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Advanced driver's assist systems for optional self-driving vehicles require highly reliable navigation capabilities to ensure dependable autonomous function of vehicle safety and driver assistance. Radars, lidars and cameras have found widespread application in nearly all vehicle brands for this purpose. In this paper we present the implementation of these sensors for constructing a 360-degree field of view of the vehicle surrounding. A stereo vision system is complemented by several long, mid and short-range radar sensors. The data gathered from the advanced sensors is prioritized and fused to create a model of the surrounding. The system is being tested on an autonomous car vehicle. In particular, this paper discusses three clustering algorithms that were tested.

Knock detection occupies a very important role in the ignition control of a Spark Ignition (S.I.) engine. Production-grade knock detection schemes must be computationally efficient and comprise a set of parameters which require calibration during Engine Control Unit (ECU) development. This is typically achieved empirically, whereby a calibration engineer induces knock on the ECU and monitors the detection performance to manually adjust the tunable parameters. In this paper, we propose automatic, on-line calibration of a production-grade, frequency-domain knock detection system. Compared to the conventional method of manual calibration, this method allows for on-line tuning of the parameters based on scientific optimization schemes. It thus enables the implementation of specific objective functions which can meet desired criteria.

The pole placement (PP) technique for design of a linear state feedback control system requires specification of all the closed-loop pole locations even though only a few poles dominate the system's transient response characteristics. The linear quadratic regulator (LQR) method, on the other hand, optimizes the system transient response and does not directly impose the location of the dominant poles. This paper presents a new optimal linear quadratic pole placement (LQPP) technique that simultaneously assigns some poles to exact desired dominant locations (partial pole placement) and adjusts the rest of the poles to optimize an LQ performance index (parametric optimization). As a result, a designer can fashion his control design to benefit from the PP and LQ techniques. The paper only addresses the LQPP technique for a single-input full-state feedback control system.

Flocking is a term that describes the behavior of a group of birds (a flock) in flight, or the swarming behavior of insects. This paper presents detailed information about how to use the flocking techniques to control a group of embedded controlled systems “Boids” such as ground systems (robotic vehicles/ swarm robots) in 2D and/or aerial systems (quadrotors) in 3D. Each one of these systems moves inside/outside of a building collectively to reach a target. This paper presents flocking behavior implemented on a server based control, which will process each of the boids properties e.g. position, speed & target. Subsequently the server will assign the appropriatee move to a specific boid. The calculated information will be used locally to control and direct the movements/flocking for each boid in the group. A simulation technique and detailed flow chart is presented. In addition to the famous Reynolds' Rules for flocking two other rules are presented; one for targeting and another for avoiding obstacles. The targeting rule was implemented to ensure that the flock moves collectively towards a common target; it was implemented by creating a target velocity parameter. The avoiding obstacles rule was implemented to ensure that the flock doesnt collide with obstacles that they could be in the path; it was implemented by creating an avoiding velocity parameter.

Emerging technologies in modeling and simulation of dynamic systems provide transportation industry with the challenging opportunity to migrate parts of the design and development process of new vehicles to the virtual environments. This paper presents the application of a full vehicle simulation in a distributed real-time simulation configuration. The purpose of the system is to provide for an easy-to use full-vehicle simulation platform that allows human-in-the-Ioop interactive driving with force and motion feedback, and for hardware-in-the-Ioop prototype evaluation through simulation. The contribution that is highlighted in this paper is the networking configuration for distributed simulation. The user front-end in Matlab/Simulink provides a familiar interface where the designer/ engineer is able to pull up any dynamic signal from the vehicle simulation, and is able to replace any subsystem from a remote unit as well.

The ability to determine an accurate global position of a vehicle has many useful commercial and military applications. The differential global positioning system (DGPS) is one of the practical navigation tools used for this purpose. However, the DGPS has limitations arising from slow updates, signal interference, and limited accuracy. This paper describes how vehicle rate sensors ran be used to help a DGPS overcome these limitations. The theoretical background for the sensor fusion is based on the principle of Kalman filtering and a fuzzy logic scheme. Validity of the method was verified by using experimental data from an actual automobile navigating around an urban area. The results demonstrated that the path of the automobile can be continuously traced with high accuracy and repeatability, in spite of the limitations of the DGPS.

In this paper, a review of mechatronics is presented with emphasis on the desired qualities of university graduates as viewed by industry. A detailed description of a sequence of two new courses on automotive mechatronic systems and a specialized laboratory are discussed. The new coursework has been added to the curriculum towards the degree of systems engineer at Oakland University and will be first offered in Fall '97. The development of the courses and the laboratory has been supported by Ford Motor Company.

A hierarchical intelligent control system paradigm for a vision-based autonomous driving scheme is presented for a leader-following HMMWV. This article shows how fuzzy logic is employed to represent knowledge at the organization level, how to resolve conflicting perceived information at the coordination level, and how to carry out the best action for the vehicle at the execution level. After introducing the methodology, simulation and experimental aspects for realizing and testing the scheme are discussed. ©1997 by John Wiley & Sons, Inc.

An innovation for a standard intermittent windshield wiper is the concept of a smart wiper that automatically turns itself on or off and adjusts its wiping cycle according to the intensity of rain. This article describes the development of a smart automatic windshield wiper system using a fuzzy logic-based conductive rain sensor scheme. Fuzzy logic is used to compensate nonlinearities in the sensor and implement intuitive reasoning for automatic operation of the wiper. An M68HC11-based microcontroller was developed to implement and test the smart wiper concept for a car. The proposed approach can be extended to other applications related to rain detection and system actuation.

This paper describes a fuzzy logic (FL) approach for incorporating an expert knowledge-based Traction Control System (TCS) for a four-wheel drive vehicle. The knowledge base is provided by a survey of existing TCS methods, understanding of the TCS hardware components and tyre adhesion characteristics, evaluation of applicable control methods, computer simulation studies, and feedback information from experienced traction test drivers in the automotive industry. The developed FL-bascd TCS was implemented on a Pentium PC system- level traction controller computer installed on a 4x4 pickup truck. Extensive experiments for traction control was conducted at available test facilities and proving grounds. The system-level fuzzy logic approach readily permits modification of old and incorporation of new ideas. The success of this fuzzy logic-based design approach was demonstrated to the government sponsor and industrial partners.

The chapter presents an approach to control the biped humanoid robot to ambulate through human imitation. For this purpose a human body motion capturing system is developed using tri-axis accelerometers (attached to human legs and torso). The tilt angle patterns information from the human is transformed to control and teach various ambulatory skills for humanoid robot bipedalism. Lyapunov stability based model reference adaptive controller (MRAC) technique is implemented to address unpredictable variations of the biped system.

The Stewart platform (SP) is a parallel closed-kinematic chain robotic mechanism that is capable of providing high structural and positional rigidity. Because of its unique capability, the platforms have been employed in many control engineering applications such as simulator shakers, robotic manipulators, etc. However, a main problem often found in the implementation of a real-time controller for the platform is the lack of an efficient algorithm for solving its highly nonlinear forward kinematic transformation (FKT), where one seeks to find the translational and orientational altitudes of the moveable platform from knowing the lengths of the platform linkages. This article describes two new direct and exact methods for computing the translational and rotational displacements of an SP by employing extra transnational displacement sensors (TDSs), in addition to the existing TDSs for the six links of the SP. The key for the approach lies in knowing where to employ the TDSs for determining positional vectors of strategic platform locations. By taking advantage of a tetrahedral geometry, closed-form solutions for the FKT can then be derived and directly evaluated. The new methods produce accurate solutions with only minimal computation necessary. The advantages and disadvantages of the proposed methods are discussed and compared to an existing method. The exact methods are being investigated for an on-line implementation of a nonlinear adaptive control system and redundancy scheme for a 25-ton Stewart platform-based Crew Station/Turret Motion Base Simulator (CS/TMBS) at the U.S. Army Tank-Automotive Command (TACOM).

This paper addresses challenges in managing complex automotive powertrain control systems by employing a heuristic method to determine complex setpoints. Two main areas of focus are the fuzzy determination of the driver characteristics and the gear selection for discrete automatic transmissions. The driver characterization determines how aggressive or docile a driver is and has a weighted impact on the gear selection. The gear selection process captures transmission performance fundamentals and infuses them with logic to eliminate undesired or physically impossible gears.

Thousands of people die every year in car accidents. Current restraint systems such as conventional seatbelts and airbags are designed to accommodate several occupant/crash scenarios and lack the capability of varying their output during the impact event. This paper introduces and demonstrates the benefits of real time control of a novel adaptive restraint system. Through applying intelligent control techniques, it will be shown in this paper that occupant injuries during frontal crashes can be minimized to much lower levels than what can be achieved today with conventional restraint systems. Our control system will be designed and tested first using a lumped parameter model and second on a complete crash simulation environment.

In every vehicle collision, the work-energy principle must be satisfied, i.e., the work done in stopping the driver must be equal to the driver's kinetic energy. Existing restraint systems such as airbags and seatbelts are designed to minimize injuries by smoothly absorbing the kinetic energy of the occupant during a crash event. Advanced systems are being developed to tailor their level of restraining to the severity of the crash, weight, size and position of the occupant. Starting with a lumped parameter model and ending with a complete crash simulation environment, this paper introduce and demonstrate the benefits of real time control of a novel adaptive restraint system using intelligent control techniques.

A model reference adaptive control (MRAC) technique for vehicle active suspension subsystems is presented. The MRAC automatically self-tunes the active suspension so that disturbance and vibration of a vehicle is reduced to a level determined by an ideal conceptual suspension reference model. The Lyapunov stability method was used in the design of the MRAC. It is shown that the MRAC suspension can accommodate large variances in sprung load and suspension component characteristics and achieves significant improvements over the passive suspension. Real-time simulation and animation (RTSA) software was developed to provide a visual aid for understanding and interpreting the performance of the MRAC suspension. >

We demonstrate that an omni-directional vehicle (ODV) with an intelligent motion planning (IMP) scheme and a combination of Ackerman & parallel steering capabilities can readily maneuver in constricted areas. A virtual simulation environment (VSE) has been developed to model the ODV motion, a suite of navigation sensors, surrounding objects and the IMP scheme. The IMP unifies the navigation sensors to enhance manual, semi-autonomous and autonomous operations. The VSE is used to demonstrate that the ODV with IMP can navigate tight areas around obstacles, saving energy and time. Metrics for performance of Ackerman versus parallel steering capabilities are also evaluated.

A supervised training algorithm and an unsupervised learning algorithm for neural models are derived using Lyapunov stability theory and the concept of model reference adaptive control (MRAC). The practicability of the algorithm is demonstrated by its successful application to a semiactive suspension system. The neurocontroller establishes its own control laws for improving the suspension performance without requiring a complete knowledge of the system dynamics. It also makes it possible to evaluate the roles of simple sensors in defining the control laws. Simulation results illustrate the self-improving suspension performance and show that superior suspension performance can be achieved using the neurocontrol. It is found that the discretized version of the developed training and learning algorithms corresponds to certain well-known existing algorithms. >

A description is given of a cognitive preview control strategy for autonomous-vehicle steering and cruise guidance by combining optimal preview control theory with rule-based perceptive cruise command generation. The authors also propose a self-training cognition procedure for determining a suitable perceptive schedule for cognitive cruise and steering control. The control yields humanlike driving action in path navigation. It is an intelligent control that decides the cruising speed, plans its control action, and learns the limitation of its steering control. The strategy is being simulated and tested on an autonomous robotic vehicle testbed which is designed for intelligent control experimentation. >

An enhanced maximal nonsaturating effort adaptive control (EMNEAC) scheme for servo-control systems that have input saturation and deadbands is described. The proposed EMNEAC builds on two key online enhancement techniques: a knowledge-enhanced parameter identification (KEPI) algorithm and a maximal nonsaturating effort selection (MNES) algorithm. The KEPI algorithm uses a priori information about the characteristics of the physical system being controlled. It generates parameter estimates which lie within admissible ranges of physical values. The EMNEAC scheme incorporates an automatic self-selection of suitable closed-loop performance specifications for online self-tuning control design. It uses heuristic information derived from computed control input and designs a controller whose output level is kept with the admissible set of control. The result is that the EMNEAC scheme puts out maximal control effort for high tracking performance without saturating the control input of the system. It is more robust in terms of stability and achieves superior performance over other standard self-tuning control algorithms. >

The jumbo container cranes at the European Container Terminal in Rotterdam are subject of a long term study to automate the international seaport and make container handling more efficient. In the paper appropriate details of the 3D dynamical behavior of the container is modeled. Simulation and visualization are carried out to study the crane behavior under disturbing conditions like unbalanced center of gravity and varying side winds. The software is used to evaluate time-optimal trajectories and control schemes for the dynamics of the electrical drives, and of the swing and skew of the container.

This paper describes a laboratory project for teaching the fundamental concepts of electrohydraulic servomechanism to undergraduate/graduate control engineering students. Although electrohydraulic servomechanisms have dominated the industrial control scene for quite some time, typical control courses do not have these as an integral part of the overall laboratory experience. Thus, the main objective of the proposed experiments is to alleviate this deficiency to a certain extent. The paper includes detailed descriptions of the experimental setup and the laboratory projects.

In recent years, there has been a growing interest in controlling both active and semi-active automotive suspension systems with a goal of improving ride comfort and vehicle handling. Many such resulting approaches have used linearized models Of the syspension's dynamics, allowing th use of linear (optimal) control theory. In actuality through, these systems and their optimal control are quite nonlinear. In this paper we propose a novel, yet highly practical alternative to such linearized design methods. This alternate optimal design method consists of a modified A* optimal-path, farward-search algorithm which is highly efficient, together with neural networks. The A* search, using a reasonably accurate system model and a given cost function, establishes te nonlinear optimal parametric control Of the suspension. The neural network, as will be shown, learns this nonlinear optimal control function, and in many ways outperforms the search from which it was taught.

A nonlinear Self-Tuning Control (STC) scheme for the nonlinear vehicle active suspension system is investigated and analyzed as an alternative suspension control methodology. The result of the nonlinear STC approach is compared with the linear STC and the model reference adaptive control (MRAC) schemes. The objective of these adaptive control active suspension systems (ACASS) is to tune the controller of the active suspension system automatically such that vehicle disturbance and vibration are reduced to a prespecified level determined by an "ideal" conceptual suspension system. Thus, an ACASS can accommodate for variances in sprung load and suspension component characteristics. The design and performance analysis of a nonlinear STC suspension system is based on a quarter-car model. Our results show significant improvement in the road handling performance of a high-speed vehicle with the STC active suspension system.

Application of Model Reference Adaptive Control (MRAC) to vehicle active suspension subsystem is investigated. The MRAC objective is to automatically self-tune the controller of the active suspension such that disturbance and vibration of a vehicle is reduced to a level which is determined by an "ideal" conceptual suspension system (a reference model). An MRAC vehicle suspension can thus accommodate for variances in sprung load and suspension component characteristics. An MRAC was designed using Lyapunov stability approach and was tested using different scenarios such as ±10% to ±30% variances of suspension components and sprung load. It is shown that the MRAC suspension closely followed the performance of the prespecified reference model and achieve significant improvements over passive suspension. Real-Time Simulation and Animation (RTSA) software was developed to provide an excellent visual aid for understanding and interpreting the better ride performance achieved by the MRAC suspension.

The problem of vibration isolation is investigated from the standpoint of modern control and optimization theory. The proposed optimal model-following suspension design was verified experimentally by means of a microcomputerized suspension model. The experimental results are very encouraging and indicate promising potential application of the proposed scheme to real-world systems. This paper describes the modeling and formulation of an optimal control suspension reference model, and the implementation algorithm of the proposed microcomputerized optimal model-following suspension scheme.

The modeling, analysis and design of a ball-balancing control system is considered. The control system is constructed and an optimal hybrid controller capable of absorbing external disturbance is fabricated. The controller design is based on the theory of disturbance-accommodating control, optimal control and digital delayed-data observer. Experimental results presented are useful for demonstrating the practicalilty aspects of the above theory.

The design and implementation of a controller for a six-degree-off-freedom robot manipulator is presented. Two IBM Personal Computers were used to implement the preliminary scheme reported here where execution of programmed motion can be carried out in either point-to-point or straight line mode.

The potential of a new adaptive control for reactive power sources is investigated. In the large disturbance regimes, the adaptive signals supplement the nominal, thyristor activated, reactors and capacitors in manipulating the network voltage. Convergence of the scheme, and its relationship to the inherent system nonlinearities is considered.