Changwon Kim

Target tracking control of a mobile robot using a Brain limbic system based control strategy

In this paper, a Brain limbic system (BLS) based control algorithm is used to address the problem of target tracking in mobile robotics. The mathematical description of this approach in the form of BELBIC (Brain Emotional Learning Based Intelligent Control; also referred to as BLS) is presented and used to generate appropriate velocity profile for the mobile robot to track its target. The overall performance of the system is enhanced via fuzzy clustering of the error and velocity pairs.

Adaptive Analytic Hierarchy Process-Based Decision Making to Enhance Vehicle Autonomy

In this paper, an autonomous vehicle function management methodology is studied. At the decision-making level, the proposed system chooses the optimal function [adaptive cruise control (ACC) or a lane change maneuver (LCM)] that is consistent with the user-selected driving mode, such as fast travel, cautious driving, and efficient travel mode, whereas the control level is implemented via a neuromorphic strategy based on the brain limbic system. To realize the decision-making strategy, the analytic hierarchy process (AHP) is used by considering driving safety, traffic flow, and fuel efficiency as objectives, while LCM and ACC are chosen as the alternative functions. The adaptive AHP is further suggested to cope with the dynamically changing traffic environment. The proposed adaptive AHP algorithm provides an optimal relative importance matrix that is essential to making decisions under varying traffic situations and driving modes. The simulation results show that the proposed structure produces an effective approach to autonomous vehicle function management.

Application of brain limbic system to adaptive cruise control

In this paper, we propose an application of neuromorphic based control strategy to achieve Adaptive Cruise Control (ACC) as an autonomous vehicle function. In this approach, a computational model of the brain limbic system is utilised to produce the required control action. The results demonstrate that the proposed controller has at least similar and generally better performance than the conventional approach. Finally, BLS based ACC approach is extended to platoons of vehicles. The results show the successful operation of the proposed approach in the platoon setting both in terms of individual vehicle performance as well as string stability of the platoon.

Development of an autonomous vehicle highway merging strategy

In this paper, we propose a highway merging method to enhance vehicle autonomy in the highway travelling. The proposed decision making algorithm includes a Modified Intelligent Driver Model (MIDM) based vehicle distance adjustment and path prediction for collision avoidance. In order to maximise the safety and driving efficiency, a time optimal target is selected when the front and rear gap conditions that secure the merging safety are not satisfied. The suggested algorithm is implemented by a lane change manoeuvre and Adaptive Cruise Control (ACC) that are based on a control strategy inspired by the brain limbic system. In order to demonstrate the performance of the suggested merging strategy, the concept of Level of Service (LOS) is utilised in the simulations.

Brain limbic system-based intelligent controller application to lane change manoeuvre

This paper presents the application of a novel neuromorphic control strategy for lane change manoeuvres in the highway environment. The lateral dynamics of a vehicle with and without wind disturbance are derived and utilised to implement a control strategy based on the brain limbic system. To show the robustness of the proposed controller, several disturbance conditions including wind, uncertainty in the cornering stiffness, and changes in the vehicle mass are investigated. To demonstrate the performance of the suggested strategy, simulation results of the proposed method are compared with the human driver model-based control scheme, which has been discussed in the literature. The simulation results demonstrate the superiority of the proposed controller in energy efficiency, driving comfort, and robustness.

Game theory based autonomous vehicles operation

In this paper, we propose a game theory based approach to decision making with application to the operation of autonomous ground vehicles in highway setting. The mixed-motive game theory is utilised as a decisionmaking strategy in the context of a two-player game involving autonomous vehicles. The payoff matrices are defined by considering the safety of each player’s decision combination in view of their desire to stay within a given lane or to change lanes in consideration of the traffic conditions that the vehicles encounter. By analysing the payoff matrix, either a pure (deterministic)strategy or a mixed (probabilistic) strategy is selected. Three 10 km velocity profiles are predefined for simulation purposes. The simulation results demonstrate effective driving performance. In particular when it is compared with non-game theory cases, game theory based results show larger payoff for both vehicles and smaller payoff differences, securing safe manoeuvring via lane change manoeuvre (LCM) and adaptive cruise control (ACC).

System identification, health monitoring, and control design of smart structures and materials

In recent years, smart structures and materials have been adopted from many engineering fields because the performance of structural systems and materials can be improved without either significantly increasing the system mass or requiring high cost of control power. To implement such a smart control technology into complex systems, one of the most important but challenging tasks in smart system realization is the development of a mathematical model for system responses that will allow control system design and diagnosis procedures to be carried out in a timely manner. A large number of studies have been performed attempting to estimate behavior of smart system dynamics, using sets of input and output measurement, called system identification (SI). SI can be classified as parametric and non-parametric approaches. Parametric methods are used to determine a finite number of parameters such as mass, stiffness, and damping ratio, which are physical quantities of systems. In general, in order to identify an accurate system model using such a parametric approach, a sufficient number of modal parameters must be obtained. However, nonparametric methods determine infinite number of parameters and estimate the model parameters without full understanding of physical systems. The nonparametric method trains measured data to predict the system response even though the identified model does not directly represent the physical quantities. In other words, the dynamic system model can be determined even when little information on the system is provided. Furthermore, the nonparametric SI can be applied to input data or output data or both input and output data sets. In particular, the output data–based SI method has become of great significance in assessing dynamic systems since the input data are not readily available. Using the output SI approach, it is possible to identify the dynamic properties of the system in real operating conditions where the loading conditions are unknown. Control systems are classified into three parts: passive, active, and semi-active (or called smart). In particular, the smart control system has been paid a great attention from a variety of engineering field because the smart control system combines the best features of both active and passive control systems. The materials that are usually used to implement the smart structure are piezoelectrics, shape memory alloys, electrostrictive, magnetorheological materials, and polymer gels, among others. Selection and design of a control algorithm for optimal operation of control devices are very important for improving the performance of the smart structure and material systems. However, the performance of the smart control systems can degrade in the presence of sensor/actuator faults and/or system damage. To address the aforementioned issues, system health monitoring (SHM) has become increasingly important for complex systems because damage affects the current or future performance of the systems. SHM can provide information when the systems experience any significant change or damage. SHM improves the safety and reliability of critical systems by detecting the damage before they reach a critical state. It also allows rapid damage assessment. In order to practice SHM more efficiently, engineers and researchers have developed various global and local approaches. With these in mind, the aim of this Special Collection is to provide an opportunity for engineers to propose their latest theoretical and practical achievements in SI, health monitoring, and control system design of smart materials and structures under a variety of environmental forces.

Analytical Hierarchy Process and Brain Limbic System combined strategy for mobile robot navigation

In this paper, a fusion of Analytical Hierarchy Process (AHP) and Brain Limbic System (BLS) control strategy is suggested to achieve mobile robot navigation in unknown environments. As a multi-objective decision making method, AHP is employed to obtain the optimal gap among the obstacles that the robot encounters. To implement the suggested method, the weighting among objectives are defined and the relative importance matrix of each objective is generated. The robot is thus enabled to move to the desired position via a brain limbic system based controller. The simulation results demonstrate the performance of fused method.

Particle Swarm Optimization for Active Structural Control of Highway Bridges Subjected to Impact Loading

The application of active structural control technology to highway bridge structures subjected to high-impact loadings is investigated. The effects of high-impact loads on infrastructure, like heavy vehicle collisions with bridge piers, have not been studied as much as seismic load effects on structures. Due to this lack of research regarding impact loads and structural control, a focused study on the application of active control devices to infrastructure after impact events can provide valuable results and conclusions. This research applies active structural control to an idealized two-span, continuous girder, concrete highway bridge structure. The idealization of a highway bridge structure as a two degree-of-freedom structural system is used to investigate the effectiveness of control devices installed between the bridge pier and deck, the two degrees of freedom. The control devices are fixed to bracing between the bridge pier and girders and controlled by the proportional-integral-derivative (PID) control. The PID control gains are optimized by both the Ziegler–Nichols ultimate sensitivity method (USM) and a new method for this impact load application called particle swarm optimization (PSO). The controlled time-domain responses are compared to the uncontrolled responses, and the effectiveness of PID control, USM optimization, and PSO is compared for this control device configuration. The results of this investigation show PID control to be effective for minimizing both superstructure and substructure responses of highway bridges after high-impact loads. Deck response reductions of greater than 19% and 37% were seen for displacement and acceleration responses, respectively, regardless of the performance index used to analyze them. PSO was much more effective than USM optimization for tuning PID control gains.

An application of the brain limbic system–based control to the electromechanical brake system:

Recently, many mechanical components of vehicles are being replaced by electrical or electromechanical parts. This change leads to the reduced weight, higher fuel efficiency, and simpler diagnosis. In particular, a reliable and robust electromechanical brake control system has been focused. So, this article investigates that an application of brain limbic system–based control, a bio-inspired control strategy, to an electromechanical brake system is suitable for dynamic and uncertain driving conditions. To do this end, control parameters are optimized through genetic algorithm. Results show the effectiveness of the suggested control method in terms of control speed, reference tracking, and robustness to the disturbance.