Model predictive control for stochastic systems by randomized algorithms

The main topic of this thesis is control of dynamic systems that are subject to stochastic disturbances and constraints on the input and the state. The main motivation for dealing with control of such systems is that there is no method available that adequately deals with this problem, despite the fact that stochastic, constrained systems are often encountered in real world problems. For example, in process industry the margins of physical quantities such as temperature, pressure, concentration, velocity and position can be expressed as amplitude constraints in a natural way. Such constraints are usually persistent in that suitable control actions need to be implemented that respect these constraints irrespective of the presence of uncontrolled disturbances that effect the system. Goals of the thesis are to 1. Formulate a mathematical problem for the synthesis of a controller that will achieve desired performance of the controlled system. More precisely, to minimize a performance measure that captures desired performance while respecting constraints in the face of stochastic disturbances. 2. Deduce verifiable conditions under which the problem formulated in 1. is solvable. 3. Formulate a solution concept for the problem in 1. that is based on the model predictive control technique. 4. Create feasible computational algorithms for the synthesis of controllers that solve control problems from 1. within the solution setup from 3. 5. Investigate convergence properties of the approximate solutions obtained by computational algorithms from 4. The main tool that is used in the thesis to solve the problem formulated in 1. is the model predictive control technique. Model predictive control has had a significant and widespread impact on industrial process control. When dealing with stochastic systems, however, application of the standard model predictive control algorithms results in a significant loss in the controlled system performance. Therefore, to deal with the problem 1. within the model predictive control framework, it was necessary to develop alternative model predictive control techniques. Contributions of the thesis are twofold. The first set of contributions is made with regard to the model predictive control of constrained, stochastic systems. In this thesis, we develop a novel approach to the model predictive control of such systems, that is based on the optimization in closed loop over the control horizon and stochastic sampling of the disturbance i.e. a randomized algorithm. The second set of contributions has been made in more general framework of the optimal control of stochastic systems that are subject to input and state constraints. We present a novel problem setup for control of such systems and give initial results that are concerned with solvability conditions for the posed optimization problem and the characterization of the optimal solution

A Synthesis Method for Automatic Handling of Inter-patient Variability in Closed-loop Anesthesia

This paper presents a convex-optimization-based technique to obtain parameters for a PID feedback controller, used to control the infusion rate of the anesthetic drug propofol. The controller design is based on a set of identified patient models, relating propofol infusion to an EEG-based conciousness index. The main contribution lies in the method automatically taking inter-patient variability into account, i.e., it guarantees robustness (sensitivity peak) and performance (disturbance rejection) over a set of patient models, without the need for manual intervention. The method is demonstrated using a clinically relevant design example. A controller designed using the proposed method is currently scheduled for clinical evaluation

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

Design, Fabrication, and Control of an Upper Arm Exoskeleton Assistive Robot

Stroke is the primary cause of permanent impairment and neurological damage in the United States and Europe. Annually, about fifteen million individuals worldwide suffer from stroke, which kills about one third of them. For many years, it was believed that major recovery can be achieved only in the first six months after a stroke. More recent research has demonstrated that even many years after a stroke, significant improvement is not out of reach. However, economic pressures, the aging population, and lack of specialists and available human resources can interrupt therapy, which impedes full recovery of patients after being discharged from hospital following initial rehabilitation. Robotic devices, and in particular portable robots that provide rehabilitation therapy at home and in clinics, are a novel way not only to optimize the cost of therapy but also to let more patients benefit from rehabilitation for a longer time. Robots used for such purposes should be smaller, lighter and more affordable than the robots currently used in clinics and hospitals. The common human-machine interaction design criteria such as work envelopes, safety, comfort, adaptability, space limitations, and weight-to-force ratio must still be taken into consideration.;In this work a light, wearable, affordable assistive robot was designed and a controller to assist with an activity of daily life (ADL) was developed. The mechanical design targeted the most vulnerable group of the society to stroke, based on the average size and age of the patients, with adjustability to accommodate a variety of individuals. The novel mechanical design avoids motion singularities and provides a large workspace for various ADLs. Unlike similar exoskeleton robots, the actuators are placed on the patient\u27s torso and the force is transmitted through a Bowden cable mechanism. Since the actuators\u27 mass does not affect the motion of the upper extremities, the robot can be more agile and more powerful. A compact novel actuation method with high power-to-weight ratio called the twisted string actuation method was used. Part of the research involved selection and testing of several string compositions and configurations to compare their suitability and to characterize their performance. Feedback sensor count and type have been carefully considered to keep the cost of the system as low as possible. A master-slave controller was designed and its performance in tracking the targeted ADL trajectory was evaluated for one degree of freedom (DOF). An outline for proposed future research will be presented

A COLLISION AVOIDANCE SYSTEM FOR AUTONOMOUS UNDERWATER VEHICLES

The work in this thesis is concerned with the development of a novel and practical collision avoidance system for autonomous underwater vehicles (AUVs). Synergistically, advanced stochastic motion planning methods, dynamics quantisation approaches, multivariable tracking controller designs, sonar data processing and workspace representation, are combined to enhance significantly the survivability of modern AUVs. The recent proliferation of autonomous AUV deployments for various missions such as seafloor surveying, scientific data gathering and mine hunting has demanded a substantial increase in vehicle autonomy. One matching requirement of such missions is to allow all the AUV to navigate safely in a dynamic and unstructured environment. Therefore, it is vital that a robust and effective collision avoidance system should be forthcoming in order to preserve the structural integrity of the vehicle whilst simultaneously increasing its autonomy. This thesis not only provides a holistic framework but also an arsenal of computational techniques in the design of a collision avoidance system for AUVs. The design of an obstacle avoidance system is first addressed. The core paradigm is the application of the Rapidly-exploring Random Tree (RRT) algorithm and the newly developed version for use as a motion planning tool. Later, this technique is merged with the Manoeuvre Automaton (MA) representation to address the inherent disadvantages of the RRT. A novel multi-node version which can also address time varying final state is suggested. Clearly, the reference trajectory generated by the aforementioned embedded planner must be tracked. Hence, the feasibility of employing the linear quadratic regulator (LQG) and the nonlinear kinematic based state-dependent Ricatti equation (SDRE) controller as trajectory trackers are explored. The obstacle detection module, which comprises of sonar processing and workspace representation submodules, is developed and tested on actual sonar data acquired in a sea-trial via a prototype forward looking sonar (AT500). The sonar processing techniques applied are fundamentally derived from the image processing perspective. Likewise, a novel occupancy grid using nonlinear function is proposed for the workspace representation of the AUV. Results are presented that demonstrate the ability of an AUV to navigate a complex environment. To the author's knowledge, it is the first time the above newly developed methodologies have been applied to an A UV collision avoidance system, and, therefore, it is considered that the work constitutes a contribution of knowledge in this area of work.J&S MARINE LT

Modeling and Control of Server-based Systems

When deploying networked computing-based applications, proper resource management of the server-side resources is essential for maintaining quality of service and cost efficiency. The work presented in this thesis is based on six papers, all investigating problems that relate to resource management of server-based systems. Using a queueing system approach we model the performance of a database system being subjected to write-heavy traffic. We then evaluate the model using simulations and validate that it accurately mimics the behavior of a real test bed. In collaboration with Ericsson we model and design a per-request admission control scheme for a Mobile Service Support System (MSS). The model is then validated and the control scheme is evaluated in a test bed. Also, we investigate the feasibility to estimate the state of a server in an MSS using an event-based Extended Kalman Filter. In the brownout paradigm of server resource management, the amount of work required to serve a client is adjusted to compensate for temporary resource shortages. In this thesis we investigate how to perform load balancing over self-adaptive server instances. The load balancing schemes are evaluated in both simulations and test bed experiments. Further, we investigate how to employ delay-compensated feedback control to automatically adjust the amount of resources to deploy to a cloud application in the presence of a large, stochastic delay. The delay-compensated control scheme is evaluated in simulations and the conclusion is that it can be made fast and responsive compared to an industry-standard solution

From classical absolute stability tests towards a comprehensive robustness analysis

In this thesis, we are concerned with the stability and performance analysis of feedback interconnections comprising a linear (time-invariant) system and an uncertain component subject to external disturbances. Building on the framework of integral quadratic constraints (IQCs), we aim at verifying stability of the interconnection using only coarse information about the input-output behavior of the uncertainty