Differential Dynamic Programming for time-delayed systems

Trajectory optimization considers the problem of deciding how to control a dynamical system to move along a trajectory which minimizes some cost function. Differential Dynamic Programming (DDP) is an optimal control method which utilizes a second-order approximation of the problem to find the control. It is fast enough to allow real-time control and has been shown to work well for trajectory optimization in robotic systems. Here we extend classic DDP to systems with multiple time-delays in the state. Being able to find optimal trajectories for time-delayed systems with DDP opens up the possibility to use richer models for system identification and control, including recurrent neural networks with multiple timesteps in the state. We demonstrate the algorithm on a two-tank continuous stirred tank reactor. We also demonstrate the algorithm on a recurrent neural network trained to model an inverted pendulum with position information only.Comment: 7 pages, 6 figures, conference, Decision and Control (CDC), 2016 IEEE 55th Conference o

Robust Control Design of Gain-scheduled Controllers for Nonlinear Processes

In the chemical or biochemical industry most processes are modeled by nonlinear equations. It is of a great significance to design high-performance nonlinear controllers for efficient control of these nonlinear processes to achieve closed-loop system's stability and high performance. However, there are many difficulties which hinder the design of such controllers due mainly to the process nonlinearity. In this work, comprehensive design procedures based on robust control have been proposed to efficiently deal with the design of gain-scheduled controllers for nonlinear systems. Since all the design procedures proposed in this work rely strongly on the process model, the first difficulty addressed in this thesis is the identification of a relatively simple model of the nonlinear processes under study. The nonlinearity of the processes makes it often difficult to obtain a first-principles model which can be used for analysis and design of the controller. As a result, relatively simple empirical models, Volterra series model and state-affine model, are chosen in this work to represent the nonlinear process for the design of controllers. The second major difficulty is that although the nonlinear models used in this thesis are easy to identify, the analysis of stability and performance for such models using nonlinear control theory is not straightforward. Instead, it is proposed in this study to investigate the stability and performance using a robust control approach. In this approach, the nonlinear model is approximated by a nominal linear model combined with a mathematical description of model error to be referred to, in this work, as model uncertainty. In the current work it was assumed that the main source of uncertainty with respect to the nominal linear model is due to the system nonlinearity. Then, in this study, robust control theoretical tools have been especially developed and applied for the design of gain-scheduled Proportional-Integral (PI) control and gain-scheduled Model Predictive Control (MPC). Gain-scheduled controllers are chosen because for nonlinear processes operated over a wide range of operation, gain-scheduling has proven to be a successful control design technique (Bequette, 1997) for nonlinear processes. To guarantee the closed-loop system's robust stability and performance with the designed controllers, a systematic approach has been proposed for the design of robust gain-scheduled controllers for nonlinear processes. The design procedure is based on robust stability and performance conditions proposed in this work. For time-varying uncertain parameters, robust stability and performance conditions using fixed Lyapunov functions and parameter-dependent Lyapunov functions, were used. Then, comprehensive procedures for the design and optimization of robust gain-scheduled PI and MPC controllers tuning parameters based on the robust stability and performance tests are then proposed. Since the closed-loop system represented by the combination of a state-affine process model and the gain-scheduled controller is found to have an affine dependence on the uncertain parameters, robust stability and performance conditions can be tested by a finite number of Linear Matrix Inequalities (LMIs). Thus, the final problems are numerically solvable. One of the inherent problems with robust control is that the design is conservative. Two approaches have been proposed in this work to reduce the conservatism. The first one is based on parameter-dependent Lyapunov functions, and it is applied when the rate of change of the time-varying uncertainty parameters is a priori available. The second one is based on the relaxation of an input-saturation factor defined in the thesis to deal with the issue of actuator saturation. Finally, to illustrate the techniques discussed in the thesis, robust gain-scheduled PI and MPC controllers are designed for a continuous stirred tank reactor (CSTR) process. A simple MIMO example with two inputs and two outputs controlled by a multivariable gain-scheduled MPC controller is also discussed to illustrate the applicability of the methods to multivariable situations. All the designed controllers are simulated and the simulations show that the proposed design procedures are efficient in designing and comparing robust gain-scheduled controllers for nonlinear processes

Modeling of real pH Neutralization Process using Multiple Neural Networks (MNN) Combination Technique.

Combining multiple neural networks appears to be a very promising approach in improving neural network generalisation since it is very difficult, if not impossible, to develop a perfect single neural network (SNN) especially when dealing with a real time data

Development and integration of Honeywell’s One-Wireless network

The purpose of this project has been to develop upon the Honeywell One-Wireless network in the Murdoch University Pilot Plant and integrate it into the Distributed Control System. This will give future students exposure to developing process control schemes around industrial wireless technology in a small plant setting. Industrial Wireless is still on the cutting edge of technology and it will challenge the status quo in Industry with its many advantages. A brief review of Industrial wireless technology has been included in this thesis report to provide the reader a background to the communications technology. Also included is Honeywell’s One-Wireless Network solution which was used in this project. There, where significant challenges in getting the network operational, and as a result a systematic troubleshooting process was followed. Once the network was operational additional wireless instruments where added to expand the network and set up in the system. From here the One-Wireless network was integrated into the Distributive Control System which operates the pilot plant, this was done using Modbus TCP/IP. To determine the effectiveness of the network a post Radio Frequency assessment was carried out to determine the impact of the network and ensure that it was following best practices. Relevant documentation on the network was developed as a handover for future students to build upon the work carried out

A Study of Application-awareness in Software-defined Data Center Networks

A data center (DC) has been a fundamental infrastructure for academia and industry for many years. Applications in DC have diverse requirements on communication. There are huge demands on data center network (DCN) control frameworks (CFs) for coordinating communication traffic. Simultaneously satisfying all demands is difficult and inefficient using existing traditional network devices and protocols. Recently, the agile software-defined Networking (SDN) is introduced to DCN for speeding up the development of the DCNCF. Application-awareness preserves the application semantics including the collective goals of communications. Previous works have illustrated that application-aware DCNCFs can much more efficiently allocate network resources by explicitly considering applications needs. A transfer application task level application-aware software-defined DCNCF (SDDCNCF) for OpenFlow software-defined DCN (SDDCN) for big data exchange is designed. The SDDCNCF achieves application-aware load balancing, short average transfer application task completion time, and high link utilization. The SDDCNCF is immediately deployable on SDDCN which consists of OpenFlow 1.3 switches. The Big Data Research Integration with Cyberinfrastructure for LSU (BIC-LSU) project adopts the SDDCNCF to construct a 40Gb/s high-speed storage area network to efficiently transfer big data for accelerating big data related researches at Louisiana State University. On the basis of the success of BIC-LSU, a coflow level application-aware SD- DCNCF for OpenFlow-based storage area networks, MinCOF, is designed. MinCOF incorporates all desirable features of existing coflow scheduling and routing frame- works and requires minimal changes on hosts. To avoid the architectural limitation of the OpenFlow SDN implementation, a coflow level application-aware SDDCNCF using fast packet processing library, Coflourish, is designed. Coflourish exploits congestion feedback assistances from SDN switches in the DCN to schedule coflows and can smoothly co-exist with arbitrary applications in a shared DCN. Coflourish is implemented using the fast packet processing library on an SDN switch, Open vSwitch with DPDK. Simulation and experiment results indicate that Coflourish effectively shortens average application completion time

Nonlinear predictive restricted structure control

This thesis defines new developments in predictive restricted structure control for industrial applications. It begins by describing the augmented system for both state-space and polynomial model descriptions. These descriptions can contain the plant model, the disturbance model, and any additional essential model subsystems. It then describes the predictive restricted structure control solution for both linear and nonlinear systems in state-space form. The solution utilizes the recent development in nonlinear predictive generalized minimum variance by adding a general operator subsystem that defines nonlinear system along with the linear or the linear parameter varying output subsystem. The next contribution is the polynomial predictive restricted structure control algorithm for polynomial linear parameter varying model that may result from nonlinear equations or experimental data-driven model identification. This algorithm utilizes the generalised predictive control method to approximate and control nonlinear systems in the linear parameter varying system inputoutput transfer operator matrices. The solution is simple in unconstrained and constrained optimization solutions and required a small computing capacity. Four examples have been chosen to test the algorithms for different nonlinear characteristics. In the first three examples, state-space versions of the algorithm for the linear, the quasi-linear parameter varying and the state-dependent were employed to control the quadruple tank process, electronic throttle body, and the continuous stirred tank reactors. In the last example, the polynomial linear parameter varying restricted structure controller is used to control automotive variable camshaft timing system.This thesis defines new developments in predictive restricted structure control for industrial applications. It begins by describing the augmented system for both state-space and polynomial model descriptions. These descriptions can contain the plant model, the disturbance model, and any additional essential model subsystems. It then describes the predictive restricted structure control solution for both linear and nonlinear systems in state-space form. The solution utilizes the recent development in nonlinear predictive generalized minimum variance by adding a general operator subsystem that defines nonlinear system along with the linear or the linear parameter varying output subsystem. The next contribution is the polynomial predictive restricted structure control algorithm for polynomial linear parameter varying model that may result from nonlinear equations or experimental data-driven model identification. This algorithm utilizes the generalised predictive control method to approximate and control nonlinear systems in the linear parameter varying system inputoutput transfer operator matrices. The solution is simple in unconstrained and constrained optimization solutions and required a small computing capacity. Four examples have been chosen to test the algorithms for different nonlinear characteristics. In the first three examples, state-space versions of the algorithm for the linear, the quasi-linear parameter varying and the state-dependent were employed to control the quadruple tank process, electronic throttle body, and the continuous stirred tank reactors. In the last example, the polynomial linear parameter varying restricted structure controller is used to control automotive variable camshaft timing system

Process Control Applications in Microbial Fuel Cells(MFC)

abstract: Microbial fuel cells(MFC) use micro-organisms called anode-respiring bacteria(ARB) to convert chemical energy into electrical energy. This process can not only treat wastewater but can also produce useful byproduct hydrogen peroxide(H2O2). Process variables like anode potential and pH play important role in the MFC operation and the focus of this dissertation are pH and potential control problems. Most of the adaptive pH control solutions use signal-based-norms as cost functions, but their strong dependency on excitation signal properties makes them sensitive to noise, disturbances, and modeling errors. System-based-norm( H-infinity) cost functions provide a viable alternative for the adaptation as they are less susceptible to the signal properties. Two variants of adaptive pH control algorithms that use approximate H-infinity frequency loop-shaping (FLS) cost metrics are proposed in this dissertation. A pH neutralization process with high retention time is studied using lab scale experiments and the experimental setup is used as a basis to develop a first-principles model. The analysis of such a model shows that only the gain of the process varies significantly with operating conditions and with buffering capacity. Consequently, the adaptation of the controller gain (single parameter) is sufficient to compensate for the variation in process gain and the focus of the proposed algorithms is the adaptation of the PI controller gain. Computer simulations and lab-scale experiments are used to study tracking, disturbance rejection and adaptation performance of these algorithms under different excitation conditions. Results show the proposed algorithm produces optimum that is less dependent on the excitation as compared to a commonly used L2 cost function based algorithm and tracks set-points reasonably well under practical conditions. The proposed direct pH control algorithm is integrated with the combined activated sludge anaerobic digestion model (CASADM) of an MFC and it is shown pH control improves its performance. Analytical grade potentiostats are commonly used in MFC potential control, but, their high cost (>$6000) and large size, make them nonviable for the field usage. This dissertation proposes an alternate low-cost($200) portable potentiostat solution. This potentiostat is tested using a ferricyanide reactor and results show it produces performance close to an analytical grade potentiostat.Dissertation/ThesisDoctoral Dissertation Electrical Engineering 201

An investigation into the merits of fuzzy logic control versus classical control.

A project report submitted to the Faculty of Engineering, University of the Witwatersrand, Johannesburg, in partial fulfilment of the requirements for the degree of Master of Science in Engineering.Up to now the benefits and problems with fuzzy control have not been fully identified and its role in the control domain needs investigation. The past trend has been to show that a fuzzy controller can provide better control than classical control, without examining what is actually being achieved. The aim in this project report is to give a fair comparison between classical and fuzzy control. Robustness, disturbance rejection, noise suppression" nonminimurn phase and dead time are examined for both controllers. The comparison is performed through computer simulation of classical and fuzzy controlled plant models. Fuzzy control has the advantage of non-linear performance and the ability to capture linguistic information. Translating quantitative information into the fuzzy domain is difficult; therefore when the system is easily mathematically modelled and linear, classical control is usually better. Which controller should be used depends on the application, control designer and information available.Andrew Chakane 201

Modeling and control to improve blood glucose concentration for people with diabetes

Diabetes mellitus is a chronical condition that features either the lack of insulin or increased insulin resistance. It is a disorder in the human metabolic system. To combat insufficiency of insulin released by pancreas, a closed-loop control system, also known as artificial pancreas (AP) in this application, have been created to mimic the functionality of a human pancreas. An AP is used to regulate blood glucose concentration (BGC) by managing the release of insulin. Therefore, an algorithm, which can administer insulin to reduce the variation of BGC and minimize the occurrences of hyper-/ hypoglycemia episodes, is the key component of an AP. The objective of the dissertation is to develop an optimal algorithm to better control BGC for people with diabetes. For people with Type 2 diabetes, prevention or treatment of diabetes mellitus can typically be done via a change of lifestyle and weight management. A virtual sensing system that does not require many manual inputs from patients can ease the burden for people with Type 2 diabetes. This dissertation covers the development of a monitoring system for Type 2 diabetes. To achieve the goal of tighter control of BGC for people with Type 1 diabetes, dynamic modeling methodology for capturing the cause-and-effect relationship between manipulated variable (i.e. insulin) and controlled variable (i.e. BGC) has been developed. Theoretically, this dissertation has established that physiologically based nonlinear parameterized wiener models being superior to nonlinear autoregressive moving average with exogenous inputs (NARMAX) models in capturing dynamic relationships in processes with correlated inputs. Based on these results, wiener models have been applied in the modeling of BGC for real subjects with Type 1 diabetes under free-living conditions. With promising results shown in wiener models, an extended physiologically based model (i.e. semi-coupled model) has been developed from wiener structure, which enables the development of a phenomenologically sound feedforward control law. The feedforward control law based on wiener models has been tested in simulated continuous-stirred-tank reactor (CSTR) that demonstrates tight control of controlled variables. Further simulation runs with a CSTR also shows feedforward predictive control (FFPC) can provide tighter control over model predictive control (MPC). Lastly, for the special application of BGC control for people with Type 1 diabetes, FFPC demonstrates tighter control than MPC under simulation environment. To account for unmeasured disturbances and inaccurate models for manipulated variable in real life scenarios, feedback predictive control (FBPC) is developed and proven to be a more effective control algorithm under both CSTR and diabetes simulation environment, which can establish the foundation for tightening BGC in real subject clinical studies

Meta-Modelling of Intensive Computational Models

Engineering process design for applications that use computationally intensive nonlinear dynamical systems can be expensive in time and resources. The presented work reviews the concept of a meta-model as a way to improve the efficiency of this process. The proposed meta-model will have a computational advantage in implementation over the computationally intensive model therefore reducing the time and resources required to design an engineering process. This work proposes to meta-model a computationally intensive nonlinear dynamical system using reduced-order linear parameter varying system modelling approach with local linear models in velocity based linearization form. The parameters of the linear time-varying meta-model are blended using Gaussian Processes regression models. The meta-model structure is transparent and relates directly to the dynamics of the computationally intensive model while the velocity-based local linear models faithfully reproduce the original system dynamics anywhere in the operating space of the system. The non-parametric blending of the meta-model local linear models by Gaussian Processes regression models is ideal to deal with data sparsity and will provide uncertainty information about the meta-model predictions. The proposed meta-model structure has been applied to second-order nonlinear dynamical systems, a small sized nonlinear transmission line model, medium sized fluid dynamics problem and the computationally intensive nonlinear transmission line model of order 5000