Development of U-model enhansed nonlinear systems

Nonlinear control system design has been widely recognised as a challenging issue where the key objective is to develop a general model prototype with conciseness, flexibility and manipulability, so that the designed control system can best match the required performance or specifications. As a generic systematic approach, U-model concept appeared in Prof. Quanmin Zhu’s Doctoral thesis, and U-model approach was firstly published in the journal paper titled with ‘U-model based pole placement for nonlinear plants’ in 2002.The U-model polynomial prototype precisely describes a wide range of smooth nonlinear polynomial models, defined as a controller output u(t-1) based time-varying polynomial models converted from the original nonlinear model. Within this equivalent U-model expression, the first study of U-model based pole placement controller design for nonlinear plants is a simple mapping exercise from ordinary linear and nonlinear difference equations to time-varying polynomials in terms of the plant input u(t-1). The U-model framework realised the concise and applicable design for nonlinear control system by using such linear polynomial control system design approaches.Since the first publication, the U-model methodology has progressed and evolved over the course of a decade. By using the U-model technique, researchers have proposed many different linear algorithms for the design of control systems for the nonlinear polynomial model including; adaptive control, internal control, sliding mode control, predictive control and neural network control. However, limited research has been concerned with the design and analysis of robust stability and performance of U-model based control systems.This project firstly proposes a suitable method to analyse the robust stability of the developed U-model based pole placement control systems against uncertainty. The parameter variation is bounded, thus the robust stability margin of the closed loop system can be determined by using LMI (Linear Matrix Inequality) based robust stability analysis procedure. U-block model is defined as an input output linear closed loop model with pole assignor converted from the U-model based control system. With the bridge of U-model approach, it connects the linear state space design approach with the nonlinear polynomial model. Therefore, LMI based linear robust controller design approaches are able to design enhanced robust control system within the U-block model structure.With such development, the first stage U-model methodology provides concise and flexible solutions for complex problems, where linear controller design methodologies are directly applied to nonlinear polynomial plant-based control system design. The next milestone work expands the U-model technique into state space control systems to establish the new framework, defined as the U-state space model, providing a generic prototype for the simplification of nonlinear state space design approaches.The U-state space model is first described as a controller output u(t-1) based time-varying state equations, which is equivalent to the original linear/nonlinear state space models after conversion. Then, a basic idea of corresponding U-state feedback control system design method is proposed based on the U-model principle. The linear state space feedback control design approach is employed to nonlinear plants described in state space realisation under U-state space structure. The desired state vectors defined as xd(t), are determined by closed loop performance (such as pole placement) or designer specifications (such as LQR). Then the desired state vectors substitute the desired state vectors into original state space equations (regarded as next time state variable xd(t) = x(t) ). Therefore, the controller output u(t-1) can be obtained from one of the roots of a root-solving iterative algorithm.A quad-rotor rotorcraft dynamic model and inverted pendulum system are introduced to verify the U-state space control system design approach for MIMO/SIMO system. The linear design approach is used to determine the closed loop state equation, then the controller output can be obtained from root solver. Numerical examples and case studies are employed in this study to demonstrate the effectiveness of the proposed methods

A vision-based optical character recognition system for real-time identification of tractors in a port container terminal

Automation has been seen as a promising solution to increase the productivity of modern sea port container terminals. The potential of increase in throughput, work efficiency and reduction of labor cost have lured stick holders to strive for the introduction of automation in the overall terminal operation. A specific container handling process that is readily amenable to automation is the deployment and control of gantry cranes in the container yard of a container terminal where typical operations of truck identification, loading and unloading containers, and job management are primarily performed manually in a typical terminal. To facilitate the overall automation of the gantry crane operation, we devised an approach for the real-time identification of tractors through the recognition of the corresponding number plates that are located on top of the tractor cabin. With this crucial piece of information, remote or automated yard operations can then be performed. A machine vision-based system is introduced whereby these number plates are read and identified in real-time while the tractors are operating in the terminal. In this paper, we present the design and implementation of the system and highlight the major difficulties encountered including the recognition of character information printed on the number plates due to poor image integrity. Working solutions are proposed to address these problems which are incorporated in the overall identification system.postprin

Job shop scheduling with artificial immune systems

The job shop scheduling is complex due to the dynamic environment. When the information of the jobs and machines are pre-defined and no unexpected events occur, the job shop is static. However, the real scheduling environment is always dynamic due to the constantly changing information and different uncertainties. This study discusses this complex job shop scheduling environment, and applies the AIS theory and switching strategy that changes the sequencing approach to the dispatching approach by taking into account the system status to solve this problem. AIS is a biological inspired computational paradigm that simulates the mechanisms of the biological immune system. Therefore, AIS presents appealing features of immune system that make AIS unique from other evolutionary intelligent algorithm, such as self-learning, long-lasting memory, cross reactive response, discrimination of self from non-self, fault tolerance, and strong adaptability to the environment. These features of AIS are successfully used in this study to solve the job shop scheduling problem. When the job shop environment is static, sequencing approach based on the clonal selection theory and immune network theory of AIS is applied. This approach achieves great performance, especially for small size problems in terms of computation time. The feature of long-lasting memory is demonstrated to be able to accelerate the convergence rate of the algorithm and reduce the computation time. When some unexpected events occasionally arrive at the job shop and disrupt the static environment, an extended deterministic dendritic cell algorithm (DCA) based on the DCA theory of AIS is proposed to arrange the rescheduling process to balance the efficiency and stability of the system. When the disturbances continuously occur, such as the continuous jobs arrival, the sequencing approach is changed to the dispatching approach that involves the priority dispatching rules (PDRs). The immune network theory of AIS is applied to propose an idiotypic network model of PDRs to arrange the application of various dispatching rules. The experiments show that the proposed network model presents strong adaptability to the dynamic job shop scheduling environment.postprin

Computer Aided Verification

This open access two-volume set LNCS 10980 and 10981 constitutes the refereed proceedings of the 30th International Conference on Computer Aided Verification, CAV 2018, held in Oxford, UK, in July 2018. The 52 full and 13 tool papers presented together with 3 invited papers and 2 tutorials were carefully reviewed and selected from 215 submissions. The papers cover a wide range of topics and techniques, from algorithmic and logical foundations of verification to practical applications in distributed, networked, cyber-physical, and autonomous systems. They are organized in topical sections on model checking, program analysis using polyhedra, synthesis, learning, runtime verification, hybrid and timed systems, tools, probabilistic systems, static analysis, theory and security, SAT, SMT and decisions procedures, concurrency, and CPS, hardware, industrial applications

Nonlinear model order reduction and control of very flexible aircraft

In the presence of aerodynamic turbulence, very flexible aircraft exhibit large deformations and as a result their behaviour is characterised as intrinsically nonlinear. These nonlinear effects become significant when the coupling of rigid–body motion with nonlinear structural dynamics occurs and needs to be taken into account for flight control system design. However, control design of large–order nonlinear systems is challenging and normally, is limited by the size of the system. Herein, nonlinear model order reduction techniques are used to make feasible a variety of linear and nonlinear control designs for large–order nonlinear coupled systems. A series of two–dimensional and three–dimensional test cases coupled with strip aerodynamics and Computational– Fluid–Dynamics is presented. A systematic approach to the model order reduction of coupled fluid–structure–flight dynamics models of arbitrary fidelity is developed. It uses information on the eigenspectrum of the coupled-system Jacobian matrix and projects the system through a Taylor series expansion, retaining terms up to third order, onto a small basis of eigenvectors representative of the full–model dynamics. The nonlinear reduced–order model representative of the dynamics of the nonlinear full–order model is then exploited for parametric worst–case gust studies and a variety of control design for gust load alleviation and flutter suppression. The control approaches were based on the robust H∞ controller and a nonlinear adaptive controller based on the model reference adaptive control scheme via a Lyapunov stability approach. A two degree–of–freedom aerofoil model coupled with strip theory and with Computational–Fluid–Dynamics is used to evaluate the model order reduction technique. The nonlinear effects are efficiently captured by the nonlinear model order reduction method. The derived reduced models are then used for control synthesis by the H∞ and the model reference adaptive control. Furthermore, the numerical models developed in this thesis are used for the description of the physics of a wind–tunnel model at the University of Liverpool and become the benchmark to design linear and nonlinear controllers. The need for nonlinear control design was demonstrated for the wind–tunnel model in simulation. It was found that for a wind–tunnel model with a cubic structural nonlinearity in the plunge degree–of–freedom, conventional linear control designs were inadequate for flutter suppression. However, a nonlinear controller was found suitable to increase the flight envelope and suppress the flutter. A large body of work dealt with the development of a numerical framework for the simulation of the flight dynamics of very flexible aircraft. Geometrically–exact nonlinear beam structural models were coupled with the rigid–body, the flight dynamics degrees–of–freedom and the strip theory aerodynamics, for the description of the nonlinear physics of free–flying aircraft. The flexibility effects of these vehicles on the flight dynamic response is quantified. It is found that different angle of attack and control input rotation is needed to trim a flexible aircraft and that a rigid analysis is not appropriate. Furthermore, it is shown that the aircraft flexibility has an impact on the flight dynamic response and needs to be included. The fully coupled models are consequently reduced in size by the nonlinear model reduction technique for a cheaper and a simpler computation of a variety of linear and nonlinear automatic control designs that are applied on the full–order nonlinear models inside the developed framework for gust load alleviation. The approach is tested on a Global Hawk type unmanned aerial vehicle developed by DSTL, on a HALE full aircraft configuration, and on a very large flexible free–flying wing. A comparison of the developed control algorithms is carefully addressed with the adaptive controller achieving better gust loads alleviation in some cases. Finally, future possible implementations and ideas related to the nonlinear model order reduction and the control design of flexible aircraft are discussed

Computer Aided Verification

This open access two-volume set LNCS 10980 and 10981 constitutes the refereed proceedings of the 30th International Conference on Computer Aided Verification, CAV 2018, held in Oxford, UK, in July 2018. The 52 full and 13 tool papers presented together with 3 invited papers and 2 tutorials were carefully reviewed and selected from 215 submissions. The papers cover a wide range of topics and techniques, from algorithmic and logical foundations of verification to practical applications in distributed, networked, cyber-physical, and autonomous systems. They are organized in topical sections on model checking, program analysis using polyhedra, synthesis, learning, runtime verification, hybrid and timed systems, tools, probabilistic systems, static analysis, theory and security, SAT, SMT and decisions procedures, concurrency, and CPS, hardware, industrial applications