Evolutionary methods for modelling and control of linear and nonlinear systems

[摘要] The aim of this work is to explore the potential and enhance the capability of evolutionary computation for the development of novel and advanced methodologies for engineering system modelling and controller design automation. The key to these modelling and design problems is optimisation.Conventional calculus-based methods currently adopted in engineering optimisation are in essence local search techniques, which require derivative information and lack of robustness in solving practical engineering problems. One objective of this research is thus to develop an effective and reliable evolutionary algorithm for engineering applications. For this, a hybrid evolutionary algorithm is developed, which combines the global search power of a "generational" EA with the interactive local fine-tuning of Boltzmann learning. It overcomes the weakness in local exploration and chromosome stagnation usually encountered in pure EAs. A novel one-integer-one-parameter coding scheme is also developed to significantly reduce the quantisation error, chromosome length and processing overhead time. An "Elitist Direct Inheritance" technique is developed to incorporate with Bolzmann learning for reducing the control parameters and convergence time of EAs. Parallelism of the hybrid EA is also realised in this thesis with nearly linear pipelinability.Generic model reduction and linearisation techniques in L2 and L∞ norms are developed based on the hybrid EA technique. They are applicable to both discrete and continuous-time systems in both the time and the frequency domains. Superior to conventional model reduction methods, the EA based techniques are capable of simultaneously recommending both an optimal order number and optimal parameters by a control gene used as a structural switch. This approach is extended to MIMO system linearisation from both a non-linear model and I/O data of the plant. It also allows linearisation for an entire operating region with the linear approximate-model network technique studied in this thesis.To build an original model, evolutionary black-box and clear-box system identificationtechniques are developed based on the L2 norm. These techniques can identify both thesystem parameters and transport delay in the same evolution process. These open-loopidentification methods are further extended to closed-loop system identification. For robustcontrol, evolutionary L∞ identification techniques are developed. Since most practicalsystems are nonlinear in nature and it is difficult to model the dominant dynamics of such asystem while retaining neglected dynamics for accuracy, evolutionary grey-box modellingtechniques are proposed. These techniques can utilise physical law dominated global clearboxstructure, with local black-boxes to include unmeasurable nonlinearities as thecoefficient models of the clear-box. This unveils a new way of engineering systemmodelling.With an accurately identified model, controller design problems still need to be overcome.Design difficulties by conventional analytical and numerical means are discussed and adesign automation technique is then developed. This is again enabled by the hybridevolutionary algorithm in this thesis. More importantly, this technique enables theunification of linear control system designs in both the time and the frequency domainsunder performance satisfaction. It is also extended to control along a trajectory of operatingpoints for nonlinear systems. In addition, a multi-objective evolutionary algorithm isdeveloped to make the design more transparent and visible. To achieve a step towardsautonomy in building control systems, a technique for direct designs from plant stepresponse data is developed, which bypasses the system identification phase. Thesecomputer-automated intelligent design methodologies are expected to offer addedproductivity and quality of control systems.

[发布机构] University:University of Glasgow;Department:School of Engineering

[关键词] TA Engineering (General). Civil engineering (General)