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Ground speed control of a large wheel loader (LWL) is a very important part of a truck loading cycle. Since the engine is at full throttle for most part of a loading cycle, the ground speed is controlled by an impeller clutch/brake pedal. Essentially, this mechanical pedal, when engaged, disconnects the engine from the driveline and applies the service brakes. However, in order to properly control the ground speed of a large wheel loader, an appropriate powertrain control strategy is needed for the directional shifts (1R-1F, 2R-2F, etc.). These shifts are usually associated with unacceptable levels of jerk and acceleration. A reference trajectory for the vehicle speed based on the desired jerk and acceleration traces can be generated which, when properly tracked by appropriate control of the impeller clutch and the brakes, results in the desired levels of jerk and acceleration. A tracking controller is therefore appropriate.

This paper presents a novel design of a control law optimizing the performance of an on-demand all wheel drive (ODAWD) vehicle with hybrid powertrain for traction enhancement via slip regulation in a driving event. Based on a reasonably simplified vehicle model (bicycle model) and optimization of a performance index based on wheel slip, a closed loop actuator control law is derived. The proposed optimal controller tries to minimize the wheel slip error by activating and dynamically controlling the electric motor drive torque to the non-driven wheel pair (e.g. rear wheels), in order to enhance vehicle longitudinal traction. Simulation of the proposed controller was performed on a validated 14 degree-of-freedom detailed vehicle model in SIMULINK.

As dynamic system models become more complex, their computation times increase. Traditionally, the model, as a whole, would be evaluated at a single time step that would give the desired stability and accuracy for all states. It is hypothesized that the models be partitioned allowing different portions of the model be solved at different time steps, allowing each state to be evaluated at a time step that will give the desired stability and accuracy. Furthermore, with the model operating at several time steps, each time step could be solved on a separate processor of a multiple processor machine. Using a Simulink ® (Simulink) model of a multiple degree of freedom, spring, mass, damper system, multiple time steps were created through the use of rate transition blocks and discrete integrators. A multithreaded program was then created by modifying the rsim_main.C script.

This paper presents a nonlinear observer and long range prediction based analytical redundancy for a Steer-By-Wire (SBW) system. A Sliding Mode Observer was designed to estimate the vehicle steering angle by using the combined linear vehicle model, SBW system, and the yaw rate. The estimated steering angle along with the current input was used to predict the steering angle at various prediction horizons via a long range prediction method. This analytical redundancy methodology was utilized to reduce the total number of redundant road-wheel angle (RWA) sensors, while maintaining a high level of reliability. The Fault Detection, Isolation and Accommodation (FDIA) algorithm was developed using a majority voting scheme, which was then used to detect faulty sensor(s) in order to maintain safe drivability. The proposed observer-prediction based FDIA algorithms as well as the linearized vehicle model were modeled in MATLAB-SIMULINK.

This paper discusses the development of a Distributed Intelligent Ground Speed Control System, similar to a cruise control system, based on Fuzzy Logic. Fuzzy sets have been developed to input speed error, acceleration and the absolute speed error in order to arrive at a defuzzified output for the impeller clutch control, brake control and the control law selection. A PI controller and a Sliding Mode controller are utilized based on the magnitude of the Absolute Speed Error. A road model is introduced with erratic set speed profiles, which is introduced to replicate a similar situation for a Stop & Go procedure. The system is simulated in a MATLAB/SIMULINK environment and the results indicate smooth and cooperative switching between the controllers stimulated by the Fuzzy Logic Controller.

Generalized predictive control is a discrete time control strategy that was developed in the late 1980’s by Clark et al [1]. The controller tries to predict the future output of a system or plant and the take control action at present time based on future output error. It is the predictive nature of the controller that is utilized in having an effective noise attenuation in a 1-D noise propagation case (such as a cylindrical enclosure where noise propagates only in one direction). Simulation results are presented for the Generalized predictive controller (GPC) and then the results are compared against a more traditional controller.

Generalized predictive control (GPC) is a discrete time control strategy proposed by Clark et al [1]. The controller tries to predict the future output of a system or plant and then takes control action at present time based on future output error. Such a predictive control algorithm is presented in this paper for deceleration slip regulation in an automobile. Most of the existing literature on the anti-lock brake control systems lacks the effectiveness of the wheel lockup prevention when the automobile is in a skid condition (in a low friction coefficient surface with panic braking situation). Simulation results show that the predictive feature of the proposed controller provides an effective way to prevent wheel lock-up in a braking event.

Generalized predictive control (GPC) is a discrete time control strategy proposed by Clark et al [1]. The controller tries to predict the future output of a system or plant and then takes control action at present time based on future output error. Such a predictive control algorithm is presented in this paper for yaw stability management of an automobile. Most of the existing literature on the yaw stability management systems lacks the insight into the yaw rate error growth when the automobile is in a understeer or oversteer condition on a low friction coefficient surface in a handling maneuver. Simulation results show that the predictive feature of the proposed controller provides an effective way to control the yaw stability of a vehicle.

Accuracy in detecting a moving object is critical to autonomous driving or advanced driver assistance systems (ADAS). By including the object classification from multiple sensor detections, the model of the object or environment can be identified more accurately. The critical parameters involved in improving the accuracy are the size and the speed of the moving object. All sensor data are to be used in defining a composite object representation so that it could be used for the class information in the core object’s description. This composite data can then be used by a deep learning network for complete perception fusion in order to solve the detection and tracking of moving objects problem. Camera image data from subsequent frames along the time axis in conjunction with the speed and size of the object will further contribute in developing better recognition algorithms.

In this paper, multiple-model adaptive estimation techniques have been successfully applied to fault detection and identification in lithium-ion batteries. The diagnostic performance of a battery depends greatly on the modeling technique used in representing the system and the associated faults under investigation. Here, both linear and non-linear battery modeling techniques are evaluated and the effects of battery model and noise estimation on the over-charge and over-discharge fault diagnosis performance are studied. Based on the experimental data obtained under the same fault scenarios for a single cell, the non-linear model based detection method is found to perform much better in accurately detecting the faults in real time when compared to those using linear model based method.

In this paper, the design, implementation, and testing of an autonomous agricultural robot with GPS guidance is presented. This robot is also responsible for weed detection and killing by spraying appropriate herbicide as well as fertilizing. This rover is powered by 5 12 V electric bike batteries and two electric motors. Machine learning algorithms such as Haar feature-based cascade classifiers has been utilized to detect three kinds of common weeds found in a corn field. The robot control system consists of GPS guided control of propulsion system and steering actuators, an image processing and detection system, and a spray control system for herbicide and fertilizer applications. Multiple microprocessors such as Raspberry Pi 3, Arduino, as well as an on-board computer have used to provide all control functions in an integrated fashion. Open sources software such as Mission Planner and ReachView have been used to provide autonomous guidance of the vehicle.

In this paper, feasibility study was completed to prove the ability of detecting soot load amount in diesel particulate filter using electromagnetic finite element model. It is critical to accurately measure the soot load in a Diesel Particulate Filter (DPF) in order to optimize the active regeneration process that clears the filter clogged with soot. Tetrahedral mesh was created with electrostatic solid element for this analysis. Because of the geometry of the DPF and the narrowness of some sections between holes, a very fine mesh was generated. A model consistency study was created illustrating the effect of clogging on the internal electric field of the filter. Multiple scenarios representing various clogging configurations were simulated. These configurations included a fully clogged filter, the top-right quadrant of holes clogged, and the lower half of holes clogged.

Generalized predictive control (GPC) is a discrete time control strategy proposed by Clark et al [1]. The controller tries to predict the future output of a system or plant and then takes control action at present time based on future output error. Such a predictive control algorithm is presented in this paper for acceleration slip regulation in an automobile. Most of the existing literature on the brake based traction control systems (BTCS) lacks the insight into the wheel slip growth when the automobile is on a low friction coefficient surface and the driver has the throttle wide open. Simulation results show that the predictive feature of the proposed controller provides an effective way to control the wheel slip in a vehicle acceleration event.

Ground speed control of large wheel type loaders using the impeller clutch and brakes while the engine is at full throttle is investigated in this paper. The control strategy requires the engagement and disengagement of the impeller clutch or the brakes, making the closed loop system discontinuous. Use of a single control strategy such as a Proportional plus Integral (PI) or Sliding Mode (SM) controller may not result in a satisfactory response, particularly with respect to response time, vehicle jerk & acceleration, and chatter due to brake/impeller clutch switching. The sliding mode controller has a fast response time without causing instability, but may cause controller chatter in situations where the boundary layer thickness (an envelope around the sliding surface) is not sufficient. On the other hand, a PI controller can be made free of such controller chatter with appropriate gain selection. However, it tends to be slow responding when high overshoot is to be avoided.

A Generalized Predictive Kinetic Energy Controller (GPKEC), which has been previously developed, is implemented to control the machine tool vibration in a lathe turning process. The control variable, tool feed, is computed using acceleration feedback through the GPKEC algorithm. The feed is controlled by attaching a high torque/low inertia permanent magnet DC servomotor to the main feed rod through a high performance timing belt. Experiments are carried out for a number of cases. The system identification of the overall machining process is done on-line and precedes the control action. Accelerometers have been used to sense the vibration signal in the feed direction. The experimental results show that GPKEC can effectively suppress the chatter vibration in a single point turning process, even in presence of an appreciable change in the dynamics of the process. GPKEC has also been observed to be robust against step disturbances.