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In practical multibody systems, various factors such as friction, joint clearances, and external events play a significant role and can greatly influence the optimal design of the system and its controller. This research focuses on the use of gradient-based optimization methods for multibody dynamic systems with the incorporation of joint friction. The dynamic formulation has been derived in using two distinct techniques: Index-1 DAE and the tangent-space formulation in minimal coordinates. It employs a two different approaches for gradient computation: direct sensitivity approach and the adjoint sensitivity approach. After a comprehensive review of different friction models developed over time, the Brown McPhee model is selected as the most suitable due to its accuracy in dynamic simulations and its compatibility with sensitivity analysis. The proposed methodology supports the simultaneous optimization of both the system and its controller. Moreover, the sensitivities obtained using these formulations have been thoroughly validated for numerical accuracy and benchmarked against other friction models that are based on dynamic events for stiction to friction transition. The approach presented is particularly valuable in applications like robotics and servo-mechanical systems where the design and actuation are closely interconnected. To obtain numerical results, a new implementation of the MBSVT (Multi-Body Systems at Virginia Tech) software package, known as MBSVT 2.0, is reprogrammed in Julia and MATLAB to ensure ease of implementation while maintaining high computational efficiency. The research includes multiple case studies that illustrate the advantages of the concurrent optimization of design and control for specific applications. Efficient techniques for control signal parameterization are presented using linear basis functions. A special focus has been made on the computational efficiency of the formulation and various techniques like sparse-matrix algebra and Jacobian-free products have been employed in the implementation. The dissertation concludes with a summary of key results and contributions and the future scope for this research. / Doctor of Philosophy / In simpler terms, this research focuses on improving the design and control of complex mechanical systems, like robots and automotive systems, by considering factors such as friction in the joints. Friction in a system can greatly affect how it performs for the desired task. The research uses a method called gradient-based optimization, which essentially means finding the most optimal parameters of the system and its controller such that they achieve a desired goal in the most optimal way. Before a model for such a system can be developed, various techniques need to be researched for incorporation of friction mathematically. A model known as Brown McPhee friction is one such model suitable for such an analysis. When optimizing any system on a computer, an iterative process needs to be performed which may prove to be very expensive in terms of computational resources required and the time taken to achieve a solution. Hence, proper mathematical and computational techniques need to be employed to ensure that the resources of a computer are utilized in the most efficient way to get the solution is the quickest way possible. Among the various novelties of this research, it is worth noting that this method that allows for simultaneous design and control optimization, which is particularly useful for applications such as robotics and servo-mechanical systems. Considering the design and control together, leads to more efficient and effective systems. The approach is tested using a software package called MBSVT 2.0, which was specifically developed as part of this research. The software is available in 3 languages: Julia, MATLAB and Fortran for universal access to people from various communities. The results from various case studies are presented that demonstrate this simultaneous design and control approach and highlights its effectiveness making the systems more robust and better performing.

Το ενεργειακό πρόβλημα αποτελεί ένα από τα πιο πολυσηζητημένα θέματα της ανθρωπότητας που απασχολεί και θα συνεχίσει να απασχολεί τον πλανήτη μας για τις επόμενες δεκαετίες. Μια λύση στο πρόβλημα της ηλεκτρικής ενέργειας είναι η εγκατάσταση και λειτουργία μικροδικτύων είτε σε οικιακή κλίμακα είτε σε επίπεδο εθνικού δικτύου ή ακόμα και σε επίπεδο μιας ολόκληρης ηπείρου. Για την επιτυχή και ευσταθή λειτουργία τέτοιων συστημάτων, κρίνεται απαραίτητος ο έλεγχος των επιμέρους στοιχείων τους ξεχωριστά. Στην παρούσα διπλωματική εργασία μοντελοποιήθηκαν και ελέγθηκαν δύο ηλεκτρονικοί μετατροπείς ισχύος, ο TCSC και ο BOOST, καθώς και η τριφασική ασύγχρονη μηχανή βραχυχκυκλωμένου κλωβού, κυρίαρχα στοιχεία ενός μικροδικτύου. Τα καταστατικά μοντέλα όμως, τόσο των μετατροπέων ισχύος όσο και της μηχανής περιγράφονται από μη γραμμικές διαφορικές εξισώσεις και συνεπώς ο έλεγχος τους δεν είναι καθόλου εύκολη υπόθεση. Συγκεκριμένα, χρησιμοποιήθηκαν μοντέρνες τεχνικές μη γραμμικού και προσαρμοστικού ελέγχου προκειμένου να ελεγχθούν τα συστήματα αυτά και να εξασφαλιστεί κάθε φορά η ευστάθεια του συστήματος στη μόνιμη κατάσταση λειτουργίας. Για τους μετατροπείς TCSC και BOOST σχεδιάστηκαν απευθείας οι μη γραμμικοί - προσαρμοστικοί ελεγκτές ενώ για την τριφασική ασύγχρονη μηχανή εφαρμόστηκε πρώτα έμμεσος διανυσματικός έλεγχος στο στατό πλαίσιο αναφοράς και στη συνέχεια σχεδιάστηκαν οι ελεγκτές και οι εκτιμητές της ροής, της ροπής και της αντίστασης. Τέλος όλα τα συστήματα προσομοιώθηκαν με τη χρήση του εργαλείου Simulink του Matlab και εξήχθησαν τα συμπεράσματα. / The energy problem is one of the most spoken issues of humanity that concerns and will continue to concern our planet for decades to come. One solution to the electrical energy problem is the installation and operation of microgrids either at household level or at the level of a national network or even at the level of an entire continent. For successful and stable operation of such systems, it is necessary to test their components separately. In this Thesis there have been modeled and tested two electronic power converters, the TCSC and BOOST, and the three-phase asynchronous machine of shortcircuited cage, dominant elements of a microgrid. The constitutive models, however, both the power converters and the machine are described by nonlinear differential equations and, therefore, their control is not at all easy. Specifically, modern techniques of nonlinear and adaptive control were used in order to check these systems so that the stability of the system in the steady state be ensured. For the inverters TCSC and BOOST the nonlinear - adaptive controllers were designed directly, while for the three-phase asynchronous machine there was applied firstly the indirect vector control in the stationary frame of reference and then the controllers and the flow, torque and resistance estimators were designed. Finally, all the systems were simulated using the Matlab Simulink tool and conclusions were drawn.

by John Tak Kuen Koo. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1994. / Includes bibliographical references (leaves 217-[220]). / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Introduction --- p.1 / Chapter 1.2 --- "Robust, Adaptive and Fuzzy Control" --- p.2 / Chapter 1.3 --- Adaptive Fuzzy Control --- p.4 / Chapter 1.4 --- Object of Study --- p.10 / Chapter 1.5 --- Scope of the Thesis --- p.13 / Chapter 2 --- Background on Adaptive Control and Fuzzy Logic Control --- p.17 / Chapter 2.1 --- Adaptive control --- p.17 / Chapter 2.1.1 --- Model reference adaptive systems --- p.20 / Chapter 2.1.2 --- MIT Rule --- p.23 / Chapter 2.1.3 --- Model Reference Adaptive Control (MRAC) --- p.24 / Chapter 2.2 --- Fuzzy Logic Control --- p.33 / Chapter 2.2.1 --- Fuzzy sets and logic --- p.33 / Chapter 2.2.2 --- Fuzzy Relation --- p.40 / Chapter 2.2.3 --- Inference Mechanisms --- p.43 / Chapter 2.2.4 --- Defuzzification --- p.49 / Chapter 3 --- Explicit Form of a Class of Fuzzy Logic Controllers --- p.51 / Chapter 3.1 --- Introduction --- p.51 / Chapter 3.2 --- Construction of a class of fuzzy controller --- p.53 / Chapter 3.3 --- Explicit form of the fuzzy controller --- p.57 / Chapter 3.4 --- Design criteria on the fuzzy controller --- p.65 / Chapter 3.5 --- B-Spline fuzzy controller --- p.68 / Chapter 4 --- Model Reference Adaptive Fuzzy Control (MRAFC) --- p.73 / Chapter 4.1 --- Introduction --- p.73 / Chapter 4.2 --- "Fuzzy Controller, Plant and Reference Model" --- p.75 / Chapter 4.3 --- Derivation of the MRAFC adaptive laws --- p.79 / Chapter 4.4 --- "Extension to the Multi-Input, Multi-Output Case" --- p.84 / Chapter 4.5 --- Simulation --- p.90 / Chapter 5 --- MRAFC on a Class of Nonlinear Systems: Type I --- p.97 / Chapter 5.1 --- Introduction --- p.98 / Chapter 5.2 --- Choice of Controller --- p.99 / Chapter 5.3 --- Derivation of the MRAFC adaptive laws --- p.102 / Chapter 5.4 --- Example: Stabilization of a pendulum --- p.109 / Chapter 6 --- MRAFC on a Class of Nonlinear Systems: Type II --- p.112 / Chapter 6.1 --- Introduction --- p.113 / Chapter 6.2 --- Fuzzy System as Function Approximator --- p.114 / Chapter 6.3 --- Construction of MRAFC for the nonlinear systems --- p.118 / Chapter 6.4 --- Input-Output Linearization --- p.130 / Chapter 6.5 --- MRAFC with Input-Output Linearization --- p.132 / Chapter 6.6 --- Example --- p.136 / Chapter 7 --- Analysis of MRAFC System --- p.140 / Chapter 7.1 --- Averaging technique --- p.140 / Chapter 7.2 --- Parameter convergence --- p.143 / Chapter 7.3 --- Robustness --- p.152 / Chapter 7.4 --- Simulation --- p.157 / Chapter 8 --- Application of MRAFC scheme on Manipulator Control --- p.166 / Chapter 8.1 --- Introduction --- p.166 / Chapter 8.2 --- Robot Manipulator Control --- p.170 / Chapter 8.3 --- MRAFC on Robot Manipulator Control --- p.173 / Chapter 8.3.1 --- Part A: Nonlinear-function feedback fuzzy controller --- p.174 / Chapter 8.3.2 --- Part B: State-feedback fuzzy controller --- p.182 / Chapter 8.4 --- Simulation --- p.186 / Chapter 9 --- Conclusion --- p.199 / Chapter A --- Implementation of MRAFC Scheme with Practical Issues --- p.203 / Chapter A.1 --- Rule Generation by MRAFC scheme --- p.203 / Chapter A.2 --- Implementation Considerations --- p.211 / Chapter A.3 --- MRAFC System Design Procedure --- p.215 / Bibliography --- p.217

Liu Lu. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2005. / Includes bibliographical references (leaves 65-70). / Abstracts in English and Chinese. / Abstract --- p.i / Acknowledgement --- p.ii / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- The Output Regulation Problem --- p.1 / Chapter 1.2 --- Control Design with Unknown High-frequency Gain Sign --- p.3 / Chapter 1.3 --- Contribution of the Thesis --- p.4 / Chapter 1.4 --- Thesis Outline --- p.5 / Chapter 2 --- Global Robust Stabilization of a Class of Nonlinear Systems --- p.6 / Chapter 2.1 --- Introduction --- p.7 / Chapter 2.2 --- Problem Formulation and Preliminaries --- p.8 / Chapter 2.3 --- Main Result --- p.11 / Chapter 2.4 --- An Example --- p.20 / Chapter 2.5 --- Application of Theorem 2.1 --- p.26 / Chapter 2.5.1 --- Chua's Circuit and Control Problem --- p.26 / Chapter 2.5.2 --- Solvability of the Control Problem --- p.28 / Chapter 2.5.3 --- Simulation Results --- p.32 / Chapter 2.5.4 --- Conclusion --- p.33 / Chapter 2.6 --- Conclusion --- p.36 / Chapter 3 --- Global Robust Output Regulation of Nonlinear Systems in Output Feedback Form --- p.39 / Chapter 3.1 --- Introduction --- p.40 / Chapter 3.2 --- Output Regulation Converted to Stabilization --- p.42 / Chapter 3.3 --- Main Result --- p.49 / Chapter 3.4 --- An Example --- p.55 / Chapter 3.5 --- Conclusion --- p.58 / Chapter 4 --- Conclusions --- p.62 / List of Figures --- p.64 / Bibliography --- p.65 / Biography

Chen Zhiyong. / "April 2005." / Thesis (Ph.D.)--Chinese University of Hong Kong, 2005. / Includes bibliographical references (p. 205-215) / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Mode of access: World Wide Web. / Abstracts in English and Chinese.

Ng Chi-fai. / Thesis submitted in: December 2000. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2001. / Includes bibliographical references (leaves 69-73). / Abstracts in English and Chinese. / Abstract --- p.i / Acknowledgments --- p.iii / List of Figures --- p.viii / List of Tables --- p.ix / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Motivation and Objectives --- p.1 / Chapter 1.2 --- Principles of Feedforward Neural Network Approximation --- p.1 / Chapter 1.3 --- Contribution of The Thesis --- p.5 / Chapter 1.4 --- Outline of The Thesis --- p.5 / Chapter 2 --- Feedforward Neural Networks: An Approximator for Nonlinear Control Law --- p.8 / Chapter 2.1 --- Optimization Methods Applied in Feedforward Neural Network Approximation --- p.8 / Chapter 2.2 --- Example in Supervised Learning --- p.10 / Chapter 2.2.1 --- Problem Description --- p.10 / Chapter 2.2.2 --- Neural Network Configuration and Training --- p.12 / Chapter 2.2.3 --- Simulation Result --- p.13 / Chapter 3 --- Neural Based Approximation of Center Manifold Equations --- p.19 / Chapter 3.1 --- Solving Center Manifold Equations by Feedforward Neural Network Approx- imation --- p.19 / Chapter 3.2 --- Example --- p.21 / Chapter 3.2.1 --- Problem Description --- p.21 / Chapter 3.2.2 --- Simulation Result --- p.24 / Chapter 3.2.3 --- Discussion --- p.24 / Chapter 4 --- Connection of Center Manifold Equations to Output Regulation Problem --- p.29 / Chapter 4.1 --- Output Regulation Theory --- p.29 / Chapter 4.2 --- Reduction of Regulator Equation into Center Manifold Equations --- p.31 / Chapter 5 --- Application to the Control Design of Ball and Beam System --- p.34 / Chapter 5.1 --- Problem Description --- p.34 / Chapter 5.2 --- Neural Approximation Solution of Center Manifold Equations --- p.37 / Chapter 5.3 --- Simulation Results --- p.38 / Chapter 5.4 --- Discussion --- p.45 / Chapter 6 --- Neural Based Disturbance Rejection of Nonlinear Benchmark Problem (TORA System) --- p.48 / Chapter 6.1 --- Problem Description --- p.48 / Chapter 6.2 --- Neural based Approximation of the Center Manifold Equations of TORA System --- p.51 / Chapter 6.3 --- Simulation Results --- p.53 / Chapter 6.4 --- Discussion --- p.59 / Chapter 7 --- Conclusion --- p.62 / Chapter 7.1 --- Future Works --- p.63 / Chapter A --- Center Manifold Theory --- p.64 / Chapter B --- Relation between Center Manifold Equation and Output Regulation Prob- lem --- p.66 / Biography --- p.68 / References --- p.69

Zhong, Renxin. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2007. / Includes bibliographical references (leaves 107-114). / Abstracts in English and Chinese. / Abstract --- p.iv / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Non-Minimum Phase Nonlinear Systems --- p.1 / Chapter 1.2 --- Robust Output Regulation Problem --- p.4 / Chapter 1.3 --- Global Robust Output Regulation for Non-Minimum Phase Nonlinear Systems in Lower Triangular Form --- p.6 / Chapter 1.4 --- Rotational/Translational Actuator System --- p.8 / Chapter 1.5 --- Organization and Contributions --- p.8 / Chapter 2 --- Global Robust Output Regulation for Non-Minimum Phase Non-linear Systems in Lower Triangular Form --- p.10 / Chapter 2.1 --- Introduction --- p.10 / Chapter 2.2 --- Assumptions and Preliminaries --- p.12 / Chapter 2.3 --- Solvability Conditions --- p.17 / Chapter 2.4 --- Numerical Examples --- p.19 / Chapter 2.5 --- Concluding Remarks --- p.46 / Chapter 3 --- Global Robust Output Regulation for A Class of Non-Minimum Phase Nonlinear Systems by Output Feedback Control --- p.47 / Chapter 3.1 --- Introduction --- p.48 / Chapter 3.2 --- Assumptions and Preliminaries --- p.49 / Chapter 3.3 --- Reduced order observer design --- p.56 / Chapter 3.4 --- Stabilization of x system --- p.59 / Chapter 3.5 --- "Interconnection of the n,z,ζ,x subsystems and small gain condition" --- p.63 / Chapter 3.6 --- Numerical example --- p.67 / Chapter 3.7 --- Conclusion --- p.76 / Chapter 4 --- Robust output regulation for the nonlinear benchmark problem via output feedback --- p.77 / Chapter 4.1 --- Introduction --- p.78 / Chapter 4.2 --- Disturbance rejection problem of the RTAC system by output feedback control --- p.79 / Chapter 4.3 --- Robust Disturbance rejection problem of the RTAC system by output feedback --- p.88 / Chapter 4.4 --- Conclusion --- p.98 / Chapter 5 --- Conclusion --- p.103 / List of Figures --- p.105 / Bibliography --- p.107 / Biography --- p.115

This work describes analysis, design, and implementation of a novel switching robust adaptive control (SRAC) method for nonlinear systems. The proposed method takes advantage of both adaptive control (AC) and robust control (RC) methods. SRAC employs one of the methods when this method is advantageous and switches to the other method when the other one becomes the preferred choice. To this end, RC is used to deal with transient effects caused by uncertainties and disturbances. The system switches over to AC for good steady state performance when certain switching criteria are satisfied. If external disturbances become dominant or new uncertainties are introduced while AC is active, the system will switch back to RC. In this manner, the switching process between AC and RC will continue to take place guaranteeing improved performance, robustness, and accuracy for the entire operation of the system. The novel idea behind the proposed method is a smart novel mechanism of bi-directional switching between RC and AC. In this mechanism, the involvement of estimators and switching rules play a decisive part in guaranteeing the smooth switching and the stability of the system. The implementation and design issues of the novel method were first evaluated by simulation on a mass spring system and then on a robot manipulator system. To control these systems with satisfactory performance, nonlinearities and uncertainties have been properly analysed and embedded into models and control algorithms. Simulation results showed the superior performance of the proposed method compared with other control methods. The experimental validation of the proposed method was conducted on a Puma 560 robot manipulator system which was established by joints 2 and 3 of the robot. Extensive comparative experimental results have validated the efficacy and superior performance of the proposed SRAC method over other control methods in the face of uncertainties and disturbances. As part of this work, a comprehensive dynamic model of robotic manipulator in the presence of joint motors, gravitational forces, friction forces and payload has been developed using MAPLE. A systematic design framework for the SRAC method has also been developed.

This thesis focuses on nonlinear robust process control synthesis and analysis. The theoretical fundamental is the L2 finite gain theory. The aim of this research is to gain better understanding of this theory and develop new process control synthesis and analysis methods for nonlinear processes with model uncertainties and unmeasured disturbances.The current nonlinear process control methods are examined in this thesis. The research scopes of this study are described as follows: 1/. To characterize the most common process control problems such as zero-offset requirement, presentation of model uncertainties and unknown disturbance in the L2 finite gain theory framework and solve the basic theoretical issues concerned in controller design. 2/. To solve numerical computation problems arising in the nonlinear controller. 3/. To investigate the relationship between robustness requirement and performance requirement for nonlinear systems in the L2 finite gain theory framework. 4/. To consider the common phenomenon such as time-delay in the new developed methods. 5/. To investigate the advantages of the controller based on the L2 finite gain theory over the current nonlinear control methods. A series of new systematic robust process control synthesis approaches are the main contributions of this study. Simulations show the potential of these newly developed methods. / Doctor of Philosophy (PhD)