Integral sliding mode control with integral switching gain for magnetic levitation apparatus

Author name used in this publication: Norbert C. CheungRefereed conference paper2008-2009 > Academic research: refereed > Refereed conference paperVersion of RecordPublishe

Robust Model Predictive Control With Integral Sliding Mode in Continuous-Time Sampled-Data Nonlinear Systems

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Sliding Mode Control

The main objective of this monograph is to present a broad range of well worked out, recent application studies as well as theoretical contributions in the field of sliding mode control system analysis and design. The contributions presented here include new theoretical developments as well as successful applications of variable structure controllers primarily in the field of power electronics, electric drives and motion steering systems. They enrich the current state of the art, and motivate and encourage new ideas and solutions in the sliding mode control area

Minimax sliding mode control design for linear evolution equations with noisy measurements and uncertain inputs

International audienceWe extend a sliding mode control methodology to linear evolution equations with uncertain but bounded inputs and noise in observations. We first describe the reachability set of the state equation in the form of an infinite-dimensional ellipsoid, and then steer the minimax center of this ellipsoid toward a finitedimensional sliding surface in finite time by using the standard sliding mode output-feedback controller in equivalent form. We demonstrate that the designed controller is the best (in the minimax sense) in the class of all measurable functionals of the output. Our design is illustrated by two numerical examples: output-feedback stabilization of linear delay equations, and control of moments for an advection-diffusion equation in 2D

Robust de-centralized control and estimation for inter-connected systems

The thesis is concerned with the theoretical development of the control of inter-connected systems to achieve the whole overall stability and specific performance. A special included feature is the Fault-Tolerant Control (FTC) problem for the inter-connected system in terms of local subsystem actuator fault estimation. Hence, the thesis describes the main FTC challenges of distributed control of uncertain non-linear inter-connected systems. The basic principle adopted throughout the work is that the controller has two components, one involving the nominal control with unmatched components including uncertainties and disturbances. The second controller dealing with matched components including uncertainties and actuator faults.The main contributions of the thesis are summarised as follows:- The non-linear inter-connected systems are controlled by two controllers. The linear part via a linear matrix inequality (LMI) technique and the discontinuous part by using Integral Sliding Mode Control (ISMC) based on state feedback control.- The development of a new observer-based state estimate control strategy for non-linear inter-connected systems. The technique is applied either to every individual subsystem or to the whole as one shot system.- A new proposal of Adaptive Output Integral Sliding Mode Control (AOISMC) based only on output information plus static output feedback control is designed via an LMI formulation to control non-linear inter-connected systems. The new method is verified by application to a mathematical example representing an electrical power generator.- The development of a new method to design a dynamic control based on an LMI framework with Output Integral Sliding Mode Control (OISMC) to improve the stability and performance.- Using the above framework, making use of LMI tools and ISMC, a method of on-line actuator fault estimation has been proposed using the Proportional Multiple Integral Observer (PMIO) for fault estimation applicable to non-linear inter-connected systems

Robust de-centralized control and estimation for inter-connected systems

The thesis is concerned with the theoretical development of the control of inter-connected systems to achieve the whole overall stability and specific performance. A special included feature is the Fault-Tolerant Control (FTC) problem for the inter-connected system in terms of local subsystem actuator fault estimation. Hence, the thesis describes the main FTC challenges of distributed control of uncertain non-linear inter-connected systems. The basic principle adopted throughout the work is that the controller has two components, one involving the nominal control with unmatched components including uncertainties and disturbances. The second controller dealing with matched components including uncertainties and actuator faults. The main contributions of the thesis are summarised as follows: - The non-linear inter-connected systems are controlled by two controllers. The linear part via a linear matrix inequality (LMI) technique and the discontinuous part by using Integral Sliding Mode Control (ISMC) based on state feedback control. - The development of a new observer-based state estimate control strategy for non-linear inter-connected systems. The technique is applied either to every individual subsystem or to the whole as one shot system. - A new proposal of Adaptive Output Integral Sliding Mode Control (AOISMC) based only on output information plus static output feedback control is designed via an LMI formulation to control non-linear inter-connected systems. The new method is verified by application to a mathematical example representing an electrical power generator. - The development of a new method to design a dynamic control based on an LMI framework with Output Integral Sliding Mode Control (OISMC) to improve the stability and performance. - Using the above framework, making use of LMI tools and ISMC, a method of on-line actuator fault estimation has been proposed using the Proportional Multiple Integral Observer (PMIO) for fault estimation applicable to non-linear inter-connected systems

Development of advanced autonomous learning algorithms for nonlinear system identification and control

Identification of nonlinear dynamical systems, data stream analysis, etc. is usually handled by autonomous learning algorithms like evolving fuzzy and evolving neuro-fuzzy systems (ENFSs). They are characterized by the single-pass learning mode and open structure-property. Such features enable their effective handling of fast and rapidly changing natures of data streams. The underlying bottleneck of ENFSs lies in its design principle, which involves a high number of free parameters (rule premise and rule consequent) to be adapted in the training process. This figure can even double in the case of the type-2 fuzzy system. From this literature gap, a novel ENFS, namely Parsimonious Learning Machine (PALM) is proposed in this thesis. To reduce the number of network parameters significantly, PALM features utilization of a new type of fuzzy rule based on the concept of hyperplane clustering, where it has no rule premise parameters. PALM is proposed in both type-1 and type-2 fuzzy systems where all of them characterize a fully dynamic rule-based system. Thus, it is capable of automatically generating, merging, and tuning the hyperplane-based fuzzy rule in a single-pass manner. Moreover, an extension of PALM, namely recurrent PALM (rPALM), is proposed and adopts the concept of teacher-forcing mechanism in the deep learning literature. The efficacy of both PALM and rPALM have been evaluated through numerical study with data streams and to identify nonlinear unmanned aerial vehicle system. The proposed models showcase significant improvements in terms of computational complexity and the number of required parameters against several renowned ENFSs while attaining comparable and often better predictive accuracy. The ENFSs have also been utilized to develop three autonomous intelligent controllers (AICons) in this thesis. They are namely Generic (G) controller, Parsimonious controller (PAC), and Reduced Parsimonious Controller (RedPAC). All these controllers start operating from scratch with an empty set of fuzzy rules, and no offline training is required. To cope with the dynamic behavior of the plant, these controllers can add, merge or prune the rules on demand. Among three AICons, the G-controller is built by utilizing an advanced incremental learning machine, namely Generic Evolving Neuro-Fuzzy Inference System. The integration of generalized adaptive resonance theory provides a compact structure of the G-controller. Consequently, the faster evolution of structure is witnessed, which lowers its computational cost. Another AICon namely, PAC is rooted with PALM's architecture. Since PALM has a dependency on user-defined thresholds to adapt the structure, these thresholds are replaced with the concept of bias- variance trade-off in PAC. In RedPAC, the network parameters have further reduced in contrast with PALM-based PAC, where the number of consequent parameters has reduced to one parameter per rule. These AICons work with very minor expert domain knowledge and developed by incorporating the sliding mode control technique. In G-controller and RedPAC, the control law and adaptation laws for the consequent parameters are derived from the SMC algorithm to establish a stable closed-loop system, where the stability of these controllers are guaranteed by using the Lyapunov function and the uniform asymptotic convergence of tracking error to zero is witnessed through the implication of an auxiliary robustifying control term. While using PAC, the boundedness and convergence of the closed-loop control system's tracking error and the controller's consequent parameters are confirmed by utilizing the LaSalle-Yoshizawa theorem. Their efficacy is evaluated by observing various trajectory tracking performance of unmanned aerial vehicles. The accuracy of these controllers is comparable or better than the benchmark controllers where the proposed controllers incur significantly fewer parameters to attain similar or better tracking performance

Wind Turbine Reliability Improvement by Fault Tolerant Control

This thesis investigates wind turbine reliability improvement, utilizing model-based fault tolerant control, so that the wind turbine continues to operate satisfactorily with the same performance index in the presence of faults as in fault-free situations. Numerical simulations are conducted on the wind turbine bench mark model associated with the considered faults and comparison is made between the performance of the proposed controllers and industrial controllers illustrating the superiority of the proposed ones

Degradation Modeling and Remaining Useful Life Estimation: From Statistical Signal Processing to Deep Learning Models

Aging critical infrastructures and valuable machineries together with recent catastrophic incidents such as the collapse of Morandi bridge, or the Gulf of Mexico oil spill disaster, call for an urgent quest to design advanced and innovative prognostic solutions, and efficiently incorporate multi-sensor streaming data sources for industrial development. Prognostic health management (PHM) is among the most critical disciplines that employs the advancement of the great interdependency between signal processing and machine learning techniques to form a key enabling technology to cope with maintenance development tasks of complex industrial and safety-critical systems. Recent advancements in predictive analytics have empowered the PHM paradigm to move from the traditional condition-based monitoring solutions and preventive maintenance programs to predictive maintenance to provide an early warning of failure, in several domains ranging from manufacturing and industrial systems to transportation and aerospace. The focus of the PHM is centered on two core dimensions; the first is taking into account the behavior and the evolution over time of a fault once it occurs, while the second one aims at estimating/predicting the remaining useful life (RUL) during which a device can perform its intended function. The first dimension is the degradation that is usually determined by a degradation model derived from measurements of critical parameters of relevance to the system. Developing an accurate model for the degradation process is a primary objective in prognosis and health management. Extensive research has been conducted to develop new theories and methodologies for degradation modeling and to accurately capture the degradation dynamics of a system. However, a unified degradation framework has yet not been developed due to: (i) structural uncertainties in the state dynamics of the system and (ii) the complex nature of the degradation process that is often non-linear and difficult to model statistically. Thus even for a single system, there is no consensus on the best degradation model. In this regard, this thesis tries to bridge this gap by proposing a general model that able to model the true degradation path without having any prior knowledge of the true degradation model of the system. Modeling and analysis of degradation behavior lead us to RUL estimation, which is the second dimension of the PHM and the second part of the thesis. The RUL is the main pillar of preventive maintenance, which is the time a machine is expected to work before requiring repair or replacement. Effective and accurate RUL estimation can avoid catastrophic failures, maximize operational availability, and consequently reduce maintenance costs. The RUL estimation is, therefore, of paramount importance and has gained significant attention for its importance to improve systems health management in complex fields including automotive, nuclear, chemical, and aerospace industries to name but a few. A vast number of researches related to different approaches to the concept of remaining useful life have been proposed, and they can be divided into three broad categories: (i) Physics-based; (ii) Data-driven, and; (iii) Hybrid approaches (multiple-model). Each category has its own limitations and issues, such as, hardly adapt to different prognostic applications, in the first one, and accuracy degradation issues, in the second one, because of the deviation of the learned models from the real behavior of the system. In addition to hardly sustain good generalization. Our thesis belongs to the third category, as it is the most promising category, in particular, the new hybrid models, on basis of two different architectures of deep neural networks, which have great potentials to tackle complex prognostic issues associated with systems with complex and unknown degradation processes