This work presents a novel optimization method for the implementation of finite-state model-based predictive current controllers in electrical drives. The proposal avoids the usual exhaustive search to find the control action, reducing the computational burden. The method is based on physical considerations of the power converter voltage vectors and is easy to implement on digital signal processors. The proposal is applied to a five-phase induction machine. Experimental results are compared with those obtained by a standard model-based controller, showing the feasibility of the proposal and the improvements in terms of sampling time reduction and control accuracy.

In this paper, an adaptive cost function FCSMPC is derived from newly obtained results concerning the distribution of figures of merits used for the assessment of stator current model-based control of multi-phase induction machines. A parameter analysis of FCSMPC is carried out for the case of a six-phase motor. After extensive simulation and Pareto screening, a new structure has been discovered linking several figures of merit. This structure provides an simple explanation for previously reported results concerning the difficulty of cost function tuning for FCSMPC. In addition, the newly discovered link among figures of merit provides valuable insight that can be used for control design. As an application, a new cost function design scheme is derived and tested. This new method avoids the usual and cumbersome procedure of testing many different controller parameters.

Optimizing the cost function in predictive control of multi-phase drives is computationally intensive. This poses a challenge since the required sampling period for drives falls within the microsecond range. Numerous methods have been proposed in the literature to address this computational demand. This paper reviews recent proposals for multi-phase drives of various kinds. A structured classification of the proposals is provided. Furthermore, an integrated framework is employed to cohesively present and connect previously unrelated methods. Key elements such as Allowed Control Set, inter-sample modulation, and weighting factor use are identified. New developments in multi-vector and single-vector approaches are discussed. Practical limitations for each approach are also considered.

Constant torque motors are needed for rotary screw compressors that are used for cooling and other applications. In such systems, the torque demanded by the load is approximately the same over the whole range of mechanical speeds. In this paper, the use of multi-phase induction machines is investigated for this type of application. The requirement of low stator current distortion is considered. A scheduled approach is used to provide the best possible tuning for each operating point, similar to the concept of gain scheduling control. Simulations and laboratory tests are used to assess the proposal and compare it with finite-state predictive control. The experiments show that a trade-off situation appears between the ripple content in stator currents in the torque-producing and harmonic planes. As a consequence, the controller tuning appears as an important step. The proposed method considers various figures of merit with cost function tuning, resulting in a scheduled scheme that provides improved results. It is shown that the approach leads to a reduction in current ripple, which is advantageous for this particular application.

Energy load forecasting for optimization of chiller operation is a topic that has been receiving increasing attention in recent years. From an engineering perspective, the methodology for designing and deploying a forecasting system for chiller operation should take into account several issues regarding prediction horizon, available data, selection of variables, model selection and adaptation. In this paper these issues are parsed to develop a neural forecaster. The method combines previous ideas such as basis expansions and local models. In particular, hyper-gaussians are proposed to provide spatial support (in input space) to models that can use auto-regressive, exogenous and past errors as variables, constituting thus a particular case of NARMAX modelling. Tests using real data from different world locations are given showing the expected performance of the proposal with respect to the objectives and allowing a comparison with other approaches.