Abstract: Automation technologies for agriculture field are needed to promote labor-saving. One of the most relevant problems in automated agriculture is represented by controlling the robot along a predetermined path in presence of rough terrain or incline ground. Unfortunately, disturbances originating from interaction with the ground, such as slipping, make it quite difficult to achieve the required accuracy. In general, it is required to move within 5-10 cm accuracy with respect to the predetermined path. Moreover, lateral velocity caused by gravity on the incline field also affects slipping. In this paper, a path-tracking controller for tracked mobile robots moving on rough terrains of incline field such as vineyard is presented. The controller is composed of a disturbance observer and an adaptive controller based on the kinematic model of the robot. The disturbance observer measures the difference between the measured and the reference yaw rate and linear velocity in order to estimate slip. Then, the adaptive controller adapts “virtual” parameter of the kinematics model: Instantaneous Centers of Rotation (ICRs). Finally, target angular velocity reference is computed according to the adapted parameter. This solution allows estimating the effects of slip without making the model too complex. Finally, the effectiveness of the proposed solution is tested in a simulation environment.
Abstract: Aiming at the verification of control algorithms for skid-steering vehicles, a vehicle simulation model of 6×6 electric skid-steering unmanned vehicle was established based on Trucksim and Simulink. The original transmission and steering mechanism of Trucksim are removed, and the electric skid-steering model and a closed-loop controller for the vehicle speed and yaw rate are built in Simulink. The simulation results are compared with the ones got by theoretical formulas. The results show that the predicted tire mechanics and vehicle kinematics of Trucksim-Simulink simulation model are closed to the theoretical results. Therefore, it can be used as an effective approach to study the dynamic performance and control algorithm of skid-steering vehicle. In this paper, a method of motion control based on feed forward control is also designed. The simulation results show that the feed forward control strategy can make the vehicle follow the target yaw rate more quickly and accurately, which makes the vehicle have more maneuverability.
Abstract: This paper studies the design of an adaptive control strategy to tune an active steering system for better drivability and maneuverability. In the first step, adaptive control strategy is applied to estimate the uncertain parameters on-line (e.g. cornering stiffness), then the estimated parameters are fed into the pole placement controller to generate corrective feedback gain to improve the steering system dynamic’s characteristics. The simulations are evaluated for three types of road conditions (dry, wet, and icy), and the performance of the adaptive pole placement control (APPC) are compared with pole placement control (PPC) and a passive system. The results show that the APPC strategy significantly improves the yaw rate and side slip angle of a bicycle plant model.
Abstract: Active Front Steering system (AFS) provides an electronically controlled superposition of an angle to the steering wheel angle. This additional degree of freedom enables a continuous and driving-situation dependent on adaptation of the steering characteristics. In an active steering system, there needs be no fixed relationship between the steering wheel and the angle of the road wheels. Not only can the effective steering ratio be varied with speed, for example, but also the road wheel angles can be controlled by a combination of driver and computer inputs. Features like steering comfort, effort and steering dynamics are optimized and stabilizing steering interventions can be performed. In contrast to the conventional stability control, the yaw rate was fed back to AFS controller and the stability performance was optimized with Sliding Mode control (SMC) method. In addition, tire uncertainties have been taken into account in SM controller to provide the control robustness. In this paper, 3-DOF nonlinear model is used to design the AFS controller and 8-DOF nonlinear model is used to model the controlled vehicle.
Abstract: This paper addresses the problem of trajectory
tracking control of an underactuated autonomous underwater vehicle
(AUV) in the horizontal plane. The underwater vehicle under
consideration is not actuated in the sway direction, and the system
matrices are not assumed to be diagonal and linear, as often found in
the literature. In addition, the effect of constant bias of environmental
disturbances is considered. Using backstepping techniques and the
tracking error dynamics, the system states are stabilized by forcing
the tracking errors to an arbitrarily small neighborhood of zero. The
effectiveness of the proposed control method is demonstrated through
numerical simulations. Simulations are carried out for an
experimental vehicle for smooth, inertial, two dimensional (2D)
reference trajectories such as constant velocity trajectory (a circle
maneuver – constant yaw rate), and time varying velocity trajectory
(a sinusoidal path – sinusoidal yaw rate).
Abstract: An integrated vehicle dynamics control system is developed in this paper by a combination of active front steering (AFS) and direct yaw-moment control (DYC) based on fuzzy logic control. The control system has a hierarchical structure consisting of two layers. A fuzzy logic controller is used in the upper layer (yaw rate controller) to keep the yaw rate in its desired value. The yaw rate error and its rate of change are applied to the upper controlling layer as inputs, where the direct yaw moment control signal and the steering angle correction of the front wheels are the outputs. In the lower layer (fuzzy integrator), a fuzzy logic controller is designed based on the working region of the lateral tire forces. Depending on the directions of the lateral forces at the front wheels, a switching function is activated to adjust the scaling factor of the fuzzy logic controller. Using a nonlinear seven degrees of freedom vehicle model, the simulation results illustrate considerable improvements which are achieved in vehicle handling through the integrated AFS/DYC control system in comparison with the individual AFS or DYC controllers.
Abstract: A variable structure model reference adaptive control
(VS-MRAC) strategy for active steering assistance of a two wheel
steering car is proposed. An ideal steering system with fixed
properties and moving on an ideal road is used as the reference
model, and the active steering assistance system is forced to attain
the same behavior as the reference model. The proposed system can
treat the nonlinear relationships between the side slip angles and
lateral forces on tire, and the uncertainties on friction of the road
surface, whose compensation are very important under critical
situations. Simulation results show improvements on yaw rate and
side slip.
Abstract: The need for micromechanical inertial sensors is increasing
in future electronic stability control (ESC) and other positioning,
navigation and guidance systems. Due to the rising density of
sensors in automotive and consumer devices the goal is not only to get
high performance, robustness and smaller package sizes, but also to
optimize the energy management of the overall sensor system. This
paper presents an evaluation concept for a surface micromachined
yaw rate sensor. Within this evaluation concept an energy-efficient
operation of the drive mode of the yaw rate sensor is enabled. The
presented system concept can be realized within a power management
subsystem.
Abstract: Vehicle which are turning or maneuvering at high speeds
are susceptible to sliding and subsequently deviate from desired path. In
this paper the dynamics governing the Yaw/Roll behavior of a vehicle
has been simulated. Two different simulations have been used one for
the real vehicle, for which a fuzzy controller is designed to increase its
directional stability property. The other simulation is for a hypothetical
vehicle with much higher tire cornering stiffness which is capable of
developing the required lateral forces at the tire-ground patch contact to
attain the desired lateral acceleration for the vehicle to follow the
desired path without slippage. This simulation model is our reference
model.
The logic for keeping the vehicle on the desired track in the cornering
or maneuvering state is to have some braking forces on the inner or
outer tires based on the direction of vehicle deviation from the desired
path. The inputs to our vehicle simulation model is steer angle δ and
vehicle velocity V , and the outputs can be any kinematical parameters
like yaw rate, yaw acceleration, side slip angle, rate of side slip angle
and so on. The proposed fuzzy controller is a feed forward controller.
This controller has two inputs which are steer angle δ and vehicle
velocity V, and the output of the controller is the correcting moment M,
which guides the vehicle back to the desired track. To develop the
membership functions for the controller inputs and output and the fuzzy
rules, the vehicle simulation has been run for 1000 times and the
correcting moment have been determined by trial and error. Results of
the vehicle simulation with fuzzy controller are very promising
and show the vehicle performance is enhanced greatly over the
vehicle without the controller. In fact the vehicle performance
with the controller is very near the performance of the reference
ideal model.