Cooperative control of networked systems (or multi-agent systems) has attracted much attention during the past few years. But most of the existing results focus on first order and second order leaderless consensus problems with linear dynamics. The second part of this dissertation solves a higher-order synchronization problem for cooperative nonlinear systems with an active leader. The communication network considered is a weighted directed graph with fixed topology. Each agent is modeled by a higher-order nonlinear system with the nonlinear dynamics unknown. External unknown disturbances perturb each agent. The leader agent is modeled as a higher-order non-autonomous nonlinear system. It acts as a command generator and can only give commands to a small portion of the networked group. A robust adaptive neural network controller is designed for each agent. Neural network learning algorithms are given such that all nodes ultimately synchronize to the leader node with a small residual error. Moreover, these controllers are totally distributed in the sense that each controller only requires its own information and its neighbors' information. / Receding horizon control (RHC), also called model predictive control (MPC), is a suboptimal control scheme over an infinite horizon that is determined by solving a finite horizon open-loop optimal control problem repeatedly. It has widespread applications in industry. Reinforcement learning (RL) is a computational intelligence method in which an optimal control policy is learned over time by evaluating the performance of suboptimal control policies. In this dissertation it is shown that reinforcement learning techniques can significantly improve the behavior of RHC. Specifically, RL methods are used to add a learning feature to RHC. It is shown that keeping track of the value learned at the previous iteration and using it as the new terminal cost for RHC can overcome traditional strong requirements for RHC stability, such as that the terminal cost be a control Lyapunov function, or that the horizon length be greater than some bound. We propose improved RHC algorithms, called updated terminal cost receding horizon control (UTC-RHC), first in the framework of discrete-time linear systems and then in the framework of continuous-time linear systems. For both cases, we show the uniform exponential stability of the closed-loop system can be guaranteed under very mild conditions. Moreover, unlike RHC, the UTC-RHC control gain approaches the optimal policy associated with the infinite horizon optimal control problem. To show these properties, non-standard Lyapunov functions are introduced for both discrete-time case and continuous-time case. / Two topics of modern control are investigated in this dissertation, namely receding horizon control (RHC) and cooperative control of networked systems. We apply learning techniques to these two topics. Specifically, we incorporate the reinforcement learning concept into the standard receding horizon control, yielding a new RHC algorithm, and relax the stability constraints required for standard RHC. For the second topic, we apply neural adaptive control in synchronization of the networked nonlinear systems and propose distributed robust adaptive controllers such that all nodes synchronize to a leader node. / Zhang, Hongwei. / Adviser: Jie Huang. / Source: Dissertation Abstracts International, Volume: 72-04, Section: B, page: . / Thesis (Ph.D.)--Chinese University of Hong Kong, 2010. / Includes bibliographical references (leaves 99-105). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Electronic reproduction. Ann Arbor, MI : ProQuest Information and Learning Company, [200-] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Abstract also in Chinese.

Pan, Yunpeng. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2009. / Includes bibliographical references (p. 98-107). / Abstract also in Chinese. / Abstract --- p.i / p.iii / Acknowledgement --- p.iv / Chapter 1 --- Introduction --- p.2 / Chapter 1.1 --- Model Predictive Control --- p.2 / Chapter 1.2 --- Neural Networks --- p.3 / Chapter 1.3 --- Existing studies --- p.6 / Chapter 1.4 --- Thesis structure --- p.7 / Chapter 2 --- Two Recurrent Neural Networks Approaches to Linear Model Predictive Control --- p.9 / Chapter 2.1 --- Problem Formulation --- p.9 / Chapter 2.1.1 --- Quadratic Programming Formulation --- p.10 / Chapter 2.1.2 --- Linear Programming Formulation --- p.13 / Chapter 2.2 --- Neural Network Approaches --- p.15 / Chapter 2.2.1 --- Neural Network Model 1 --- p.15 / Chapter 2.2.2 --- Neural Network Model 2 --- p.16 / Chapter 2.2.3 --- Control Scheme --- p.17 / Chapter 2.3 --- Simulation Results --- p.18 / Chapter 3 --- Model Predictive Control for Nonlinear Affine Systems Based on the Simplified Dual Neural Network --- p.22 / Chapter 3.1 --- Problem Formulation --- p.22 / Chapter 3.2 --- A Neural Network Approach --- p.25 / Chapter 3.2.1 --- The Simplified Dual Network --- p.26 / Chapter 3.2.2 --- RNN-based MPC Scheme --- p.28 / Chapter 3.3 --- Simulation Results --- p.28 / Chapter 3.3.1 --- Example 1 --- p.28 / Chapter 3.3.2 --- Example 2 --- p.29 / Chapter 3.3.3 --- Example 3 --- p.33 / Chapter 4 --- Nonlinear Model Predictive Control Using a Recurrent Neural Network --- p.36 / Chapter 4.1 --- Problem Formulation --- p.36 / Chapter 4.2 --- A Recurrent Neural Network Approach --- p.40 / Chapter 4.2.1 --- Neural Network Model --- p.40 / Chapter 4.2.2 --- Learning Algorithm --- p.41 / Chapter 4.2.3 --- Control Scheme --- p.41 / Chapter 4.3 --- Application to Mobile Robot Tracking --- p.42 / Chapter 4.3.1 --- Example 1 --- p.44 / Chapter 4.3/2 --- Example 2 --- p.44 / Chapter 4.3.3 --- Example 3 --- p.46 / Chapter 4.3.4 --- Example 4 --- p.48 / Chapter 5 --- Model Predictive Control of Unknown Nonlinear Dynamic Sys- tems Based on Recurrent Neural Networks --- p.50 / Chapter 5.1 --- MPC System Description --- p.51 / Chapter 5.1.1 --- Model Predictive Control --- p.51 / Chapter 5.1.2 --- Dynamical System Identification --- p.52 / Chapter 5.2 --- Problem Formulation --- p.54 / Chapter 5.3 --- Dynamic Optimization --- p.58 / Chapter 5.3.1 --- The Simplified Dual Neural Network --- p.59 / Chapter 5.3.2 --- A Recursive Learning Algorithm --- p.60 / Chapter 5.3.3 --- Convergence Analysis --- p.61 / Chapter 5.4 --- RNN-based MPC Scheme --- p.65 / Chapter 5.5 --- Simulation Results --- p.67 / Chapter 5.5.1 --- Example 1 --- p.67 / Chapter 5.5.2 --- Example 2 --- p.68 / Chapter 5.5.3 --- Example 3 --- p.76 / Chapter 6 --- Model Predictive Control for Systems With Bounded Uncertainties Using a Discrete-Time Recurrent Neural Network --- p.81 / Chapter 6.1 --- Problem Formulation --- p.82 / Chapter 6.1.1 --- Process Model --- p.82 / Chapter 6.1.2 --- Robust. MPC Design --- p.82 / Chapter 6.2 --- Recurrent Neural Network Approach --- p.86 / Chapter 6.2.1 --- Neural Network Model --- p.86 / Chapter 6.2.2 --- Convergence Analysis --- p.88 / Chapter 6.2.3 --- Control Scheme --- p.90 / Chapter 6.3 --- Simulation Results --- p.91 / Chapter 7 --- Summary and future works --- p.95 / Chapter 7.1 --- Summary --- p.95 / Chapter 7.2 --- Future works --- p.96 / Bibliography --- p.97

Process control has been playing an increasingly important role in many industrial applications as an effective way to improve product quality, process costeffectiveness and safety. Simple linear dynamic models are used extensively in process control practice, but they are limited to the type of process behavior they can approximate. It is well-documented that simple nonlinear models can often provide much better approximations to process dynamics than linear models. It is evident that there is a potential of significant improvement of control quality through the implementation of the model-based control procedures. However, such control applications are still not widely implemented because mathematical process models in model-based control could be very difficult and expensive to obtain due to the complexity of those systems and poor understanding of the underlying physics. The main objective of this thesis is to develop new approaches to modeling and control of nonlinear processes. In this thesis, the multivariable nonlinear processes are approximated using a model with a static nonlinearity and a linear dynamics. In particular, the Hammerstein model structure, where the nonlinearity is on the input, is used. Cardinal spline functions are used to identify the multivariable input nonlinearity. Highlycoupled nonlinearity can also be identified due to flexibility and versatility of cardinal spline functions. An approach that can be used to identify both the nonlinearity and linear dynamics in a single step has been developed. The condition of persistent excitation has also been derived. Nonlinear control design approaches for the above models are then developed in this thesis based on: (1) a nonlinear compensator; (2) the extended internal model control (IMC); and (3) the model predictive control (MPC) framework. The concept of passivity is used to guarantee the stability of the closed-loop system of each of the approaches. In the nonlinear compensator approach, the passivity of the process is recovered using an appropriate static nonlinearity. The non-passive linear system is passified using a feedforward system, so that the passified overall system can be stabilized by a passive linear controller with the nonlinear compensator. In the extended IMC approach, dynamic inverses are used for both the input nonlinearity and linear dynamics. The concept of passive systems and the passivity-based stability conditions are used to obtain the invertible approximations of the subsystems and guarantee the stability of the nonlinear closed-loop system. In the MPC approach, a numerical inverse is implemented. The condition for which the numerical inversion is guaranteed to converge is derived. Based on these conditions, the input space in which the numerical inverse can be obtained is identified. This constitutes new constraints on the input space, in addition to the physical input constraints. The total input constraints are transformed into linear input constraints using polytopic descriptions and incorporated in the MPC design.

Model Predictive Control (MPC) refers to a class of algorithms that optimize the future behavior of the plant subject to operational constraints [46]. The merits of the class algorithms include its ability to handle imposed hard constraints on the system and perform on-line optimization. This thesis investigates design and implementation of continuous time model predictive control using Laguerre polynomials and extends the design ap- proaches proposed in [43] to include intermittent predictive control, as well as to include the case of the nonlinear predictive control. In the Intermittent Predictive Control, the Laguerre functions are used to describe the control trajectories between two sample points to save the com- putational time and make the implementation feasible in the situation of the fast sampling of a dynamic system. In the nonlinear predictive control, the Laguerre polynomials are used to describe the trajectories of the nonlinear control signals so that the reced- ing horizon control principle are applied in the design with respect to the nonlinear system constraints. In addition, the thesis reviews several Quadratic Programming methods and compares their performances in the implementation of the predictive control. The thesis also presents simulation results of predictive control of the autonomous underwater vehicle and the water tank.

<p>The power to mass ratio of a heavy truck causes even moderate slopes to have a significant influence on the motion. The velocity will inevitable vary within an interval that is primarily determined by the ratio and the road topography. If further variations are actuated by a controller, there is a potential to lower the fuel consumption by taking the upcoming topography into account. This possibility is explored through theoretical and simulation studies as well as experiments in this work.</p><p>Look-ahead control is a predictive strategy that repeatedly solves an optimization problem online by means of a tailored dynamic programming algorithm. The scenario in this work is a drive mission for a heavy diesel truck where the route is known. It is assumed that there is road data on-board and that the current heading is known. A look-ahead controller is then developed to minimize fuel consumption and trip time.</p><p>The look-ahead control is realized and evaluated in a demonstrator vehicle and further studied in simulations. In the prototype demonstration, information about the road slope ahead is extracted from an on-board database in combination with a GPS unit. The algorithm calculates the optimal velocity trajectory online and feeds the conventional cruise controller with new set points. The results from the experiments and simulations confirm that look-ahead control reduces the fuel consumption without increasing the travel time. Also, the number of gear shifts is reduced. Drivers and passengers that have participated in tests and demonstrations have perceived the vehicle behavior as comfortable and natural.</p> / Report code: LIU-TEK-LIC-2007:28.

A common problem encountered in mobile hydraulics is the desire to automate motion control functions in a restricted-cost and restricted-sensor environment. In this thesis a solution to this problem is presented. A velocity control scheme based on a novel single component pressure compensated ow controller was developed and evaluated. <p> The development of the controller involved solving several distinct technical challenges. First, a model reference control scheme was developed to provide control of the valve spool displacement for a particular electrohydraulic proportional valve. The control scheme had the effect of desensitizing the transient behaviour of the valve dynamics to changes in operating condition. Next, the pressure/flow relationship of the same valve was examined. A general approach for the mathematical characterization of this relationship was developed. This method was based on a modification of the so-called turbulent orifice equation. The general approach included a self-tuning algorithm. Next, the modified turbulent orifice equation was applied in conjunction with the model reference valve controller to create a single component pressure compensated flow control device. This required an inverse solution to the modified orifice equation. Finally, the kinematics of a specific single link hydraulically actuated mechanism were solved. Integration of the kinematic solution with the flow control device allowed for predictive velocity control of the single link mechanism.

A common problem encountered in mobile hydraulics is the desire to automate motion control functions in a restricted-cost and restricted-sensor environment. In this thesis a solution to this problem is presented. A velocity control scheme based on a novel single component pressure compensated ow controller was developed and evaluated. <p> The development of the controller involved solving several distinct technical challenges. First, a model reference control scheme was developed to provide control of the valve spool displacement for a particular electrohydraulic proportional valve. The control scheme had the effect of desensitizing the transient behaviour of the valve dynamics to changes in operating condition. Next, the pressure/flow relationship of the same valve was examined. A general approach for the mathematical characterization of this relationship was developed. This method was based on a modification of the so-called turbulent orifice equation. The general approach included a self-tuning algorithm. Next, the modified turbulent orifice equation was applied in conjunction with the model reference valve controller to create a single component pressure compensated flow control device. This required an inverse solution to the modified orifice equation. Finally, the kinematics of a specific single link hydraulically actuated mechanism were solved. Integration of the kinematic solution with the flow control device allowed for predictive velocity control of the single link mechanism.

Repetitive Model Predictive Control (RMPC) incorporates the idea of Repetitive Control (RC) into Model Predictive Control (MPC) to take full advantage of the constraint handling, multivariable control features of MPC in periodic processes. The RMPC achieves perfect asymptotic tracking/rejection in periodic processes, provided that the period length used in the control formulation matches the actual period of the reference/disturbance exactly. Even a small mismatch between the actual period of process and the controller period can deteriorate the RMPC performance significantly. The period mismatch occurs either from an inaccurate estimation of actual frequency of disturbance due to resolution limit or from trying to force the controller period to be an integer multiple of sampling time. An extension of RMPC called Robust Repetitive Model Predictive Control (R-RMPC) is proposed for such cases where period length cannot be predetermined accurately, or where period is not an integer multiple of sampling time. This robust RMPC borrows the idea of using weighted, multiple memory loops in RC for robustness enhancement. The modified RMPC is more robust in the sense that small changes in period length do not diminish the tracking/rejection properties by much. Simulation results show that R-RMPC achieves significant improvement over the standard RMPC in case of a slight period mismatch. The effectiveness of this Robust RMPC is demonstrated by applying it to a mechanical motion tracking machine whose function is to follow a constant trajectory while rejecting periodic disturbances of an uncertain period.

Thesis (Ph. D.)--University of Florida, 2001. / Title from first page of PDF file. Document formatted into pages; contains viii, 118 p.; also contains graphics. Vita. Includes bibliographical references (p. 115-117).

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