12 research outputs found

    A Survey of Deep Learning Applications to Autonomous Vehicle Control

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    Designing a controller for autonomous vehicles capable of providing adequate performance in all driving scenarios is challenging due to the highly complex environment and inability to test the system in the wide variety of scenarios which it may encounter after deployment. However, deep learning methods have shown great promise in not only providing excellent performance for complex and non-linear control problems, but also in generalising previously learned rules to new scenarios. For these reasons, the use of deep learning for vehicle control is becoming increasingly popular. Although important advancements have been achieved in this field, these works have not been fully summarised. This paper surveys a wide range of research works reported in the literature which aim to control a vehicle through deep learning methods. Although there exists overlap between control and perception, the focus of this paper is on vehicle control, rather than the wider perception problem which includes tasks such as semantic segmentation and object detection. The paper identifies the strengths and limitations of available deep learning methods through comparative analysis and discusses the research challenges in terms of computation, architecture selection, goal specification, generalisation, verification and validation, as well as safety. Overall, this survey brings timely and topical information to a rapidly evolving field relevant to intelligent transportation systems.Comment: 23 pages, 3 figures, Accepted in IEEE Transactions on Intelligent Transportation System

    Active Suppression ofAerofoil Flutter via Neural-Network-Based Adaptive Nonlinear Optimal Control

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    This thesis deals with active flutter suppression (AFS) on aerofoils via adaptive nonlinear optimal control using neural networks (NNs). Aeroelastic flutter can damage aerofoils if not properly controlled. AFS not only ensures flutter-free flight but also enables the use of aerodynamically more efficient lightweight aerofoils. However, existing optimal controllers for AFS are generally susceptible to modelling errors while other controllers less prone to uncertainties do not provide optimal control. This thesis, thus, aims to reduce the impact of the dilemma by proposing new solutions based on nonlinear optimal control online synthesis (NOCOS) according to online updated dynamics. Existing NOCOS methods, with NNs as essential elements, require a separate initial stabilising control law for the overall system, an additional stabilising tuning loop for the actor NN, or an additional stabilising term in the critic NN tuning law, to guarantee the closed-loop stability for unstable and marginally stable systems. The resulting complexity is undesired in AFS applications due to computational concerns in real-time implementation. Moreover, the existing NOCOS methods are confined to locally nonlinear systems, while aeroelastic systems under consideration are globally nonlinear. These make all the existing NOCOS algorithms inapplicable to AFS without modification and improvement. Therefore, this thesis solves the aforementioned problems through the following step-by-step approaches. Firstly, a four degrees-of-freedom (4-DOF) aeroelastic model is considered, where leading- and trailing-edge control surfaces of the aerofoil are used to actively suppress flutter. Accordingly, a virtual stiffness-damping system (VSDS) is developed to simulate physical stiffness in the aeroelastic system. The VSDS, together with a scaled-down typical aerofoil section placed in a wind tunnel, serve as an experimental 4-DOF aeroelastic test-bed for synthesis and validation of proposed AFS controllers that follow. Secondly, a Modified form of NN-based Value Function Approximation (MVFA), tuned by gradient-descent learning, is proposed for NOCOS to address the closedloop stability in a compact controller configuration suitable for real-time implementation. Its validity and efficacy are examined by the Lyapunov stability analysis and numerical studies. Thirdly, a systematic procedure based on linear matrix inequalities is further proposed for synthesising a scheduled parameter matrix to generalise the MVFA to to globally nonlinear cases, so that the new NN controller suits AFS applications. In addition, the extended Kalman filter (EKF) is proposed for the new NN controller for fast parameter convergence. An identifier NN is also derived to capture and update aeroelastic dynamics in real time to mitigate the impact of modelling errors. Wind-tunnel experiments were conducted for validation. Finally, a non-quadratic functional is introduced to generalise the performance index to tackle the problem where control inputs are constrained. The feasibility of including the non-quadratic cost function under the proposed control scheme based on the MVFA is examined via the Lyapunov stability analysis and was also experimentally evaluated through the wind-tunnel testings. The proposed NN controllers are compact in structure and shown capable of maintaining the closed-loop stability while eliminating the need for a separate initial stabilising control law for the overall system, an additional tuning loop for the actor NN, and an additional stabilising term in the critic NN tuning law. Under the new control schemes, online synthesised nonlinear control laws are optimal in the cases with and without constraints in control. Comparisons drawn with a popular linear-parameter-varying (LPV) controller in the form of the widely used linear quadratic regulator (LQR) in experiments show that the proposed NN controllers outperform the LPV-LQR algorithm and improve AFS from the optimal control perspective. Specifically, the proposed NN controllers can effectively mitigate the impact of modelling errors, successfully solving the mentioned dilemma involved in AFS. The results also confirm that the proposed NN controllers are suitable for real-time implementation.Thesis (Ph.D.) -- University of Adelaide, School of Mechanical Engineering, 201

    Evolutionary methods for modelling and control of linear and nonlinear systems

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    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 identification techniques are developed based on the L2 norm. These techniques can identify both the system parameters and transport delay in the same evolution process. These open-loop identification methods are further extended to closed-loop system identification. For robust control, evolutionary L∞ identification techniques are developed. Since most practical systems are nonlinear in nature and it is difficult to model the dominant dynamics of such a system while retaining neglected dynamics for accuracy, evolutionary grey-box modelling techniques are proposed. These techniques can utilise physical law dominated global clearbox structure, with local black-boxes to include unmeasurable nonlinearities as the coefficient models of the clear-box. This unveils a new way of engineering system modelling. With an accurately identified model, controller design problems still need to be overcome. Design difficulties by conventional analytical and numerical means are discussed and a design automation technique is then developed. This is again enabled by the hybrid evolutionary algorithm in this thesis. More importantly, this technique enables the unification of linear control system designs in both the time and the frequency domains under performance satisfaction. It is also extended to control along a trajectory of operating points for nonlinear systems. In addition, a multi-objective evolutionary algorithm is developed to make the design more transparent and visible. To achieve a step towards autonomy in building control systems, a technique for direct designs from plant step response data is developed, which bypasses the system identification phase. These computer-automated intelligent design methodologies are expected to offer added productivity and quality of control systems

    A survey of the application of soft computing to investment and financial trading

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    Harnessing Neural Dynamics as a Computational Resource

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    Researchers study nervous systems at levels of scale spanning several orders of magnitude, both in terms of time and space. While some parts of the brain are well understood at specific levels of description, there are few overarching theories that systematically bridge low-level mechanism and high-level function. The Neural Engineering Framework (NEF) is an attempt at providing such a theory. The NEF enables researchers to systematically map dynamical systems—corresponding to some hypothesised brain function—onto biologically constrained spiking neural networks. In this thesis, we present several extensions to the NEF that broaden both the range of neural resources that can be harnessed for spatiotemporal computation and the range of available biological constraints. Specifically, we suggest a method for harnessing the dynamics inherent in passive dendritic trees for computation, allowing us to construct single-layer spiking neural networks that, for some functions, achieve substantially lower errors than larger multi-layer networks. Furthermore, we suggest “temporal tuning” as a unifying approach to harnessing temporal resources for computation through time. This allows modellers to directly constrain networks to temporal tuning observed in nature, in ways not previously well-supported by the NEF. We then explore specific examples of neurally plausible dynamics using these techniques. In particular, we propose a new “information erasure” technique for constructing LTI systems generating temporal bases. Such LTI systems can be used to establish an optimal basis for spatiotemporal computation. We demonstrate how this captures “time cells” that have been observed throughout the brain. As well, we demonstrate the viability of our extensions by constructing an adaptive filter model of the cerebellum that successfully reproduces key features of eyeblink conditioning observed in neurobiological experiments. Outside the cognitive sciences, our work can help exploit resources available on existing neuromorphic computers, and inform future neuromorphic hardware design. In machine learning, our spatiotemporal NEF populations map cleanly onto the Legendre Memory Unit (LMU), a promising artificial neural network architecture for stream-to-stream processing that outperforms competing approaches. We find that one of our LTI systems derived through “information erasure” may serve as a computationally less expensive alternative to the LTI system commonly used in the LMU

    Forecasting: theory and practice

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    Forecasting has always been in the forefront of decision making and planning. The uncertainty that surrounds the future is both exciting and challenging, with individuals and organisations seeking to minimise risks and maximise utilities. The lack of a free-lunch theorem implies the need for a diverse set of forecasting methods to tackle an array of applications. This unique article provides a non-systematic review of the theory and the practice of forecasting. We offer a wide range of theoretical, state-of-the-art models, methods, principles, and approaches to prepare, produce, organise, and evaluate forecasts. We then demonstrate how such theoretical concepts are applied in a variety of real-life contexts, including operations, economics, finance, energy, environment, and social good. We do not claim that this review is an exhaustive list of methods and applications. The list was compiled based on the expertise and interests of the authors. However, we wish that our encyclopedic presentation will offer a point of reference for the rich work that has been undertaken over the last decades, with some key insights for the future of the forecasting theory and practice
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