Hyeongcheol Lee

Robust Adaptive Fuzzy Control by Backstepping for a Class of MIMO Nonlinear Systems

This paper presents a robust adaptive control method for a class of multi-input-multi-output (MIMO) nonlinear systems that are transformable to a parametric-strict-feedback form which has couplings among input channels and the appearance of parametric uncertainties in the input matrices. The proposed approach effectively combines the design techniques of robust adaptive control by backstepping and adaptive fuzzy-logic control in order to remove the matching-condition requirement and to provide boundedness of tracking errors, even under dominant model uncertainties and poor parameter adaptation. Unlike previous robust adaptive fuzzy controls of MIMO nonlinear systems, this research introduces the robustness terms explicitly in the controller structure to counteract the effects of model uncertainties and parameter-adaptation errors. Uniform boundedness of the MIMO nonlinear control system is proved, and simulation results further validate the effectiveness and performance of the proposed control method.

Mode Transition Control Using Disturbance Compensation for a Parallel Hybrid Electric Vehicle

This paper proposes a new approach for the mode transition control in a parallel hybrid electric vehicle (HEV) with an engine clutch. During mode transition, a noticeable jerk or torque fluctuation in the hybrid powertrain, which may result in the vibration of the drivetrain and an unpleasant driving sensation, occurs frequently because of discontinuity of the dynamics and the characteristics of the powertrain system. Therefore, ensuring smooth vehicle operation during the mode transitions is an important objective of the HEV control. This paper presents a four-phase control strategy during transition from the electric vehicle mode to the hybrid mode by using disturbance observers. The disturbance observer estimates and compensates for disturbances to improve control accuracy, tracking performance, and drivability. The vehicle model is developed to represent the characteristics of the transient response of the target system and is used to simulate and verify the proposed control algorithms. The simulation results show the validity of the proposed control algorithms.

Height and Leveling Control of Automotive Air Suspension System Using Sliding Mode Approach

Electronically controlled air suspension systems have been used in vehicles to improve ride comfort and handling safety by adjusting vehicle height. This paper proposes a new nonlinear controller to adjust the height of the vehicle sprung mass (height control) and to regulate the roll and pitch angles of the vehicle body (leveling control) by an air suspension system. A sliding mode control algorithm is designed to improve the tracking accuracy of the control and to overcome nonlinearities and uncertainties in the air suspension system. A mathematical model of the air suspension system is formulated in a nonlinear affine form to describe the dynamic behavior of the system and to derive the control algorithm. The sliding mode observer is also designed to estimate the pressures inside four air springs. The effectiveness and performance of the proposed control algorithm are verified by simulations and actual vehicle tests.

Fault-Tolerant Control Algorithm for a Four-Corner Closed-Loop Air Suspension System

This paper presents a new systematic control and fail-safe design methodology for a four-corner closed-loop air suspension (CLAS) system. The proposed control algorithm consists of “system control” determining the target heights of the four corners of the vehicle body from driving conditions and from the drivers commands and “actuator control” achieving the target heights by controlling actuators. A sliding-mode control with phase-compensated feedback signal is applied as the main part of the system control. With the use of the sliding-mode control, the proposed system control can improve control accuracy and robustness against delays and disturbances as well as reduce the bounce oscillation of the vehicle body. This paper proposes a stepwise height control as the actuator control to overcome the limited power of the production CLAS system. The stepwise height control adjusts the front corners and the rear corners alternately until the four corners reach their corresponding target heights. A fail-safe algorithm (FA) is also proposed to provide the fault detection (FD), diagnosis, and management of the CLAS. In particular, a model-based FD method for the pressure sensor and the height sensors, which are critical components in the CLAS control system, has been proposed. A mathematical model of a CLAS system is developed for algorithm development and simulation. The proposed control algorithm and FA are verified by simulations and actual vehicle tests.

A robust road bank angle estimation based on a proportional–integral H∞ filter

This paper presents a new robust road bank angle estimation method that does not require a differential global positioning system or any additional expensive sensors. A modified bicycle model, which is less sensitive to model uncertainties than is the conventional bicycle model, is proposed. The road bank angle estimation algorithm designed using this model can improve robustness against modelling errors and uncertainties. A proportional–integral H∞ filter based on the game theory approach, which is designed for the worst cases with respect to the sensor noises and disturbances, is used as the estimator in order to improve further the stability and robustness of the bank estimation. The effectiveness and performance of the proposed estimation algorithm are verified by simulations and tests, and the results are compared with those of previous road bank angle estimation methods.

Model-based fault detection and isolation for electric power steering system

Electric power steering (EPS) system is an advanced steering system that assists steering torque using the electric motor. To guarantee reliability and safety of EPS System, fault management is especially important. This paper provides a fault detection and isolation (FDI) technique for the sensors of EPS system through the model-based FDI method. In the presented work, FDI algorithm is composed of a two-step process by using the mathematical models of the electric motor and the steering system. In the first step, faults of the phase current sensor and the angular velocity sensor of the electric motor can be detected by using parity equation which is derived from the mathematical model of the electric motor. In the subsequent step, residual is produced by cross-checking with three torque signals in mathematical model of the steering system, estimated assist torque from the measured phase motor current, estimated road torque from the tire model, and drivers wheel steering torque. And then the steering sensor fault can be detected by comparing the residual with the threshold which includes model uncertainties. Through the both steps, the EPS system sensor fault can be isolated by identifying the faulty component.

Virtual test track

The virtual test track (VTT) is a real-time vehicle simulator used for powertrain and chassis system development in a virtual environment. The VTT is designed and built based on the rapid control prototyping (RCP) concept. Therefore, different from the conventional vehicle simulator, the VTT can provide many additional benefits, such as ease of use, flexibility of interface with other devices, and ability to easily implement any hardware-in-the-loop system. The VTT consists of a powerful simulation engine to solve the equations of a complicated vehicle dynamics model in real-time and a sophisticated animation engine to provide real-time visual representation of vehicle behavior. It also contains multiple virtual test environments with variable surfaces and weather conditions to provide different types of driving conditions.

Design of an Airbag Deployment Algorithm Based on Precrash Information

Airbag systems have become an essential safety device for guaranteeing the physical well-being of drivers and their passengers. Unlike other safety devices, airbags are used as the last resort in a collision, and because they are directly linked to the life of the driver and passengers, the proper functioning of the system is an issue of paramount importance. Hence, to ensure the precision and reliability of airbag operation, it is necessary to design a robust crash algorithm. Currently, several companies are working to achieve an optimal deployment time for airbags in collisions by diversifying the type and location of crash-related sensors. Nevertheless, several problems must still be confronted. For instance, when a vehicle operates off road or when the sensor inside the airbag control unit (ACU) receives a powerful shock, the vehicles airbags may inadvertently deploy, although no collision has occurred, because a crashlike signal is delivered to the ACU. Alternately, in a collision situation that requires airbag deployment, the crash algorithm may make an erroneous judgment with regard to the collision configuration and miss the time frame for airbag deployment or fail to deploy the airbags altogether. Such problems can be attributed to the following two major causes: 1) Only signals produced through crash tests are used in the design of crash algorithms, and 2) the algorithms themselves only utilize postcrash input from the relevant sensors. To resolve these issues, this paper proposes a precrash algorithm that generates information about the crash scenarios before a collision has occurred. The purpose of the precrash algorithm is to make judgments about the impending collision configuration prior to impact by estimating the behavior of frontal objects and to communicate this information to the crash algorithm to enable correct recognition of the crash scenarios. This paper also proposes a crash algorithm based on crash-related sensors for the verification of and interfacing with the proposed precrash algorithm. The limitations of existing crash algorithms, which deploy airbags using only postcrash signals, were resolved through the development of an integrative crash algorithm that combines the precrash and crash algorithms to reflect precrash information. The developed algorithm was verified by running a wide range of simulations using CarSim, based on data from real crash tests. The results showed that, compared with independently using the crash algorithm, adding precrash information estimation significantly improved the reliability of airbag deployment.

Model-based fault-tolerant control for an automotive air suspension control system

This paper presents a new fault-tolerant control (FTC) algorithm for an automotive air suspension control (ASC) system. The FTC algorithm proposed in this paper provides the fault detection, diagnosis, and management of a closed-loop air suspension control system. A new model-based fault detection and isolation algorithm for the height sensors, which are critical but vulnerable components of the ASC system, is also proposed. The height sensor fault is detected on the basis of the geometric relationships of the suspension and is isolated by implementing the analytical redundancy of the height sensors using roll and pitch angle estimates derived by a Kalman filter. An adaptive threshold is designed and applied in order to enhance the robustness of the fault detection and isolation against model uncertainties. The effectiveness of the proposed model-based FTC algorithm is verified via simulation and actual vehicle tests.

A Supervisory Control Algorithm for a Series Hybrid Vehicle With Multiple Energy Sources

This paper presents a new power distribution strategy (PDS) for a series electric hybrid vehicle (SHEV) in the sense of minimizing fuel usage. The target vehicle has three energy sources, i.e., an engine and generator set (EGS), a battery, and an ultracapacitor, and two traction motors for the front and rear wheels. A two-step PDS based on the real-time optimization technique is proposed to distribute the demand power among three energy sources. Several existing PDSs, such as the thermostat strategy and the power-follower strategy, are modified and applied to be compared with the proposed strategy. An optimal torque distribution strategy between front and rear traction motors is also proposed to minimize the electric energy consumption and to further improve fuel economy. Simulation results demonstrate the feasibility and effectiveness of the proposed strategies.