Modeling Human Control Behavior in Command-following Tasks

Humans interact with a variety of complex dynamic systems on a daily basis. However, they are often the lesser understood component of human-in-the-loop (HITL) systems. In this dissertation, we present the results of two HITL experiments to investigate the control strategies that humans use when performing command-following tasks. The first experiment is designed to investigate the control strategies that humans use to interact with nonlinear dynamic systems. Two groups of human subjects interact with a dynamic system and perform a command-following task. One group interacts with a linear time-invariant (LTI) dynamic system and the other group interacts with a Wiener system, which consists of the same LTI dynamics cascaded with a static output nonlinearity. In the second experiment, we examine the impacts of a relaxed command-following control objective on the control strategies used by humans. Two groups of human subjects interact with the same dynamic system and perform a command-following task; however, the groups have different control objectives. One group\u27s control objective is to follow the reference command as closely as possible at all times, while the other group\u27s control objective is to follow the reference command with some allowable error. We develop and utilize a new subsystem identification (SSID) algorithm to model control behavior of the human subjects participating in these HITL experiments. This SSID algorithm can identify the feedback and feedforward controllers used by human subjects, and is applicable to both linear and nonlinear dynamic systems. The SSID results of the first experiment indicate that adaptive feedforward inversion is the main control strategy used by human subjects for both linear and nonlinear plants. The results of the second experiment suggest that not all the human subjects who are instructed to perform a relaxed command-following task adopt adaptive feedforward inversion as their primary control strategy. The control behavior of those human subjects contains significant nonlinearities, which cannot be captured by a LTI control model. We present a nonlinear feedforward control architecture that can model several aspects of their control behavior

Adaptive and Resilient Flight Control System for a Small Unmanned Aerial System

The main purpose of this paper is to develop an onboard adaptive and robust flight control system that improves control, stability, and survivability of a small unmanned aerial system in off-nominal or out-of-envelope conditions. The aerodynamics of aircraft associated with hazardous and adverse onboard conditions is inherently nonlinear and unsteady. The presented flight control system improves functionalities required to adapt the flight control in the presence of aircraft model uncertainties. The fault tolerant inner loop is enhanced by an adaptive real-time artificial neural network parameter identification to monitor important changes in the aircraft’s dynamics due to nonlinear and unsteady aerodynamics. The real-time artificial neural network parameter identification is done using the sliding mode learning concept and a modified version of the self-adaptive Levenberg algorithm. Numerically estimated stability and control derivatives are obtained by delta-based methods. New nonlinear guidance logic, stable in Lyapunov sense, is developed to guide the aircraft. The designed flight control system has better performance compared to a commercial off-the-shelf autopilot system in guiding and controlling an unmanned air system during a trajectory following

Learning-based Predictive Path Following Control for Nonlinear Systems Under Uncertain Disturbances

Accurate path following is challenging for autonomous robots operating in uncertain environments. Adaptive and predictive control strategies are crucial for a nonlinear robotic system to achieve high-performance path following control. In this paper, we propose a novel learning-based predictive control scheme that couples a high-level model predictive path following controller (MPFC) with a low-level learning-based feedback linearization controller (LB-FBLC) for nonlinear systems under uncertain disturbances. The low-level LB-FBLC utilizes Gaussian Processes to learn the uncertain environmental disturbances online and tracks the reference state accurately with a probabilistic stability guarantee. Meanwhile, the high-level MPFC exploits the linearized system model augmented with a virtual linear path dynamics model to optimize the evolution of path reference targets, and provides the reference states and controls for the low-level LB-FBLC. Simulation results illustrate the effectiveness of the proposed control strategy on a quadrotor path following task under unknown wind disturbances.Comment: 8 pages, 7 figures, accepted for publication in IEEE Robotics and Automation Letters ( Volume: 6, Issue: 2, April 2021

Neuroadaptive Model Following Controller Design for a Nonaffine UAV Model

This paper proposes a new model-following adaptive control design technique for nonlinear systems that are nonaffine in control. The adaptive controller uses online neural networks that guarantee tracking in the presence of unmodeled dynamics and/or parameter uncertainties present in the system model through an online control adaptation procedure. The controller design is carried out in two steps: (i) synthesis of a set of neural networks which capture the unmodeled (neglected) dynamics or model uncertainties due to parametric variations and (ii) synthesis of a controller that drives the state of the actual plant to that of a reference model. This method is tested using a three degree of freedom model of a UAV. Numerical results which demonstrate these features and clearly bring out the potential of the proposed approach are presented in this paper

Adaptive control using variable structure systems.

Adaptive control is employed in control systems required to operate satisfactorily regardless of parameter variations, external disturbances and changes in the environment. A conceptually simple approach to adaptive control is the model reference approach which yields a nonlinear feedback system. In a model reference control system the system output is made to follow the output of a specified model. There are numerous approaches to the design of model reference adaptive control systems (MRAC). In this thesis the theory of variable structure systems (VSS) is studied and applied in the design of MRAC systems. VSS are inherently nonlinear feedback systems which exhibit certain adaptive properties including insensitivity to a range of parameter variations and certain external disturbances when operating in the sliding mode. The application of VSS theory to the problem of adaptive model-following has demonstrated the simplicity of the design. It also ensures the asymptotic stability of the controlled system and provides direct control over the error transient. The notion of system zeros arises naturally when tackling the problem of output model-following control systems. Certain interrelations between VSS, system zeros and the output model following problem have suggested a new method for computing the zeros of linear multivariable square systems. A fundamental operator in VSS is shown to be a projector. The employment of projector theory in the study of VSS provides further insight into their operation. Furthermore new methods for constructing the switching hyperplanes matrix are formulated by utilizing projector theory. The linear control law ensuring output model-following and the necessary order reduction is shown to be identical to the equivalent control encountered in VSS. The control law also decouples the system, assigns arbitrary poles and possesses certain adaptive properties. The extension of VSS theory to output model following systems using output information is also discussed

Co-Optimization of Adaptive Cruise Control and Hybrid Electric Vehicle Energy Management via Model Predictive Mixed Integer Control

In this paper, a model predictive mixed integer control method for BYD Qin Plus DM-i (Dual Model intelligent) plug-in hybrid electric vehicle (PHEV) is proposed for co-optimization to reduce fuel consumption during car following. First, the adaptive cruise control (ACC) model for energy-saving driving is established. Then, a control-oriented energy management strategy (EMS) model considering the clutch engagement and disengagement is constructed. Finally, the co-optimization structure by integrating ACC model and EMS model is created and is converted to the mixed integer nonlinear programming (MINLP). The results show that this modeling method can be applied to EMS based on the model predictive control (MPC) framework and verify that co-optimization can achieve a 5.1$\%$ reduction in fuel consumption compared to sequential optimization with the guarantee of ACC performance

Interval Valued Fuzzy Modeling and Indirect Adaptive Control of Quadrotor

In this paper, a combination of fuzzy clustering estimation and sliding mode control is used to control a quadrotor system, whose mathematical model is complex and has unknown elements, including structure, parameters, and so on. In addition, they may be affected by external environmental disturbances. At first, the nonlinear unknown part of the system is estimated by a fuzzy model, A new method is presented for constructing a Takagi-Sugeno (TS) interval-valued fuzzy model (IVFM) based on inputoutput data of the identified system. Following the construction of the fuzzy model that estimates the unknown part of the quadrotor system, a control and on-line adjusting of the fuzzy modeled part of dynamics is used. In this step, the system model will be estimated in adaptive form so that the dynamic equations can be used in sliding mode control. Finally, the proposed technique is applied, and the simulation results are presented to show the effectiveness of this approach in controlling the quadrotor with unknown nonlinear dynamics.Comment: 25 page