and complexity in vehicle dynamics, especially the vehicle driven by multi-actuators. A new traction method is proposed in this paper in the glocal (global/local) framework to overcome this problem. The command to each local IWM includes two signals: (i) the local signal through anti-slip control in the lower-layer, and (ii) the global signal distributed from the driver or the upper-layer controller that managing the average wheel velocity. Stability of the whole system is guaranteed based on the fundamental passivity theory. The effectiveness of the proposed approach is verified by using Carsim/Matlab co-simulator.

Robust Performance Improvement of Lateral Motion in Four-Wheel Independent-Drive Electric Vehicles

In this article a robust two-layer control scheme is proposed to improve the performance of Four-Wheel Independent-Drive (4-WID) electric vehicles. The main objective is to enhance the lateral motion performance of electric vehicle under various manoeuvres and in the existence of parameter uncertainties and exterior disturbances. The upper-layer control law is proposed according to a novel terminal sliding mode approach formulated using the longitudinal velocity and yaw rate dynamics of the electric vehicle. It aims at generating the desired driving forces of each tire along with the vehicle's yaw momentum while guaranteeing chattering-free motion and finite-time stability in the existence of parametric uncertainties and disturbances. The lower-layer control law is formulated in the light of a constrained optimization problem to generate the optimal forces and torques for the in-wheel electric motors and achieve the desired longitudinal force and yaw momentum. The efficacy of the suggested approach is confirmed using CARSIM; a high-fidelity simulation package. Its performance analysis considered three standard driving manoeuvres and included a comparative analysis of other designs. The obtained results show that the offered methodology ensures the finite-time stability of the vehicle and improves its manoeuvrability and tracking performance under various driving conditions.