Co-simulation Platform Research Articles

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.

Implementation of Novel Reduced-Order H∞ Filter for Simultaneous Detection and Mitigation of FDI-Attacks in AGC Systems

Recent developments in power systems to build a smart grid depend critically on communication networks which provide unprecedented advantages for comprehensive system visibility, monitoring, protection, and control. However, it increases the system complexity and vulnerability to cyberattacks that could jeopardize the seamless system operation. The attack must be detected effectively, and then proper measures to mitigate the effect quickly to restore normality. This work implements a reduced-order <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$H_{\infty }$ </tex-math></inline-formula> filter to simultaneously estimate the state variable and unknown inputs such as noise or attack signals in the automatic generation control (AGC) system. Further, the knowledge of the disturbances is utilized to mitigate the attack by appropriately compensating the sensor readings, such that the control decisions are based on the correct states, not the corrupted signals generated in the attack scenario. The stability criterion is provided based on Lyapunov functions to check the estimation error in the presence of the exogenous disturbance. Further, the proposed <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$H_{\infty }$ </tex-math></inline-formula> estimator performance is evaluated by comparing it with the Kalman filter to verify its effectiveness. The estimator’s performance is validated by developing a cosimulation platform using the server–client architecture of data exchange. Further, the performance is validated in a real-time digital simulator (RTDS) platform used as the power system interacting with an estimator deployed on Raspberry Pi board sensing the attacks.

Scalable Resilience Analysis Through Power Systems Co-Simulation

The ability to robustly characterize transmission and distribution grid resilience requires the ability to perform time-scale analysis that interweaves communications, control, and power contributions. This consideration is important to ensuring an understanding of how each individual aspect can affect the resulting systemic resilience. The combination of co-simulation of these time-based characteristics and a resilience-specific metric provides a likely method to inform both design planning and implementation/operational goals to ensure resilience in power systems. The Microgrids, Infrastructure Resilience, and Advanced Controls Launchpad co-simulation platform (MIRACL-CSP) has been developed to allow the modular integration of power grid models with control and metrics applications. This paper introduces MIRACL-CSP as a fundamental platform to study and improve the resilient operation of a microgrid. It offers a holistic investigation environment for systemically comparing the cyber-physical resilience to natural and manmade events. We emphasize the importance/advantage of intertwining the distribution system simulator GridLAB-D with the Power Distribution Designing for Resilience (PowDDeR) application to analyze the resilience of the St. Mary’s microgrid in Alaska. The resilience is evaluated in both short-term (frequency stability) and long-term (energy constrained) metrics. The results of the analysis of the St. Mary’s microgrid show that there is a trade-off between the two. As inertia-based generation assets are taken off-line, short-term resilience drops. However, the long-term resilience is retained longer as less fuel is being used.

Speed Control of Secondary Regulation Heave Compensation Based on Fuzzy PID

Aiming at the problem that the widely used heave compensation technology is difficult to balance the compensation effect and energy consumption, based on the secondary regulation technology, the fuzzy PID control method is selected to design a winch heave compensation system with energy recovery. Firstly, a mathematical model is established and analyze the energy-saving characteristics of the heave compensation system. Then, the swash plate controller and speed outer loop controller are designed, and the simulation is model with the AMESim-MATLAB co-simulation platform. Finally, the simulation results are tested by experiments. And the test result reflects that the compensation efficiency of the compensation is more than 90%, and part of the energy can be recovered. The controller has good control accuracy and stability. The compensation system can effectively compensate for the position deviation caused by waves, thus providing a safety guarantee for offshore operations.

Human gait tracking for rehabilitation exoskeleton: adaptive fractional order sliding mode control approach

To improve the rehabilitation training effect of hemiplegic patients, in this paper, a discrete adaptive fractional order fast terminal sliding mode control approach is proposed for the lower limb exoskeleton system to implement high-precision human gait tracking tasks. Firstly, a discrete dynamic model is established based on the Lagrange system discretization criterion for the lower limb exoskeleton robot. Then, in order to design a discrete adaptive fractional order fast terminal sliding mode controller, the Grünwald–Letnikov fractional order operator is introduced to combine with fast terminal attractor to construct a fractional order fast terminal sliding surface. An adaptive parameter adjustment strategy is proposed for the reaching law of sliding mode control, which drives the sliding mode to the stable region dynamically. Moreover, the stability of the control system is proved in the sense of Lyapunov, and the guidelines for selecting the control parameters are given. Finally, the simulations are tested on the MATLAB-Opensim co-simulation platform. Compared with the conventional discrete sliding mode control and discrete fast terminal sliding mode control, the results verify the superiority of the proposed method in improving lower limb rehabilitation training.

Robust path following control of 4WID autonomous vehicle with driving condition adaptive mechanism

This paper proposes a novel integrated path following control scheme for a 4-Wheel Independent Drive (4WID) autonomous vehicle that can adaptively change its mechanism according to the driving conditions. The proposed integrated system handles the lateral steering controller, longitudinal speed, and yaw moment controls considering tire force capacity of each corner. For the lateral controller, the cornering stiffness uncertainties and the transient performance are considered and combined into an H∞ robust controller based on linear matrix inequality (LMI) theory. A super-twisting sliding mode controller (STSMC) based longitudinal controller is designed to deal with disturbances and suppress chattering. When encountering extreme conditions, the active yaw moment controller with hierarchical structure is adaptively activated to prevent large deviation from the reference path and maintain the stability of vehicle. For the tire force allocation, an optimization algorithm is proposed, which has flexible equality constraints to coordinate the longitudinal and lateral motions according to the driving conditions. Simulations based on Carsim-Simulink co-simulation platform show that the proposed method is effective and has excellent performance in both normal and extreme driving conditions.

Trajectory Control Strategy and System Modeling of Load-Sensitive Hydraulic Excavator

Accurate control of excavator trajectory is the foundation for the intelligent and unmanned development of excavators. The excavator operation process requires multiple actuators to cooperate to complete the response action. However, the existing control methods to realize a single actuator of the excavator can no longer meet the practical demand. Based on this, a hybrid adaptive quantum particle swarm optimization algorithm (HAQPSO) is proposed to tune the proportional integral derivative (PID) controller parameters for enhancing the trajectory control accuracy of excavator actuators. To increase particle randomization and search speed and avoid the local convergence of QPSO, the QPSO is combined with circle chaotic mapping, Gaussian mutation operators, and adaptive adjustment factors, while the linear transformation of the contraction-expansion coefficient (CE) is improved to the dynamic adjustment mode. Through the interface block, a co-simulation platform for the load-sensitive system excavator is constructed, and trajectory experiments of multiple actuator compound actions are carried out. The simulation results show that—compared with ZN-PID, PSO-PID, and QPSO-PID—the trajectory error accuracy of the boom is improved by 26.59%, 32.95%, and 9.44%, respectively, which proves the high control accuracy of HAQPSO-PID in controlling the trajectory of multiple actuators.

Integrated Direct Yaw Control and Antislip Regulation Mixed Control of Distributed Drive Electric Vehicle Using Cosimulation Methodology

The improvement of handling and stability performance of Distributed Drive Electric Vehicle (DDEV) is analyzed, visualized, and designed by proposing and deploying the mixed control strategies in this paper including Direct Yaw Control (DYC), Antislip Regulation (ASR) and a novel Dual-mode Switching Control (DMSC). First, by drawing the phase trajectory stability domain, the vehicle stability limit boundary can be determined, which provides the basis for the lateral stability constraint of the vehicle. Then, the practicability and real time visualization of driving efficiency and timeliness of DDEV is achieved to reduce the margin of error for the desired torque value by employing the DYC strategy which uses a fuzzy PID algorithm. Furthermore, the ASR strategy which adopts the optimal slip rate algorithm to determine the requirement of desired torque value based on the different road conditions is used to reduce slip phenomenon effectively and to maintain handling control of DDEV. In response to different scenes especially conflict and coexistence between DYC and ASR, the DMSC strategy is applied to find a more suitable slip rate range by using the root mean square error method (REME). Finally, the cosimulation platform of ADAMS/Car and MATLAB/Simulink is built to simulate the mixed control strategies by integrating DYC, ASR, and DMSC. The simulation results show that the DMSC can effectively prevent DDEV from entering the dangerous limit driving state when turning and driving. The strategy has a more significant control effect needed to meet the requirements of the driving safety of the vehicle and handling stability. The DMSC is adopted and downloaded into the electronic control unit of our student type formula vehicle called Flash V6 which was designed and developed by a team of students, the ZUST ATTACKER Team.

Dynamic coordinated control strategy of power on gear shifting for novel dual motor electric vehicle

In order to solve the gear shifting power interruption problem in electric vehicles with automatic mechanical transmission, this paper proposes a novel dual-motor powertrain system including two motors and one gearbox, which can realize nine combined gears. Based on the working state of the power source, the clutch and the synchronizer in each working mode, as well as the operating characteristics of the vehicle, the dynamics of each gear shifting stage is analyzed, and the dynamic equations are established. The shifting process is simulated based on the co-simulation platform of MATLAB and AMEsim. Finally, a hardware in the loop test system including solenoid valve is designed and developed, and the gear shifting process from 01 to 11 is selected as a representative scheme for the experimental validation. The experimental results show that compared with the control strategy that the motor target speed is greater than the speed of the driven plate of the clutch, the control strategy that the motor target speed is lower than the speed of the driven plate of the clutch proposed in this paper reduces the shift time and sliding friction work by 37.4% and 87.3% respectively.

A multi-objective optimization method for hydraulically interconnected suspension system

To enhance the ride comfort and roll stability of the special vehicle, this study concentrates on the inherent parameters optimization of the hydraulically interconnected suspension (HIS) system. A road-tire-HIS-body model of a special vehicle is established based on the AMESim/Simulink co-simulation platform. Then the sensitivity of the HIS system is analyzed to determine the design variables to be optimized through the AMESim/Simulink/Isight co-simulation. Two objectives including improving the ride comfort and roll stability of the vehicle are proposed. In order to enhance the population diversity and accelerate the convergence velocity, the improved non-dominated sorting genetic algorithm II (NSGA-II) based on orthogonal experimental design method is used for multi-objective optimization problem of the HIS system. The numerical simulations indicate that the improved NSGA-II (INSGA-II) can generate better distributed Pareto solutions than traditional NSGA-II. Furthermore, simulation results indicate that both of the ride comfort and roll stability of the vehicle are improved based on the HIS system optimized by INSGA-II than NSGA-II. Compared with the NSGA-II method, the vehicle ride comfort and roll stability of HIS system optimized by INSGA-II are increased by 17.62% and 8.56% in sinusoidal conditions, respectively.

Eco-Driving Control Architecture for Platoons of Uncertain Heterogeneous Nonlinear Connected Autonomous Electric Vehicles

The improvement of energy performance for platoons of autonomous connected vehicles is one of the major challenges the road transport sector is facing with. To this aim, this work addresses and solves the energy-consumption problem for uncertain heterogeneous electric nonlinear autonomous vehicles platoon via a novel Eco-Driving Control Architecture able to optimize its energy consumption performance while ensuring the fulfillment of the optimal leader tracking trajectory. Specifically, it consists of a Nonlinear Model Predictive Control (NMPC) strategy, driving the leader motion and computing the optimal ecological trajectory to be imposed on the whole platoon, and a novel distributed exponentially-stable robust PID-like protocol, driving the follower vehicles motion for achieving a precise leader-tracking with a desired transient behavior as required for the accurate implementation of the energy-saving control. The exponential stability of the overall vehicular network is analytically proven with the Lyapunov theory and the derived robust stability conditions allow the proper tuning of the control gains on the basis of the desired decay rate. The efficiency of the proposed approach is corroborated via the high-fidelity Mixed Traffic Simulator (MiTraS) co-simulation platform under different operative scenarios and a wide uncertainty range for the vehicles parameters. Simulation results confirm how the proposed architecture ensures the eco-driving behaviour for the whole vehicles platoon.

Resilience Enhancement of Pilot Protection in Power Systems

Concerns about the cybersecurity and resilience of power systems have heightened in electric utilities and regulatory agencies over the past decade mainly because of the unpredictable target, location and scale of cyberattacks and the potential severity of consequences. The cybersecurity of pilot protection is paramount in bulk power systems considering their prevalence and the crucial role they play in protecting critical assets and preventing large system disturbances and major blackouts. This paper investigates the resilience of pilot protection using a co-simulation platform based on OPAL-RT simulator and Riverbed Modeler. It is demonstrated that software-defined networking for operational technology (OT SDN) significantly improves the resilience of pilot protection to false data injection (FDI) attacks compared to traditional networks. Moreover, the resilience of OT SDN based pilot protection to denial of service (DoS) attack against the SDN controller is investigated both in the proactive and reactive modes of operation. The simulation results verified the resilience of OT SDN controller in OT SDN based pilot protection to DoS attacks in the proactive mode of operation.

Analysis of Suspension Control Strategy of Wheel-track Composite Amphibious Vehicle Based on AMESim-Simulink

Amphibious vehicles are an important unit in amphibious transportation. However, the problem of vertical load control of amphibious vehicles on soft roads such as tidal flats and swamps still needs to be solved urgently. Based on a new configuration of wheel-track composite amphibious vehicle (W-TCAV), the coupling relationship between suspension control and vertical load on soft roads is analyzed, and its 1/8 hydraulic system of hydro-pneumatic suspensions is established; Under the condition of known vertical load distribution range, based on the AMESim-Simulink co-simulation platform, the control and tracking effects of two different control strategies are compared and analyzed. The simulation results show that compared with the PID control strategy, the effect of fuzzy PID control on the suspension hydraulic system is more desired, the control response speed to the desired vertical load is faster, and the tracking accuracy is higher. It provides a better control scheme for the suspension system and vertical load control of the W-TCAV.

Design of PAM-8 VLC Transceiver System Employing Neural Network-Based FFE and Post-Equalization

Wireless communication technology adopting electromagnetic waves in an unlicensed spectrum, such as visible light, for communication has attracted wide research efforts. Visible light communication (VLC) utilizes visible light as a communication medium to transmit signals but faces a limited communication bandwidth and low data rate, which is caused by the intrinsic characteristics of LEDs. This paper first studies a mathematical model of limited bandwidth and its effect on transmitted signals and then analyzes the free space and underwater channel loss. With the theoretical analysis, a VLC transceiver system is presented for solving bandwidth limitation by utilizing a pulse-amplitude modulation-8 (PAM-8) scheme and a hybrid equalization method. The proposed hybrid equalization combined a passive equalizer, a neural network (NN)-based feed-forward equalization (FFE), and a radial basis function neural network (RBF-NN). The feasibility of this VLC system was verified through a co-simulation platform with both free-space and underwater channels. Compared with a VLC system adopting a deep neural network (DNN)-based post-equalization method, the proposed VLC system could achieve a data rate of 3.6 Gbps with a bit error rate (BER) of 3.8 × 10−3 over a 3 m free-space channel. The RBF-NN achieved a reduced training time of 10 min, which was 86.7% lower than the conventional DNN-based post-equalization method.

MPC-Based Obstacle Avoidance Path Tracking Control for Distributed Drive Electric Vehicles

A path tracking controller based on front wheel steering angle and additional yaw moment control is designed to achieve safe obstacle avoidance of distributed drive electric vehicles. Using sixth-degree polynomial at a given time with anti-collision and anti-rollover conditions, the path planning of obstacle avoidance is proposed. The front wheel steering angle and additional yaw moment are output by the Model Predictive Control (MPC) controller. The wheel torque is distributed by the torque distribution controller. Through additional yaw moment and the vertical force ratio of the wheel, the obstacle avoidance path tracking control is realized. The co-simulation platform is established with Carsim/Simulink. The obstacle avoidance path, model predictive controller and torque distribution controller designed in this paper are simulated. The results show that obstacle avoidance path and tracking controller for the distributed drive electric vehicles effectively meet the requirements of safe obstacle avoidance.

High-speed lateral stability and trajectory tracking performance for a tractor-semitrailer with active trailer steering.

An active trailer steering (ATS) controller is investigated to improve the lateral stability and trajectory tracking performance of the tractor-semitrailer. First of all, a linear yaw-roll dynamic model of the tractor-semitrailer with steerable trailer wheels is established, and the model accuracy is verified. Then a linear quadratic regulator (LQR) for actively steering the trailer's wheels is designed. For the LQR controller, the lateral acceleration and the sideslip angle at the center of gravity (CG) of the trailer are taken as the optimization objectives, and the steering angle of the wheel on the middle axle of the trailer is set as the control input. Finally, the effectiveness of the designed controller is tested based on the co-simulation platform under the single lane-change (SLC) maneuver at the speed of 100 km/h and the double lane-change (DLC) maneuver at the speed of 80 km/h and 88km/h. Research results show that under the high-speed SLC maneuver, the designed LQR controller can significantly improves the lateral stability and trajectory tracking performance of the trailer, and cannot affect apparently the trajectory and dynamic responses of the tractor. Under the high-speed DLC maneuver, the designed controller can still make the tractor-semitrailer reach a new steady state in a short time, and improve the vehicle lateral stability and the trajectory tracking performance of the trailer at the same time.

Multi-Rate Parallel Real-Time Simulation Method for Doubly Fed Wind Power Systems Based on FPGA–CPU

A multi-rate parallel real-time simulation method based on FPGA–CPU is studied to realize the asynchronous co-simulation of the converter of doubly fed wind power systems with the wind turbine and external power grid. The doubly fed wind power system is partitioned by simulation step length, and the partitioned small-step-length data are processed using integral homogenization. For large-step data, an improved delay-compensated linear interpolation method combined with Newton interpolation is proposed for processing. The general small time-step (GST) model method is used to implement the FPGA modeling of the small-step converter, and resource optimization is achieved through timing time-division multiplexing. Asynchronous parallel co-simulation of a doubly fed wind power system is implemented on an FPGA–CPU co-simulation platform. Among them, the FPGA realizes the development of the converter HDL with a small step of 1 μs, while the CPU completes the simulation of the wind turbine and power grid synchronously with a large step of 50 μs. Finally, by comparing with MATLAB/Simulink offline simulation and analyzing the error, it is concluded that the simulation accuracy of the improved method in this paper is higher than that of the un-interpolated parallel simulation, which verifies the real-time performance and accuracy of the modeling and improved method in this paper.

Real-Time Grid and DER Co-Simulation Platform for Testing Large-Scale DER Coordination Schemes

Distributed energy resources (DERs), such as responsive loads and energy storage systems, can help grid operators to more effectively balance supply and demand and manage network constraints. However, consumer acceptance of load coordination schemes depends on ensuring customer quality of service (QoS), thus requiring the careful management of device-level constraints. Simultaneously managing both device-level and system-level objectives is a difficult problem. Most potential solutions to the DER coordination problem are in some way distributed, with decision logic running both at the device (such as a thermostat) and the coordinator, with potentially complex communications between devices and one or more coordinators. It is difficult to effectively test distributed DER control schemes, particularly when there are thousands of devices with both device-level and grid-level constraints and asynchronous communications. This paper addresses this challenge by presenting a novel real-time grid-and-DER cyber-physical co-simulation platform for testing advanced DER coordination schemes and characterizing the behaviors of a DER fleet. The co-simulation platform is particularly suitable for: <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$i$ </tex-math></inline-formula> ) testing the real-time performance of a large fleet of DERs delivering advanced grid services; <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$ii$ </tex-math></inline-formula> ) characterizing the distribution of states for a large fleet of managed DERs; and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$iii$ </tex-math></inline-formula> ) incorporating practical limitations of DERs and communications and analyzing the effects on fleet-wide performance. To illustrate these benefits, we demonstrate how the platform can be used to test a fleet with thousands of DERs coordinated using Packetized Energy Management (PEM) and interacting with a realistic transmission system model.

3D Gradient Reconstruction-Based Path Planning Method for Autonomous Vehicle With Enhanced Roll Stability

Path planning has received more and more attention due to its indispensability in autonomous vehicle (AV). Generally speaking, the stability of AV is not fully considered in path planning, therefore, the planned path may be detrimental for maintaining the stability. And this problem is even more acute for roll stability in complex 3D environment, such as off-road terrain environment. To enhance the roll stability on off-road terrain, a 3D gradient reconstruction-based path planning method (3DGRB-PPM) is presented in this work. The 3DGRB-PPM can keep the roll angle at a low level even in complex 3D environment, thus greatly enhancing the roll stability. The 3DGRB-PPM includes two parts, a gradient reconstruction unit and an adaptive fusion unit. In order to ensure the roll stability of AV in path planning, the gradient reconstruction unit is designed by constructing two potential fields, a joint potential field of gradient and roll angle for enhancing the roll stability and a 3D artificial potential field for reaching the destination. In order to coordinate these two potential fields, an adaptive fusion unit is designed by fuzzy inference rule. The simulation is implemented on the Matlab-Carsim co-simulation platform, and the simulation results show that the path planned by 3DGRB-PPM has good performance with enhanced roll stability.

Lane-Changing Strategy Based on a Novel Sliding Mode Control Approach for Connected Automated Vehicles

Safe and efficient autonomous lane changing is a key step of connected automated vehicles (CAVs), which can greatly reduce the traffic accident rate and relieve the traffic pressure. Aiming at the requirements of the smoothness and efficiency of the lane-changing trajectory of CAVs, it is necessary to design the lane changing controller to integrate the sensing, decision-making, and control tasks in the driving process. Firstly, based on the vehicle dynamics model, this paper proposes a vehicle lane-changing control strategy based on NNTSMC method (neural network enhanced non-singular fast terminal sliding mode control). The designed lane-changing controller can well realize the designed path tracking, and both lateral position and yaw angle can well track the expected value. This method enables the vehicle to control the front wheel steering angle intelligently, and the lateral acceleration during steering changes in the small scope, which ensures the steering stability of the vehicle. In this study, an improved adaptive RBF neural network with bounded mapping is designed to estimate the upper bound of the total disturbance of the system, which effectively reduces the chattering phenomenon of the control force. The Lyapunov function constructed in this study proves that the designed controller can ensure the stability of the controlled system. Finally, a comparative experiment is performed by the MATLAB/Simulink-CarSim co-simulation. Compared with SMC and TSMC (non-singular fast terminal sliding mode control), the proposed method has a performance improvement of at least 58.0% and 34.1%, respectively. The effectiveness and superiority of the proposed control method were confirmed by the experiments on the co-simulation platform.