Nonlinear Modeling and Control of Driving Interfaces and Continuum Robots for System Performance Gains

With the rise of (semi)autonomous vehicles and continuum robotics technology and applications, there has been an increasing interest in controller and haptic interface designs. The presence of nonlinearities in the vehicle dynamics is the main challenge in the selection of control algorithms for real-time regulation and tracking of (semi)autonomous vehicles. Moreover, control of continuum structures with infinite dimensions proves to be difficult due to their complex dynamics plus the soft and flexible nature of the manipulator body. The trajectory tracking and control of automobile and robotic systems requires control algorithms that can effectively deal with the nonlinearities of the system without the need for approximation, modeling uncertainties, and input disturbances. Control strategies based on a linearized model are often inadequate in meeting precise performance requirements. To cope with these challenges, one must consider nonlinear techniques. Nonlinear control systems provide tools and methodologies for enabling the design and realization of (semi)autonomous vehicle and continuum robots with extended specifications based on the operational mission profiles. This dissertation provides an insight into various nonlinear controllers developed for (semi)autonomous vehicles and continuum robots as a guideline for future applications in the automobile and soft robotics field. A comprehensive assessment of the approaches and control strategies, as well as insight into the future areas of research in this field, are presented.First, two vehicle haptic interfaces, including a robotic grip and a joystick, both of which are accompanied by nonlinear sliding mode control, have been developed and studied on a steer-by-wire platform integrated with a virtual reality driving environment. An operator-in-the-loop evaluation that included 30 human test subjects was used to investigate these haptic steering interfaces over a prescribed series of driving maneuvers through real time data logging and post-test questionnaires. A conventional steering wheel with a robust sliding mode controller was used for all the driving events for comparison. Test subjects operated these interfaces for a given track comprised of a double lane-change maneuver and a country road driving event. Subjective and objective results demonstrate that the driver’s experience can be enhanced up to 75.3% with a robotic steering input when compared to the traditional steering wheel during extreme maneuvers such as high-speed driving and sharp turn (e.g., hairpin turn) passing. Second, a cellphone-inspired portable human-machine-interface (HMI) that incorporated the directional control of the vehicle as well as the brake and throttle functionality into a single holistic device will be presented. A nonlinear adaptive control technique and an optimal control approach based on driver intent were also proposed to accompany the mechatronic system for combined longitudinal and lateral vehicle guidance. Assisting the disabled drivers by excluding extensive arm and leg movements ergonomically, the device has been tested in a driving simulator platform. Human test subjects evaluated the mechatronic system with various control configurations through obstacle avoidance and city road driving test, and a conventional set of steering wheel and pedals were also utilized for comparison. Subjective and objective results from the tests demonstrate that the mobile driving interface with the proposed control scheme can enhance the driver’s performance by up to 55.8% when compared to the traditional driving system during aggressive maneuvers. The system’s superior performance during certain vehicle maneuvers and approval received from the participants demonstrated its potential as an alternative driving adaptation for disabled drivers. Third, a novel strategy is designed for trajectory control of a multi-section continuum robot in three-dimensional space to achieve accurate orientation, curvature, and section length tracking. The formulation connects the continuum manipulator dynamic behavior to a virtual discrete-jointed robot whose degrees of freedom are directly mapped to those of a continuum robot section under the hypothesis of constant curvature. Based on this connection, a computed torque control architecture is developed for the virtual robot, for which inverse kinematics and dynamic equations are constructed and exploited, with appropriate transformations developed for implementation on the continuum robot. The control algorithm is validated in a realistic simulation and implemented on a six degree-of-freedom two-section OctArm continuum manipulator. Both simulation and experimental results show that the proposed method could manage simultaneous extension/contraction, bending, and torsion actions on multi-section continuum robots with decent tracking performance (e.g. steady state arc length and curvature tracking error of 3.3mm and 130mm-1, respectively). Last, semi-autonomous vehicles equipped with assistive control systems may experience degraded lateral behaviors when aggressive driver steering commands compete with high levels of autonomy. This challenge can be mitigated with effective operator intent recognition, which can configure automated systems in context-specific situations where the driver intends to perform a steering maneuver. In this article, an ensemble learning-based driver intent recognition strategy has been developed. A nonlinear model predictive control algorithm has been designed and implemented to generate haptic feedback for lateral vehicle guidance, assisting the drivers in accomplishing their intended action. To validate the framework, operator-in-the-loop testing with 30 human subjects was conducted on a steer-by-wire platform with a virtual reality driving environment. The roadway scenarios included lane change, obstacle avoidance, intersection turns, and highway exit. The automated system with learning-based driver intent recognition was compared to both the automated system with a finite state machine-based driver intent estimator and the automated system without any driver intent prediction for all driving events. Test results demonstrate that semi-autonomous vehicle performance can be enhanced by up to 74.1% with a learning-based intent predictor. The proposed holistic framework that integrates human intelligence, machine learning algorithms, and vehicle control can help solve the driver-system conflict problem leading to safer vehicle operations

Pre-Deployment Testing of Low Speed, Urban Road Autonomous Driving in a Simulated Environment

Low speed autonomous shuttles emulating SAE Level L4 automated driving using human driver assisted autonomy have been operating in geo-fenced areas in several cities in the US and the rest of the world. These autonomous vehicles (AV) are operated by small to mid-sized technology companies that do not have the resources of automotive OEMs for carrying out exhaustive, comprehensive testing of their AV technology solutions before public road deployment. Due to the low speed of operation and hence not operating on roads containing highways, the base vehicles of these AV shuttles are not required to go through rigorous certification tests. The way the driver assisted AV technology is tested and allowed for public road deployment is continuously evolving but is not standardized and shows differences between the different states where these vehicles operate. Currently, AVs and AV shuttles deployed on public roads are using these deployments for testing and improving their technology. However, this is not the right approach. Safe and extensive testing in a lab and controlled test environment including Model-in-the-Loop (MiL), Hardware-in-the-Loop (HiL) and Autonomous-Vehicle-in-the-Loop (AViL) testing should be the prerequisite to such public road deployments. This paper presents three dimensional virtual modeling of an AV shuttle deployment site and simulation testing in this virtual environment. We have two deployment sites in Columbus of these AV shuttles through the Department of Transportation funded Smart City Challenge project named Smart Columbus. The Linden residential area AV shuttle deployment site of Smart Columbus is used as the specific example for illustrating the AV testing method proposed in this paper

Energy-Optimal Control of Over-Actuated Systems - with Application to a Hybrid Feed Drive

Over-actuated (or input-redundant) systems are characterized by the use of more actuators than the degrees of freedom to be controlled. They are widely used in modern mechanical systems to satisfy various control requirements, such as precision, motion range, fault tolerance, and energy efficiency. This thesis is particularly motivated by an over-actuated hybrid feed drive (HFD) which combines two complementary actuators with the aim to reduce energy consumption without sacrificing positioning accuracy in precision manufacturing. This work addresses the control challenges in achieving energy optimality without sacrificing control performance in so-called weakly input-redundant systems, which characterize the HFD and most other over-actuated systems used in practice. Using calculus of variations, an optimal control ratio/subspace is derived to specify the optimal relationship among the redundant actuators irrespective of external disturbances, leading to a new technique termed optimal control subspace-based (OCS) control allocation. It is shown that the optimal control ratio/subspace is non-causal; accordingly, a causal approximation is proposed and employed in energy-efficient structured controller design for the HFD. Moreover, the concept of control proxy is proposed as an accurate causal measurement of the deviation from the optimal control ratio/subspace. The proxy enables control allocation for weakly redundant systems to be converted into regulation problems, which can be tackled using standard controller design methodologies. Compared to an existing allocation technique, proxy-based control allocation is shown to dynamically allocate control efforts optimally without sacrificing control performance. The relationship between the proposed OCS control allocation and the traditional linear quadratic control approach is discussed for weakly input redundant systems. The two approaches are shown to be equivalent given perfect knowledge of disturbances; however, the OCS control allocation approach is shown to be more desirable for practical applications like the HFD, where disturbances are typically unknown. The OCS control allocation approach is validated in simulations and machining experiments on the HFD; significant reductions in control energy without sacrificing positioning accuracy are achieved.PHDMechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/146104/1/molong_1.pd

Systematization of integrated motion control of ground vehicles

This paper gives an extended analysis of automotive control systems as components of the integrated motion control (IMC). The cooperation of various chassis and powertrain systems is discussed from a viewpoint of improvement of vehicle performance in relation to longitudinal, lateral, and vertical motion dynamics. The classification of IMC systems is proposed. Particular attention is placed on the architecture and methods of subsystems integration

Sliding Mode Observer Based Controller for Active Steering Control

The purpose of this paper is to enhance the performance of steering control of a vehicle. A nonlinear sliding mode observer based active steering controller that will overcome the disturbances such as road condition and crosswind is proposed. Condition of stability is given by using Lyapunov stability theory that relates to sliding mode characteristics. The controller proves that it is able to stabilize the steering wheel better when disturbances such as braking action and crosswind are included in the system. Lastly, simulations are given to prove the validity of the controller stability. In the simulations, comparisons are made between the outcome of the uncontrolled, Linear Quadratic Regulator (LQR), Sliding Mode Controller (SMC) and Sliding Mode Observer Based Controller (SMOC)

Kinisi: A Platform for Autonomizing Off-Road Vehicles

This project proposed a modular system that would autonomize off-road vehicles while retaining full manual operability. This MQP team designed and developed a Level 3 autonomous vehicle prototype using an SAE Baja vehicle outfitted with actuators and exteroceptive sensors. At the end of the project, the vehicle had a drive-by-wire system, could localize itself using sensors, generate a map of its surroundings, and plan a path to follow a desired trajectory. Given a map, the vehicle could traverse a series of obstacles in an enclosed environment. The long- term goal is to alter the software system to make it modular and operate in real-time, so the vehicle can autonomously navigate off-road terrain to rescue and aid a distressed individual

A systematic approach to cooperative driving systems based on optimal control allocation

This dissertation proposes a systematic approach to vehicle dynamic control, where interaction between the human driver and on-board automated driving systems is considered a fundamental part of the overall control design. The hierarchical control system is to address motion control in three regions. First is normal driving, where the vehicle stays within the linear region of the tyre. Second is limit driving, where the vehicle stays within the nonlinear region of the tyre. Third is over-limit driving, where the driver demands go beyond the tyre force limits. The third case is addressed by a proposed control moderator (CM). The aim is to consider all three cases within a consistent hierarchical chassis control framework. The upper-level of the hierarchical control structure relates to both optimal vehicle control under normal and limit driving, and saturating driver demands for over-limit driving, these corresponding to a fully autonomous controller and driver assistance controller respectively. Model Predictive Control (MPC) is used as the core control technique for path following under normal driving conditions, and a Moderated Particle Reference (MPR) control strategy is proposed for the road departure mitigation during limit and over-limit driving. The MPR model is validated to ensure predictable and stable operation near the friction limits, maintaining controllability for curvature and speed tracking, which effectively limits demands on the vehicle while preserving the control interaction of the driver. In the next level of the hierarchical control structure, a novel control allocation (CA) approach based on pseudo-inverse method is proposed, while a general linearly constrained quadratic programming (CQP) approach is considered as a benchmark. From extended simulation experiments, it is found that the proposed Pseudo-Inverse CA (PICA) method can achieve a close match to CQP performance in normal driving conditions. This applies for multiple control targets (including path tracking, energy-efficient, etc.) and PICA is found to achieve improved performance in limit and over-limit driving, again addressing multiple control targets (including road departure mitigation, energyefficient, etc.). Furthermore, the PICA method shows its inherent advantages of achieving the same control performance with much less computational cost and is guaranteed to provide a feasible control target for the actuators to track during the highly dynamic driving scenarios. In addition, it can effectively solve the constrained optimal control problem with additional mechanical and electronic actuator constraints. Thus, the proposed PICA method, which uses Control Re-Allocation (making multiple calls to the pseudo-inverse operator) can be considered a feasible and novel alternative approach to control allocation, with advantages over the standard CQP method. Finally, in the lower-level of the hierarchical control structure, the desired tyre control variables are obtained through an analytical inverse tyre model and a sliding mode controller (SMC) is employed for the actuators to track the control target. The proposed hierarchical control system is validated with both driving simulator studies and from testing a real vehicle, considering a wide range of driving scenarios, from low-speed path tracking to safety-critical vehicle dynamic control. It therefore opens up a systematic approach to extended vehicle control applications, from fully autonomous driving to driver assistance systems and control objects from passenger cars to vehicles with higher centre of gravity (CoG) like SUVs, trucks and etc. . .

A gain scheduled robust linear quadratic regulator for vehicle direct yaw moment control

Yaw moment control systems improve vehicle stability and handling in severe driving manoeuvres. Nevertheless, the control system performance is limited by the unmodelled dynamics and parameter uncertainties. To guarantee robustness of the control system against system uncertainties, this paper proposes a gain scheduling Robust Linear Quadratic Regulator (RLQR), in which an extra control term is added to the feedback of a conventional LQR to limit the closed-loop tracking error in a neighbourhood of the origin of its state-space, despite of the uncertainties and persistent disturbances acting on the plant. In addition, the intrinsic parameter-varying nature of the vehicle dynamics model with respect to the longitudinal vehicle velocity can jeopardize the closed-loop performance of fixed-gain control algorithms in different driving conditions. Therefore, the control gains optimally vary based on the actual longitudinal vehicle velocity to adapt the closed-loop system to the variations of this parameter. The effectiveness of the proposed RLQR in improving the robustness of classical LQR against model uncertainties and parameter variations is proven analytically, numerically and experimentally. The numerical and experimental results are consistent with the analytical analysis proving that the proposed RLQR reduces the ultimate bound of error dynamics