12,620 research outputs found

    High-Speed and Low-Cost Implementation of Explicit Model Predictive Controllers

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    This paper presents a new form of piecewise-affine (PWA) solution, referred to as PWA hierarchical (PWAH), to approximate the explicit model predictive control (MPC) law, achieving a very rapid control response with the use of very few computational and memory resources. This is possible because PWAH controllers consist of single-input single-output PWA modules connected in cascade so that the parameters needed to define them increase linearly instead of exponentially with the input dimension of the control problem. PWAH controllers are not universal approximators but several explicit MPC controllers can be efficiently approximated by them. A methodology to design PWAH controllers is presented and validated with application examples already solved by MPC approaches. The designed PWAH controllers implemented in field-programmable gate arrays provide the highest control speed using the fewest resources compared with the other digital implementations reported in the literature.Ministerio de EconomĂ­a, Industria y Competitividad TEC2014-57971-

    Min-Max MPC based on a computationally efficient upper bound of the worst case cost

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    Min-Max MPC (MMMPC) controllers [P.J. Campo, M. Morari, Robust model predictive control, in: Proc. American Control Conference, June 10–12, 1987, pp. 1021–1026] suffer from a great computational burden which limits their applicability in the industry. Sometimes upper bounds of the worst possible case of a performance index have been used to reduce the computational burden. This paper proposes a computationally efficient MMMPC control strategy in which the worst case cost is approximated by an upper bound based on a diagonalization scheme. The upper bound can be computed with O(n3) operations and using only simple matrix operations. This implies that the algorithm can be coded easily even in non-mathematical oriented programming languages such as those found in industrial embedded control hardware. A simulation example is given in the paper

    Computational burden reduction in Min-Max MPC

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    Min–max model predictive control (MMMPC) is one of the strategies used to control plants subject to bounded uncertainties. The implementation of MMMPC suffers a large computational burden due to the complex numerical optimization problem that has to be solved at every sampling time. This paper shows how to overcome this by transforming the original problem into a reduced min–max problem whose solution is much simpler. In this way, the range of processes to which MMMPC can be applied is considerably broadened. Proofs based on the properties of the cost function and simulation examples are given in the paper

    Towards the development of a smart flying sensor: illustration in the field of precision agriculture

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    Sensing is an important element to quantify productivity, product quality and to make decisions. Applications, such as mapping, surveillance, exploration and precision agriculture, require a reliable platform for remote sensing. This paper presents the first steps towards the development of a smart flying sensor based on an unmanned aerial vehicle (UAV). The concept of smart remote sensing is illustrated and its performance tested for the task of mapping the volume of grain inside a trailer during forage harvesting. Novelty lies in: (1) the development of a position-estimation method with time delay compensation based on inertial measurement unit (IMU) sensors and image processing; (2) a method to build a 3D map using information obtained from a regular camera; and (3) the design and implementation of a path-following control algorithm using model predictive control (MPC). Experimental results on a lab-scale system validate the effectiveness of the proposed methodology

    Linear Model Predictive Control of Induction Machine

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    This article presents new control algorithm for induction machine based on linear model predictive control (MPC). Controller works in similar manners as field oriented control (FOC), but control is performed in stator coordinates. This reduces computational demands as Park’s transformation is absent and induction machine mathematical model in stator coordinates contains less nonlinear elements. Another aim of proposed controller was to achieve fast torque response

    Consciosusness in Cognitive Architectures. A Principled Analysis of RCS, Soar and ACT-R

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    This report analyses the aplicability of the principles of consciousness developed in the ASys project to three of the most relevant cognitive architectures. This is done in relation to their aplicability to build integrated control systems and studying their support for general mechanisms of real-time consciousness.\ud To analyse these architectures the ASys Framework is employed. This is a conceptual framework based on an extension for cognitive autonomous systems of the General Systems Theory (GST).\ud A general qualitative evaluation criteria for cognitive architectures is established based upon: a) requirements for a cognitive architecture, b) the theoretical framework based on the GST and c) core design principles for integrated cognitive conscious control systems
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