Upper-level Control Research Articles

Methods and Principles of Construction of Intelligent Unmanned Systems for Train Control of Urban Off-Street Transport

The principles of constructing intelligent unmanned traffic control systems for off-street urban rail transport are considered, while a block diagram and connections between subsystems are proposed. The features of the construction of upper-level control algorithms are shown. Functional features of subsystems are defined, and links between subsystems are considered.

Adaptive Response Method for Communication Failures of Hierarchical Reactive Power Control in Wind Farms

Advanced controllers have been widely introduced in power system-related research to alleviate the uncertainties of renewable sources. The performance of the controllers should be verified in a realistic environment before the controllers are used in practical applications. This study focuses on voltage regulation of wind farms in voltage drop cases where communication failures occur and discusses modifications done on the online wind farm management system. The modified management system is designed to respond to a voltage drop when the outer reference signal from the upper-level controller is absent due to technical problems, especially communication errors. Such a situation has not been considered in most of the management systems in the literature. The predictable situations in real fields have been further verified under the hardware-in-the-loop simulation environment RTDS <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">TM</sup> . During communication failure, the proposed method can respond automatically to voltage recovery, along with a designed objective function. A comparative analysis of actual control modes was also conducted to prove the relevance and effectiveness of the proposed method.

A novel integrated control framework of AFS, ASS, and DYC based on ideal roll angle to improve vehicle stability

The current research on vehicle stability control mainly focuses on following the ideal yaw rate and sideslip angle, without considering the potential of ideal roll angle in improving the vehicle stability. In addition, the mutation of tire-road friction coefficient promotes a great challenge to the stability control. To improve the vehicle stability, in this study, firstly, the three-dimensional stability region of “lateral speed-yaw rate-roll angle” was studied, and a method to determine the ideal roll angle was proposed. Secondly, a novel integrated control framework of AFS, ASS, and DYC based on ideal roll angle was proposed to actively control the front tire slip angles, suspension forces, and motor torques: In the upper-level controller, model predictive control and tire force distribution algorithm were used to obtain the optimal four-tire longitudinal forces, front tire lateral forces and additional roll moment under constraints; In the lower-level controller, the upper virtual target was realized by the optimal allocation algorithm of actuators and the tire slip controller. Finally, the proposed control framework was verified on the varied-µ road. The results indicated that compared with the two existing control strategies, the proposed framework can significantly improve the vehicle following performance and stability.

Coordinated control scheme for EV charging and volt/var devices scheduling to regulate voltages of active distribution networks

This paper proposes a three-stage model predictive control (MPC)-based centralized coordinated approach to schedule charging of electric vehicles (EV) and volt/var (VV) devices. The approach aims at maintaining bus voltage magnitudes and state-of-charge (SOC) of EV battery within desired limits with minimal usage of control resources and cost of electricity consumption. The first stage determines the optimal operating points of traditional discrete control devices on an hourly basis. The second stage dispatches the optimal set-points of power electronics interfaced fast devices [photovoltaic (PV) and EV inverters] every one minute. The third stage schedules charging/discharging of EV half-hourly with respect to the real-time electricity price. Furthermore, several rules are formulated to adjust the local volt/var curve of PV and EV inverters according to the voltage magnitudes. The combined central and local control approach ensures that EVs attain the desired SOC at the time of their departure from the charging station without violating the voltage limits. The proposed control approach is tested in a 33-bus distribution network and 38-bus distribution network with different operating conditions. Simulation results depict that the performance of the proposed control approach is better than uncoordinated charging in terms of reduced voltage deviations, energy loss, and control resources utilization. • A three-stage model predictive control (MPC)-based centralized coordinated approach as upper-level control. • Rules are formulated to adjust the local volt/var curve of PV and EV inverters. • Aims at maintaining bus voltage magnitudes and reach desired state-of-charge of EV battery. • Objectives are minimal usage of control resources and cost of electricity consumption. • Coordinated EV charging along with the operation of other VRD (smart inverters, OLTC).

Evaluation of different power tracking operating strategies considering turbine loading and power dynamics

Abstract. This work focuses on the design, implementation, and implications of different operational strategies for wind turbines when providing a power tracking functionality. Power tracking is necessary for the contribution to stabilization of the electrical grid. Specifically, two different operational strategies are used as the foundation for a model-based control design that allows the turbine to follow a given power demand. The first relies on keeping a constant rotational speed while varying the generator torque to match the power demand. The second approach varies both the generator torque and the rotational speed of the turbine equally to yield the desired power output. In the power reduction mode, both operational strategies employ the pitch to maintain the desired rotational speed of the turbine. The attainable power dynamics of the two closed-loop systems with varying power demands are analyzed and compared. Reduced-order models formulated as transfer functions and suitable for integration into an upper-level control design are proposed. It is found that the first strategy involving only the generator torque while keeping a constant rotational speed provides significantly faster power control authority. Further, the resulting fatigue loading in turbulent wind conditions is discussed for the two operational strategies, where constant operational storage is emulated to enable a bidirectional variation in the power output. Without any additional load-reducing control loops, the results suggest that the first operational strategy involving variation in the generator torque only is more favorable with regard to the resulting loading of the turbine structure. The simulation studies are conducted for NREL's 5 MW reference turbine using FAST.

LQR-MPC-Based Trajectory-Tracking Controller of Autonomous Vehicle Subject to Coupling Effects and Driving State Uncertainties.

This paper presents a lateral and longitudinal coupling controller for a trajectory-tracking control system. The proposed controller can simultaneously minimize lateral tracking deviation while tracking the desired trajectory and vehicle speed. Firstly, we propose a hierarchical control structure composed of upper and lower-level controllers. In the upper-level controller, the linear quadratic regulator (LQR) controller is designed to compute the desired front wheel steering angle for minimizing the lateral tracking deviation, and the model-predictive controller is developed to compute the desired acceleration for maintaining the planed vehicle speed. The lower-level controller enables the achievement of the desired steering angle and acceleration via the corresponding component devices. Furthermore, an observer based on the Extended Kalman Filter (EKF) is proposed to update the vehicle driving states, which are sensitive to the trajectory-tracking control and difficult to measure directly using the existing vehicle sensors. Finally, the Co-simulation (CarSim-MATLAB/Simulink) results demonstrate that the proposed coupling controller is able to robustly realize the trajectory tracking control and can effectively reduce the lateral tracking error.

Advanced post‐impact safety and stability control for electric vehicles

In terms of the possible secondary or multiple events accidents (MEA) after an initial vehicle to vehicle (V2V) impact, this research proposes an active safety & stability controller for independent-driven electric vehicles towards recovering the vehicle to safety states after an impact. The controller aims to regulate the course angle and lateral deviation that enforce the vehicle back to its original driving path. The upper-level controller is derived based on sliding mode technique. And the low-level controller aims to solve a convex quadratic allocation problem, which inherently incorporates fault-tolerance property. The independent in-wheel-motor (IWM) fault is very likely to happen under a real V2V impact and it contributes much to the driving safety & stability. Two typical real-end-collision scenarios under low- and high longitudinal velocities are designed to verify the proposed controller performance. The low- and high velocities collisions aim to simulate the urban and highway accidents, respectively. Compared to other control methods, i.e. pure braking and static allocation, the RMSEs of safety (lateral deviation, course angle) and stability indexes (yaw rate, sideslip angle) under proposed controller reduce significantly both in urban and highway accidents. Moreover, the controller performs robust enough against the impact-induced IWM fault. It further supports the effectiveness at dealing with the safety and stability of the ego vehicle after an initial impact and prevent possible MEA.

Hierarchical MPC for coupled subsystems using adjustable tubes

A hierarchical Model Predictive Control (MPC) formulation is presented for coupled discrete-time linear systems with state and input constraints. Compared to a centralized approach, a two-level hierarchical controller, with one controller in the upper-level and one controller per subsystem in the lower-level, can significantly reduce the computational cost associated with MPC. Hierarchical coordination is achieved using adjustable tubes, which are optimized by the upper-level controller and bound permissible lower-level controller deviations from the system trajectories determined by the upper-level controller. The size of these adjustable tubes determines the degree of uncertainty between subsystems and directly affects the required constraint tightening under a tube-based robust MPC framework. Sets are represented as zonotopes to enable the ability to optimize the size of these adjustable tubes and perform the necessary constraint tightening online as part of the MPC optimization problems. State and input constraint satisfaction is proven for the two-level hierarchical controller with an arbitrary number of controllers at the lower-level and a numerical example demonstrates the key features and performance of the approach.

Coordinated yaw stability control for extreme path tracking of 4WIDEVs based on predictive control

To improve safety and stability of four-wheel independent drive electric vehicles (4WIDEVs) in extreme path tracking maneuvers, a hierarchical yaw stability control scheme based on predictive control is proposed in this work. The upper-level stability control strategy coordinates active front steering (AFS) and direct yaw moment control (DYC), yielding desired front steering angle and longitudinal force of each tire. The control task is formulated into a finite horizon optimal control problem, which is solved by hybrid model predictive control (hMPC) algorithm. The lower-level slip ratio control strategy calculates and tracks the optimal longitudinal slip ratio of each tire for implementation of the longitudinal tire force distributions in a wide range of road friction coefficient. Based on a qualified piecewise affine (PWA) approximation of lateral tire force models, the stable regions of vehicle states under multiple varying inputs are obtained by analyzing trajectories of fixed points. Furthermore, the control modes of the upper-level control strategy with different control inputs and state constraints are determined by judging bifurcation point of system. Finally, the proposed control approach is verified and compared through the dSPACE-based hardware-in-the-loop (HIL) experiments. The results indicate that the proposed yaw stability control strategy can improve yaw stability performance and reduce the tracking error significantly in extreme situations.

MULTILEVEL CONTROL OF A TRANSPORT ROBOT

The control systems of modern robots are multilevel. The upper level control of the transport robot by voice commands and simulation of its motion in the software product Microsoft Robotics Developer Studio are considered. Visual Programming Language for creating and debugging applications was used to simulate the motion of the transport robot. An Android tablet or mobile phone with an application is used to recognize voice commands and the robot can be controlled using the Bluetooth interface. A program has been developed that will allow a human to control the robot using speech. An example of simulation of the robot and its motion path using voice control is given. The developed control system allows the transport robot to follow from one target point to another using voice control. At a low level, optimal asynchronous motor based actuator control using a model oriented approach in SimInTech is considered.

Integrated tire slip energy dissipation and lateral stability control of distributed drive electric vehicle with mechanical elastic wheel

Stability and energy consumption have always been important issues in electric vehicle research. Excessive slip energy not only aggravates tire wear, but also consumes energy of electric vehicle. In order to ensure the lateral stability and to reduce the slip energy dissipation of the distributed drive electric vehicle (DDEV) equipped with Mechanical Elastic Wheel (MEW), an integrated framework considering both tire slip energy dissipation and lateral stability control is proposed. The SESC (Slip Energy and Stability Control) is a hierarchical control framework for DDEV with MEW. A PID speed tracking controller and an (Integral Terminal Slide Mode) ITSM controller are designed at the upper-level controller. The ITSM controller can improve the lateral stability of the vehicle by obtaining the desired yaw moment. Speed tracking controller can stabilize the speed of the vehicle and obtain the desired longitudinal force. At the lower-level controller, the brush model of the MEW is proposed to express tire slip energy. In order to reduce the error of the vehicle dynamics and the slip energy dissipation, a mixed objective function including a holistic corner controller (HCC) and a minimum tire slip energy characterization is proposed. The proposed control framework is verified by Carsim and Matlab/Simulink under emergency simulation conditions. The simulation results show that the SESC based method can improve the lateral stability of DDEV with MEW effectively, and has better performance compared with fuzzyPID+AD based method. Meanwhile, the SESC achieves less slip energy than conventional torque distribution method.

Research on Combined Frequency Regulation Control Method of Wind Storage with Storage System Optimized Intervals Considered

To solve the insufficient frequency regulation capacity and inertia of the power system caused by the increase of grid-connected wind capacity, a combined wind-storage frequency regulation control strategy considering the optimized intervals of the energy storage system is proposed. This article selects the joint frequency regulation of wind turbine overspeed control and energy storage virtual synchronous control, and virtual synchronous control is used as the upper-level control of the entire wind-storage system. In the process of frequency modulation, the output power of wind turbine and energy storage is reasonably distributed through the upper control. On this basis, to ensure the long-term and stable operation of energy storage, the state of charge (SOC) partition principle of energy storage is set up, which is divided into different areas according to the size of the SOC, and the output of the energy storage device is adjusted according to the divided regions. The results of simulation analysis and verification show that the proposed strategy provides additional frequency regulation capacity and inertia during the frequency variation and greatly improves the frequency stability. Besides, the SOC zoning principle ensures the stable operation of the energy storage.

Coordinated Control of Path Following and Yaw Stability for Distributed Drive Autonomous Vehicle

Abstract This paper researches a coordinated control approach of path following and yaw motion stability for distributed drive autonomous electric vehicle. In the upper-level controller, a linear time-varying model predictive control algorithm is used to obtain the front wheel steering angle to guarantee the vehicle to drive along the planned path. Meanwhile, the sliding mode control theory is aimed to calculate the external yaw moment to ensure the vehicle yaw stability. In the low-level controller, this research proposes the particle swarm optimization algorithm to execute the torque distribution control. The Matlab/Simulink simulation results indicate that the coordinated control strategy can not only effectively improve the accuracy of path following, but also guarantee yaw motion stability of the autonomous vehicles.

A simple time-predicting-based pressure control method for soft pneumatic actuators using on/off solenoid valves

The study of soft pneumatic actuator control techniques is an important research topic in soft robotics. However, much of the research has concentrated on upper-level control algorithms, with little attention paid to the bottom-level pneumatic controller. This work aims to develop a simple, low cost, and quick-response pressure controller for soft pneumatic actuators. The controller is designed based on an on/off valve-driven pneumatic supply system, which is high speed, economical, and easy to set up. Using the actual pneumatic properties of a controlled object to estimate the switching time of the valves, the controller avoids complex nonlinear pneumatic computation and can achieve a high control frequency. Experiments demonstrate that the controller performs well in terms of speed and precision, having practical value in the control of soft actuators.

MPC-Based Integrated Control of Trajectory Tracking and Handling Stability for Intelligent Driving Vehicle Driven by Four Hub Motor

Aiming at the problem of trajectory tracking and handling stability of intelligent four hub-motor independent-drive electric vehicle at high speed, an integrated control strategy based on model predictive control (MPC) is proposed. Firstly, a kinematics preview model is established considering the adaptive change of preview distance with longitudinal vehicle speed. Secondly, combined with the proposed preview model and considering the vehicle dynamic characteristics, an upper level controller of MPC is created to track the lateral deviation, heading angle deviation, sideslip angle and yaw rate to obtain the optimal solution of front wheel angle and additional yaw moment, which realize the control of handling stability while ensuring the trajectory tracking accuracy at same time. Then considering the varying road adhesion and the influence of longitudinal slip at the four tires, a lower level controller composed of torque optimal control and sliding mode controller is designed to realize the optimal torque distribution of four hub motors. Finally, through the co-simulation of MATLAB / Simulink and CarSim and the hardware-in-the-loop experiment, it is verified that the proposed integrated control strategy can effectively improve the path tracking accuracy and ensure the vehicle handling stability at high speed. Compared with the general MPC strategy, who does not control the sideslip angle and yaw rate with no preview model, the cumulative lateral tracking error of the integrated control is reduced by 51.9% and 87.7% respectively.

Resilience Enhancement at Edge Cloud Systems

It is becoming common practice to push interactive and location-based services from remote datacenters to resource-constrained edge domains. This trend creates new management challenges at the network edge, not least to ensure resilience. These challenges now need to be investigated and overcome. In this paper, we explore the use of open-source programmable asset orchestration at edge cloud systems to guarantee operational resilience and a satisfactory performance level despite system incidents such as faults, congestion, or cyber-attacks. We discuss the design and deployment of a new cross-level configurable solution, Resilient Edge Cloud Systems (RECS). Results from appropriate tests made on RECS highlight the positive effects of deploying novel service and resource management algorithms at both data and control planes of the programmable edge system to mitigate against disruptive events such as control channel issues, service overload, or link congestion. RECS offers the following benefits: i) the switch automatically selects the standalone operation mode after its disconnection from the upper-level controllers; ii) deployment of edge virtualized services is made, according to client requests; iii) the client requests are served by edge services and the related traffic is balanced among the alternative on-demand routing paths to the edge location where each service is available for its clients; iv) the TCP traffic quality is protected from unfair competitiveness of UDP flows; and v) a set of redundant controllers is orchestrated by a top-level multi-thread cluster manager, using a novel management protocol with low overhead.

Artificial Monitoring of Eccentric Synchronous Reluctance Motors Using Neural Networks

This paper proposes an artificial neural network for monitoring and detecting the eccentric error of synchronous reluctance motors. Firstly, a 15 kW synchronous reluctance motor is introduced and took as a case study to investigate the effects of eccentric rotor. Then, the equivalent magnetic circuits of the studied motor are analyzed and developed, in cases of dynamic eccentric rotor and static eccentric rotor condition, respectively. After that, the analytical equations of the studied motor are derived, in terms of its air-gap flux density, electromagnetic torque, and electromagnetic force, followed by the electromagnetic finite element analyses. Then, the modal analyses of the stator and the whole motor are performed, respectively, to explore the natural frequency and the modal shape of the motor, by which the further vibrational analysis is possible to be conducted. The vibration level of the housing is furtherly studied to investigate its relationship with the rotor eccentricity, which is validated by the prototype test. Furthermore, an artificial neural network, which has 3 layers, is proposed. By taking the air-gap flux density, the electromagnetic force, and the vibrational level as inputs, and taking the eccentric distance as output, the proposed neural network is trained till the error smaller than 5%. Therefore, this neural network is obtaining the input parameters of the tested motor, based on which it is automatically monitoring and reporting the eccentric error to the upper-level control center.

Informational Task “Calculation of Technical and Economic Indicators” NPP I&C ULCS and Risk Potential Assessment

Calculation of technical and economic indicators (TEI) is part of the information tasks of the upper-level control system of the NPP instrumentation and control system (I&C system). The TEI characterize the efficiency, reliability, and durability of the power unit equipment during its operation. Based on the automation of the TEI determination, objective information can be obtained about the efficiency of technological processes, the actual state of power equipment, as well as the risk potential of the ongoing processes.

A Three-Dimensional Integrated Non-Linear Coordinate Control Framework for Combined Yaw- and Roll-Stability Control during Tyre Blow-Out.

A tyre blow-out can greatly affect vehicle stability and cause serious accidents. In the literature, however, studies on comprehensive three-dimensional vehicle dynamics modelling and stability control strategies in the event of a sudden tyre blow-out are seriously lacking. In this study, a comprehensive 14 degrees-of-freedom (DOF) vehicle dynamics model is first proposed to describe the vehicle yaw-plane and roll-plane dynamics performance after a tyre blow-out. Then, based on the proposed 14 DOF dynamics model, an integrated control framework for a combined yaw plane and roll-plane stability control is presented. This integrated control framework consists of a vehicle state predictor, an upper-level control mode supervisor and a lower-level 14 DOF model predictive controller (MPC). The state predictor is designed to predict the vehicle’s future states, and the upper-level control mode supervisor can use these future states to determine a suitable control mode. After that, based on the selected control mode, the lower-level MPC can control the individual driving actuator to achieve the combined yaw plane and roll plane control. Finally, a series of simulation tests are conducted to verify the effectiveness of the proposed control strategy.

A Novel Adaptive Cruise Control Strategy for Electric Vehicles Based on a Hierarchical Framework

Conclusive evidence has demonstrated the critical importance of adaptive cruise control (ACC) in relieving traffic congestion. To improve the performance of the ACC system, this paper proposes a novel ACC strategy for electric vehicles based on a hierarchical framework. Three main efforts have been made to distinguish our work from the existing research. Firstly, a sliding acceleration identification model is established based on the recursive least squares algorithm with multiple forgetting factors (MFF-RLS). Secondly, with vehicle following, economy, and comfort as the optimization objectives, the upper-level controller is developed based on the model predictive control (MPC) algorithm. Benefit from the identification of the sliding acceleration, the MPC controller holds better capability in accommodating environmental changes. Thirdly, an iterative learning lower-level controller is designed to control the driving and braking systems. Considering the efficiency of regenerative braking, the braking force distribution strategy is also designed in the lower-level controller. Simulation results show that, compared with the conventional MPC-based ACC strategy, the proposed strategy has similar performance in vehicle following, but it makes great improvements in comfort and economy. The specific features are that the vehicle acceleration and speed fluctuation are significantly reduced, and the energy consumption is also reduced by 2.05%.