Modeling, identification and analysis of tractor and single axle towed implement system

Increased and sustained agricultural productivity is a key to meet the globally increasing demands for food and energy. Automation of agricultural machinery is one of the ways to improve the efficiency and productivity of various field operations. Because a field implement performs most of these operations, accurate implement guidance is needed to reduce production cost, increase yield, and improve sustainability. Model-based guidance controller design and virtual prototyping techniques can be used in automatic guidance controller development to improve the accuracy and robustness of the guidance controller while reducing the development time and cost. Hence, development and analysis of accurate tractor and implement system models are needed to support automatic tractor and implement guidance controller development. Real-time vehicle model simulation capability allows engineers and users to intuitively interact with the realistic virtual prototypes and to evaluate the performance of physical hardware. As the model complexity is increased to improve the model accuracy and/or fidelity, the computational need will also increases thus increasing the challenge to meet real-time constraints. In this regard, it is important to minimize the computational load to a Virtual Reality (VR)-based real-time dynamics model simulation system. In this dissertation, various strategies were investigated to reduce the computational burden on the dynamics model simulation so that real-time simulation could be achieved for increasingly complex models. A distributed architecture was developed for a virtual reality-based off-road vehicle real-time simulator to distribute the overall computational load of the system across multiple machines. Multi-rate model simulation was also used to simulate various system dynamics with different integration time steps so that the computational power can be distributed more intelligently. It is also important to study the trade-off between the model accuracy/fidelity and model complexity. Three different tractor-and-single-axle-towed-implement system models with varying degrees of fidelity, namely a kinematic model, a dynamic model, and a dynamic model with tire relaxation length, were developed, and the simulated transient and steady state responses were compared at various forward velocities and input frequencies. Both open and closed loop system characteristics were studied. Field experiments were also carried out to characterize the input-output relationship of the tractor-implement steering system. The responses from all three models were similar at lower forward velocities and with low frequency steering inputs (\u3c 0.2 Hz). However, when the system was operated at higher forward velocities or with higher frequency steering inputs, the responses from the three models varied substantially. In this case, the dynamic model with tire relaxation length best represented the experimental system responses. The system model contained various uncertain or varying parameters. It was important to understand and quantify the effect of parameter variation on system responses. Sensitivity analysis was used to identify the effect of variation in tire cornering stiffness, tire relaxation length, and implement inertial parameters on simulated system responses. Overall, the system was most sensitive to the tire cornering stiffness and least sensitive to the implement inertial parameters. In general, the uncertainty in the input parameters and the output variables were related in a non-linear fashion. At 4.5 m/s forward velocity, a 10% uncertainty in cornering stiffness caused a 2% average output uncertainty whereas a 50% uncertainty in cornering stiffness caused a 20% output uncertainty. Finally, a parameter identification method was used to estimate the uncertain model parameters from measured field data. The accuracy of the model responses improved substantially when the model was simulated with the estimated parameters. It was concluded that a dynamic model with tire relaxation length will represent a tractor and single axle towed implement system with reasonable accuracy. The study also helped improve the understanding of the relative importance of various model parameters, which will help to more judiciously allocate resources for estimating system parameters. Moreover, the analysis indicated that various vehicle parameters can be estimated with reasonable accuracy using a dynamic model, experimental data, and a parameter estimation method. The work will provide a framework for off-road vehicle and implement simulation through which engineers and scientists can determine to which parameters the system is most sensitive and how a model would perform with estimated model parameters

Towards the development of a smart flying sensor: illustration in the field of precision agriculture

Sensing is an important element to quantify productivity, product quality and to make decisions. Applications, such as mapping, surveillance, exploration and precision agriculture, require a reliable platform for remote sensing. This paper presents the first steps towards the development of a smart flying sensor based on an unmanned aerial vehicle (UAV). The concept of smart remote sensing is illustrated and its performance tested for the task of mapping the volume of grain inside a trailer during forage harvesting. Novelty lies in: (1) the development of a position-estimation method with time delay compensation based on inertial measurement unit (IMU) sensors and image processing; (2) a method to build a 3D map using information obtained from a regular camera; and (3) the design and implementation of a path-following control algorithm using model predictive control (MPC). Experimental results on a lab-scale system validate the effectiveness of the proposed methodology

DESIGN OF AVIONICS AND CONTROLLERS FOR AUTONOMOUS TAKEOFF, HOVER AND LANDING OF A MINI-TANDEM HELICOPTER

Robotics autonomy is an active research area these days and promises very useful applications. A lot of research has been carried out on Vertical Takeoff and Landing (VTOL) vehicles especially single rotor small scale helicopters. This thesis focuses on a small scale twin rotor helicopter. These helicopters are more useful because of their power efficiency, scalability, long range of center of gravity, shorter blades and most importantly their "all lift" feature. By "all lift" we mean that unlike single rotor helicopters where tail rotor's power is wasted just to cancel the torque of the main rotor both of its rotors are used for generating lift. This makes twin rotors ideal for lifting heavy weights. This thesis considers avionics systems and the controllers development for a twin rotor. It involves electronic component selection and integration, software development, system identification and design of zero rate compensators. The compensators designed are responsible for autonomous take-off, hover and landing of this unmanned aerial vehicle (UAV). Both time and frequency domain system identification approaches were evaluated and a selection was made based on hardware limitations. A systematic approach is developed to demonstrate that a rapid prototyping UAV can be designed from cheap off-the-shelf components that are readily available and functionally compatible. At the end some modifications to existing mechanical structure are proposed for more robust outdoor hovering

The stability of the tethered trailer and its control

Tethered trailer vehicle is a nonprofessional tractor that drags an unpowered vehicle with rope. In this paper, a nonlinear dynamic model of the tractor is developed. With the Dugoff’s tire model. A new nonlinear tethered tractor-trailer model is created to simulate critical parameters. A trailer front-wheel steering feedback control strategy is derived in order to improve stability and trajectory tracking feature the comparison of the simulation results for tension of the traction rope, the trajectory following resistance, and the handling stability clearly demonstrates the efficacy of the proposed control strategy

Development of an intelligent master-slave system between agricultural vehicles

This paper presents a method to develop an intelligent master-slave system between agricultural vehicles, which will enable a semi-autonomous agricultural vehicle (slave) to follow a leading tractor (master) with a given lateral and longitudinal offset. In our study not only the follow-up motions but also the site-specific control of the apparatus such as rear and front power lift was considered. In the first part of this paper the recent research works in the area autonomous farming were discussed and the restrictions of these research works were illustrated. In the second part an approach to construct a master-slave system between two agricultural vehicles was demonstrated. In the next part the mathematic modelling of this master-slave system and the simulation results about the control algorithm were demonstrated. Afterwards the result of a real field test was presented and the safety considerations about such an intelligent vehicle system were made

Correct-By-Construction Control Synthesis for Systems with Disturbance and Uncertainty

This dissertation focuses on correct-by-construction control synthesis for Cyber-Physical Systems (CPS) under model uncertainty and disturbance. CPSs are systems that interact with the physical world and perform complicated dynamic tasks where safety is often the overriding factor. Correct-by-construction control synthesis is a concept that provides formal performance guarantees to closed-loop systems by rigorous mathematic reasoning. Since CPSs interact with the environment, disturbance and modeling uncertainty are critical to the success of the control synthesis. Disturbance and uncertainty may come from a variety of sources, such as exogenous disturbance, the disturbance caused by co-existing controllers and modeling uncertainty. To better accommodate the different types of disturbance and uncertainty, the verification and control synthesis methods must be chosen accordingly. Four approaches are included in this dissertation. First, to deal with exogenous disturbance, a polar algorithm is developed to compute an avoidable set for obstacle avoidance. Second, a supervised learning based method is proposed to design a good student controller that has safety built-in and rarely triggers the intervention of the supervisory controller, thus targeting the design of the student controller. Third, to deal with the disturbance caused by co-existing controllers, a Lyapunov verification method is proposed to formally verify the safety of coexisting controllers while respecting the confidentiality requirement. Finally, a data-driven approach is proposed to deal with model uncertainty. A minimal robust control invariant set is computed for an uncertain dynamic system without a given model by first identifying the set of admissible models and then simultaneously computing the invariant set while selecting the optimal model. The proposed methods are applicable to many real-world applications and reflect the notion of using the structure of the system to achieve performance guarantees without being overly conservative.PHDMechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/145933/1/chenyx_1.pd