Trajectory Tracking Control of an Autonomous Ground Vehicle

This thesis proposes a solution to the problem of making an autonomous nonholonomic ground vehicle track a special trajectory while following a reference velocity profile. The proposed strategies have been analyzed, simulated and eventually implemented and verified in Alice, Team Caltech's contribution to the 2007 DARPA Urban Challenge competition for autonomous vehicles. The system architecture of Alice is reviewed. A kinematic vehicle model is derived. Lateral and longitudinal controllers are proposed and analyzed, with emphasis on the nonlinear state feedback lateral controller. Relevant implementation aspects and contingency management is discussed. Finally, results from simulation and field tests are presented and discussed

On Automation in Anesthesia

The thesis discusses closed-loop control of the hypnotic and the analgesic components of anesthesia. The objective of the work has been to develop a system which independently controls the intravenous infusion rates of the hypnotic drug propofol and analgesic drug remifentanil. The system is designed to track a reference hypnotic depth level, while maintaining adequate analgesia. This is complicated by inter-patient variability in drug sensitivity, disturbances caused foremost by surgical stimulation, and measurement noise. A commercially available monitor is used to measure the hypnotic depth of the patient, while a simple soft sensor estimates the analgesic depth. Both induction and maintenance of anesthesia are closed-loop controlled, using a PID controller for propofol and a P controller for remifentanil. In order to tune the controllers, patient models have been identified from clinical data, with body mass as only biometric parameter. Care has been taken to characterize identifiability and produce models which are safe for the intended application. A scheme for individualizing the controller tuning upon completion of the induction phase of anesthesia is proposed. Practical aspects such as integrator anti-windup and loss of the measurement signal are explicitly addressed. The validity of the performance measures, most commonly reported in closed-loop anesthesia studies, is debated and a new set of measures is proposed. It is shown, both in simulation and clinically, that PID control provides a viable approach. Both results from simulations and clinical trials are presented. These results suggest that closed-loop controlled anesthesia can be provided in a safe and efficient manner, relieving the regulatory and server controller role of the anesthesiologist. However, outlier patient dynamics, unmeasurable disturbances and scenarios which are not considered in the controller synthesis, urge the presence of an anesthesiologist. Closed-loop controlled anesthesia should therefore not be viewed as a replacement of human expertise, but rather as a tool, similar to the cruise controller of a car

The Differential-Algebraic Windkessel Model with Power As Input

The lack of methods to evaluate mechanical function of donated hearts in the context of transplantation imposes large precautionary margins, translating into a low utilization rate of donor organs. This has spawned research into cyber-physical models constituting artificial afterloads (arterial trees), that can serve to evaluate the contractile capacity of the donor heart.The Windkessel model is an established linear time-invariant afterload model, that researchers committed to creating a cyber-physical afterload have used as a template. With aortic volumetric flow as input and aortic pressure as output, it is not directly obvious how a Windkessel model will respond to changes in heart contractility.We transform the classic Windkessel model to relate power, rather than flow, to pressure. This alters the model into a differential-algebraic equation, albeit one that is straightforward to simulate. We then propose a power signal model, that is based on pressure and flow measurements and optimal in a Bayesian sense within the class of C2 signals. Finally, we show how the proposed signal model can be used to create relevant simulation scenarios, and use this to illustrate why it is problematic to use the Windkessel model as a basis for designing a clinically relevant artificial afterload

Actively controlled cardiac afterload

Ex vivo (outside of the body) working heart models enable the evaluation of isolated hearts. They are envisioned to play an important role in increasing the currently low utilization rate of donor hearts for transplantation. For the heart to work in isolation, an afterload (flow impedance) is needed. To date, afterload devices have been constructed by combining multiple constituent elements such as pumps, flow resistances, and flow capacitances (compliances), typically to replicate the structure of so-called Windkessel models. This limits active control to that achievable by varying these elements, making it slow and subject to the problem of dynamic coupling between parameters. Here we present a novel concept to achieve Windkessel dynamics through a very simple variable flow impedance. The impedance is actively controlled using feedback from a pressure measurement. Through simulations we demonstrate the ability to perfectly emulate Windkessel dynamics, while imposing tight pressure limits needed for safe operation—something not achievable with the verbatim implementation using constituent elements

Identifiability of pharmacological models for online individualization

There is a large variability between individuals in the response to anesthetic drugs, that seriously limits the achievable performance of closed-loop controlled drug dosing. Full individualization of patient models based on early clinical response data has been suggested as a means to improve performance with maintained robustness (safety). We use estimation theoretic analysis and realization theory to characterize practical identifiability of the standard pharmacological model structure from anesthetic induction phase data and conclude that such approaches are not practically feasible

Autotuning of an In-Line pH Control System

A novel autotuning procedure is presented through application to an industrial in-line pH control system. The procedure has three advantages over classical relay auto-tuners: experiment duration is very short (no need for limit-cycle convergence); all data is used for identification (instead of only peaks and switch instances); a parameter uncertainty model is identified and utilized for robust controller synthesis

An automatic tuner with short experiment and probabilistic plant parameterization

A novel automatic tuning strategy is proposed. It is based on an experiment of very short duration, followed by simultaneous identification of LTI model parameters and an estimate of their error covariance. The parametric uncertainty model is subsequently exploited to design linear controllers with magnitude bounds on some closed-loop transfer function of interest, such as the sensitivity function. The method is demonstrated through industrially relevant examples. Robustness is enforced through probabilistic constraints on the H∞ norms of the sensitivity function, while minimizing load disturbance integral error (IE) to ensure performance. To demonstrate the strength of the proposed method, identification for the mentioned examples is carried out under a high level of measurement noise

Teaching Control Principles to Industry Practitioners

This paper addresses the need of continued education of process industry practitioners such as operators and instrumentation engineers. The process industry regulatory control tuning situation of today is reviewed. Areas of potential improvement are identified. A course, aimed at fulfilling these needs is presented. Especially, useful laboratory experiments are outlined. The suggested course was given within PICLU – a regional collaboration between academia and process industry in Scandinavia

Pharmacometric covariate modeling using symbolic regression networks

A central challenge within pharmacometrics is to establish a relation between pharmacological model parameters, such as compartment volumes and diffusion rate constants, and known population covariates, such as age and body mass. There is rich literature dedicated to the learning of functional mappings from the covariates to the model parameters, once a search class of functions has been determined. However, the state-of-the-art selection of the search class itself is ad hoc. We demonstrate how neural network-based symbolic regression can be used to simultaneously find the function form and its parameters. The method is put in relation to the literature on symbolic regression and equation learning. A conceptual demonstration is provided through examples, as is a road map to full-scale employment to pharmacological data sets, relevant to closed-loop anesthesia

Nonlinear lateral control strategy for nonholonomic vehicles

This paper proposes an intuitive nonlinear lateral control strategy for trajectory tracking in autonomous nonholonomic vehicles. The controller has been implemented and verified in Alice, Team Caltech's contribution to the 2007 DARPA Urban Challenge competition for autonomous motorcars. A kinematic model is derived. The control law is described and analyzed. Results from simulations and field tests are given and evaluated. Finally, the key features of the proposed controller are reviewed, followed by a discussion of some limitations of the proposed strategy