Search

A theoretical and experimental comparison between standard and recently-developed improved indirect field-oriented controllers for induction motors is presented. It is shown that standard indirect field-oriented control algorithm provides asymptotic speed and flux regulation, while the improved one guarantees global exponential speed-flux tracking under condition of constant load torque. The difference between the flux sub-system design for the two controllers is analyzed. It is shown that the improved controller provides closed loop properties for flux subsystem when motor is rotating, leading to robust stabilization of the rotor flux vector space position and robust tracking of its modulus. It is demonstrated that both controllers have similar complexity and can be tuned using standard procedure for cascaded systems. Intensive experimental study shows that improved controller provides about one order higher speed tracking performance as compared with standard solution. Moreover, improved robustness with respect to rotor parameters variation leads to improved energy efficiency and robust stabilization of dynamic performance. Index Terms--Energy efficiency, electric drives, nonlinear control, vector control.

In this article, we introduce an easy-to-implement sensorless controller specifically designed for the regulation of the propellers of unmanned aerial vehicles (UAVs). As motivation, we present a comparison of the usual motor control architectures, i.e., field-oriented control (FOC) and brushless DC (BLDC) control, with special attention to the typical back-electromotive force shapes found in this application. In particular, we show that the adoption of sensorless FOC provides several advantages, both from the efficiency and the signal quality viewpoints, provided that accurate rotor position reconstruction is available. Therefore, a recently proposed observer is integrated into a nested FOC architecture, with formal stability guarantees and low computational effort, making the resulting strategy suitable for implementation in embedded computing systems. The algorithm is then compared experimentally to a sensorless BLDC controller and a high-end commercial drive, thus validating the previous results and showing effective time-varying speed tracking, as required for precise aggressive maneuvering. These features of efficiency, accuracy, and simplicity might prove instrumental in bolstering the introduction of a novel class of high-performance, robust UAV sensorless controllers in the forthcoming years. [ABSTRACT FROM AUTHOR]

In this paper, the stator/rotor currents control problem of doubly-fed induction generator under faulty line voltage is carried out. Common grid faults cause a steep decline in the line voltage profile, commonly denoted as voltage dip. This point is critical for such kind of machines, having their stator windings directly connected to the grid. In this respect, solid methodological nonlinear control theory arguments are exploited and applied to design a novel controller, whose main goal is to improve the system behaviour during voltage dips, endowing it with low voltage ride through capability, a fundamental feature required by modern Grid Codes. The proposed solution exploits both feedforward and feedback actions. The feedforward part relies on suitable reference trajectories for the system internal dynamics, which are designed to prevent large oscillations in the rotor currents and command voltages, excited by line perturbations. The feedback part uses state measurements and is designed according to Linear Matrix Inequalities (LMI) based saturated control techniques to further reduce oscillations, while explicitly accounting for the system constraints. Numerical simulations verify the benefits of the internal dynamics trajectory planning, and the saturated state feedback action, in crucially improving the Doubly-Fed Induction Machine response under severe grid faults. [ABSTRACT FROM AUTHOR]

Advanced cooling systems and optimization strategies are critical to operate modern supercomputers and high-performance computing systems in an energy-efficient fashion. Hybrid architectures combining emerging liquid cooling with traditional air cooling are a promising solution. Standard management techniques maintain these systems at fixed operating points, typically without coordination between the diverse cooling knobs. In this paper, we propose an energy-aware optimization strategy exploiting heterogeneous cooling systems in a holistic fashion with the goal of minimizing the overall cooling system power consumption, while at the same time meeting the system thermal constraints. To this purpose, we developed a modeling approach to build a low-order analytical model, which captures the overall thermal behavior of the system. Then, this compact and computationally manageable model is exploited to set and solve a treatable optimization problem, leading to definition of an energy-optimal cooling strategy. The proposed method is presented taking Galileo as real-life case study. Galileo is a high-performance computing system with hybrid cooling architecture recently installed at CINECA (a supercomputing facility located in Italy). The cooling strategy resulting from the proposed approach is compared with common strategies in order to assess the efficiency advantages. [ABSTRACT FROM PUBLISHER]

In this brief, a plug-in unit is presented to manage control voltage saturation and maximum current limit in shunt active filters (SAFs), where unconstrained control algorithms are already defined. The proposed unit extends the operating region of such devices, i.e., under large transients and overload conditions, with performance guarantees. Therefore, improved robustness, availability, and composability of SAFs are obtained. The solution is composed of two parts. An antiwindup (AW) unit is defined to deal with control input saturation by modifying the current references through a suitably designed additional dynamics. In addition, a current saturation strategy is formulated. Again the current reference is modified, accounting for the limitations of the system, augmented with the AW scheme. The approach is valid for any kind of unconstrained controller adopted to steer SAFs. Here, results are presented considering an internal-model-based current controller. Simulation and experimental tests confirm the effectiveness of the method. [ABSTRACT FROM PUBLISHER]

Today and future many-core systems are facing the utilization wall and dark silicon problems, for which not all the processing engines can be powered at the same time as this will lead to a power consumption higher than the Total Design Power (TDP) budget. Recently, computational sprinting approaches addressed the problem by exploiting the intrinsic thermal capacitance of the chip and the properties of common applications, which require intense, but temporary, use of resources. The thermal capacitance, possibly augmented with phase change materials, enables the temporary activation of all the resources simultaneously, although they largely exceed the steady-state thermal design power. In this article, we present an innovative and lowoverhead hierarchical model-predictive controller for managing thermally safe sprinting with predictable resprinting rate, which ensures the correct execution of mixed-criticality tasks. Well-targeted simulations, also based on real workload benchmarks, show the applicability and the effectiveness of our solution. [ABSTRACT FROM AUTHOR]

Next-generation Multiprocessor, or Multicore, Systems-on-Chip offer very high computing performance at the expense of a very high power density unevenly distributed on the chip. The hot spots thus generated represent a significant source of performance and reliability degradation, as well as power consumption increase. In recent years, run-time thermal control strategies have been developed to deal with this issue by acting on some "computational knobs" (e.g., clock frequencies and supply voltages). In this context, schemes based on Model Predictive Control (MPC) are particularly suitable due to their capability to deal with constraints explicitly. In this paper, we first discuss relevant properties for the design of predictive controllers for thermal systems. Starting from the Partial Differential Equation (PDE) describing heat diffusion in a solid, we prove meaningful feasibility properties that can be leveraged for constraint reduction. We then present a procedure to derive approximated but effective modular thermal models intended to build an efficient distributed MPC. Finally, a two-layer control solution is proposed to maximize performance while preserving feasibility despite model approximations. The effectiveness of this approach is validated through extensive and realistic numerical simulations. • A distributed controller is proposed for the optimal thermal capping of MPSoCs. • Control feasibility conditions are studied exploiting a PDE thermal model. • A hysteresis controller is designed for decentralized ultimate thermal capping. • A distributed MPC strategy is employed for performance optimization. • The resulting two-layer solution is validated for relevant simulation benchmarks. [ABSTRACT FROM AUTHOR]

Aggressive thermal management is a critical feature for high-end computing platforms, as worst-case thermal budgeting is becoming unaffordable. Reactive thermal management, which sets temperature thresholds to trigger thermal capping actions, is too “near-sighted,” and it may lead to severe performance degradation and thermal overshoots. More aggressive proactive thermal managements minimize performance penalty with smooth optimal control. These techniques require knowledge of thermal models, which have to be accurate and simple to make the controls effective, while keeping their complexity limited. In practice, these models are not provided by manufacturers, and in most cases, they strongly depend on the deployment environment. Hence, procedures to automatically derive thermal models in the field are needed. In this paper, we propose a gray-box procedure to learn a compact and physically consistent model for multicore chips. We leverage the physical consistency of the proposed model to tame the model complexity and to face large quantization noise in measurements. We exploit Output Error structures along with Levenberg–Marquardt and Least Squares optimization algorithms. We tackle the problem in a real-life contest: we developed a complete infrastructure for model building and thermal data collection in the Linux environment, and we tested it on an Intel Nehalem-based server CPU. [ABSTRACT FROM AUTHOR]

As result of technology scaling, single-chip multicore power density increases and its spatial and temporal workload variation leads to temperature hot-spots, which may cause nonuniform ageing and accelerated chip failure. These critical issues can be tackled by closed-loop thermal and reliability management policies. Model predictive controllers (MPC) outperform classic feedback controllers since they are capable of minimizing performance loss while enforcing safe working temperature. Unfortunately, MPC controllers rely on a priori knowledge of thermal models and their complexity exponentially grows with the number of controlled cores. In this paper, we present a scalable, fully distributed, energy-aware thermal management solution for single-chip multicore platforms. The model-predictive controller complexity is drastically reduced by splitting it in a set of simpler interacting controllers, each one allocated to a core in the system. Locally, each node selects the optimal frequency to meet temperature constraints while minimizing the performance penalty and system energy. Comparable performance with state-of-the-art MPC controllers is achieved by letting controllers exchange a limited amount of information at runtime on a neighborhood basis. In addition, we address model uncertainty by supporting learning of the thermal model with a novel distributed self-calibration approach that matches well the controller architecture. [ABSTRACT FROM AUTHOR]

A novel speed sensorless indirect field-oriented control for the full-order model of the induction motor is presented. It provides local exponential tracking of smooth speed and flux amplitude reference signals together with local exponential field orientation, on the basis of stator current measurements only and under assumption of unknown constant load torque. Speed estimation is performed through a reduced-order adaptive observer based on the torque current dynamics, while no flux estimate is required for both observation and control purposes. The absence of the flux model in the proposed algorithm allows for simple and effective time-scale separation between the speed-flux tracking error dynamics (slow subsystem) and the estimation error dynamics (fast subsystem). This property is exploited to obtain a high performance sensorless controller, with features similar to those of standard field-oriented induction motor drives. Moreover, time-scale separation and physically-based decomposition into speed and flux subsystems allow for a simple and constructive tuning procedure. The theoretical analysis based on the singular perturbation method enlightens that a persistency of excitation condition is necessary for the asymptotic stability. From a practical viewpoint, it is related to the well-known observability and instability issues due to a lack of back-emf signal at zero-frequency excitation. A flux reference selection strategy has been developed to guarantee Persistency of excitation in every operating condition. Extensive simulation and experimental tests confirm the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

The paper presents a new method for the estimation of the electric parameters of induction motors (IMs). During the identification process the rotor flux is also estimated. The procedure relies on standstill tests performed with a standard drive architecture, hence, it is suitable for self-commissioning drives. The identification scheme is based on the model reference adaptive system (MRAS) approach. A novel parallel adaptive observer (PAO) has been designed, starting from the series-parallel Kreis-selmeier observer. The most interesting features of the proposed method are the following: 1) rapidity and accuracy of the identification process; 2) low-computational burden; 3) excellent noise rejection, thanks to the adopted parallel structure; 4) avoidance of incorrect parameter estimation due to magnetic saturation phenomena, thanks to recursive rotor flux monitoring. The performances of the new scheme are shown by means of simulation and experimental tests. The estimation results are validated by comparison with a powerful batch nonlinear least square (NLS) method and by evaluating the steady-state mechanical curve of the IM used in the tests. [ABSTRACT FROM AUTHOR]

A new indirect stator flux field-oriented output feedback control for Doubly Fed Induction Machine is presented. It assures global exponential torque tracking and stabilization of the stator-side power factor at unity level, provided that electric machine physical constraints are satisfied. Based on the inner torque control system, a speed tracking controller, with load torque compensation is designed using passivity approach. In contrast to existing solutions, the stator voltage vector oriented reference frame is adopted in order to improve robustness properties with respect to induction machine parameters variation. To achieve smooth connection of electric machine to the line grid a synchronization algorithm is developed for excitation stage of the Doubly Fed Induction Machine operation. The solution proposed requires measurements of line voltages, rotor currents, rotor position and speed. Intensive experimental studies demonstrate high dynamic performance capabilities of the control algorithm proposed. Index Terms—AC generators; AC motor drives; electric machines; Lyapunov-based control; power control; torque control; velocity control. [ABSTRACT FROM AUTHOR]

Abstract: An innovative indirect field-oriented output feedback controller for induction motor drives is presented. This solution is based on output feedback since only speed and position of the motor shaft are measured, while current sensors are avoided. This approach is suitable for low cost applications, where the position sensor cannot be removed to guarantee accurate position tracking. The proposed method provides global asymptotic tracking of smooth position and flux references in presence of unknown constant load torque. It is based on the natural passivity of the electromagnetic part of the machine and it guarantees asymptotic decoupling of the induction motor mechanical and electrical subsystems achieving at the same time asymptotic field orientation. Lyapunov analysis and nonlinear control design have been adopted to obtain good position tracking performances and effective torque–flux decoupling. The cascaded structure of the controller allows performing a constructive tuning procedure for speed and position control loops. Results of experimental tests are presented to demonstrate the tracking and robustness features of the proposed solution. [Copyright &y& Elsevier]