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The generalized modulation strategy of the conventional matrix converter (MC), i.e., three-leg MC, was previously developed using the duty-cycle space vector (DCSV) representation of switch duty cycles. This paper uses the DCSV representation of switch duty cycles to develop a new formulation of the generalized modulation strategy for one-, three-, and four-leg MCs. The four-leg MC, unlike the three-leg MC, is capable of providing a neutral connection at the output side of the MC. The neutral connection enables the four-leg MC to operate as a power supply providing a balanced, regulated, and fixed-frequency voltage for balanced and unbalanced loads, including the single-phase load. The generalized modulation strategy of the four-leg MC in this paper is developed in the context of power supply applications. Using the new formulation of the generalized modulation strategy, the direct duty-cycle modulation strategies based on DCSV calculation are developed for one-, three-, and four-leg MCs, which are referred to as the DCSV-based modulation strategy of the MC. Part I of the paper formulates the DCSV-based modulation strategy of the three- and four-leg MCs. [ABSTRACT FROM PUBLISHER]

In this paper, a discrete-time neural network for solving convex quadratic programming (QP) problems in constrained model predictive control (MPC) technology is investigated and implemented on a digital signal processor (DSP) device. This makes it possible to apply MPC technology to local control for high-dimensional multiple-input–multiple-output systems. The convergence issue of the discrete-time neural network is first studied. By choosing a proper error function, a sufficient condition is obtained under which the neural network converges to the exact optimal solution globally. This is the theoretical basis of this paper. An integrated hardware and software design method to implement the neural network on a DSP chip as a universal QP solver is then developed. With the QP solver handling the computational tasks in MPC problems, a general DSP-based MPC controller is achieved. A prototype system is built on a TMDSEVM6678L DSP development board. It is then applied to an air-separation-unit system and achieves satisfactory control performance. This verifies the effectiveness of the whole design. [ABSTRACT FROM PUBLISHER]

In this paper, a real-time implementation of selective harmonic elimination pulsewidth modulation (SHEPWM) using generalized Hopfield neural network (GHNN) as applied to the single-phase inverter is designed and discussed. Finding the location of the switching instants in the case of SHEPWM involves the solution of a set of nonlinear algebraic transcendental equations. The problem is redrafted as an optimization problem, and it is solved by using GHNN. Though, in principle, the $n$ number of harmonics can be eliminated using the presented methodology, this paper tries for the case of the elimination of the 5th-, 7th-, 11th-, and 13th-order harmonics while retaining the desired fundamental. An energy function is formulated for this problem and the set of ordinary differential equations (ODEs) describing the behavior of GHNN is obtained. A program in KEIL C was developed to solve these systems of ODEs by the Runge–Kutta fourth-order method to give the switching instants for continuously varying modulation indexes $(M)$. A MATLAB simulation was carried out, and an experimental setup was also constructed in order to validate the simulated results. The fast Fourier transform analysis of the simulated output voltage waveform and the experimental output voltage waveform confirms the effectiveness of the proposed method. Hence, the proposed method proves that it is much applicable in the industrial applications by virtue of its suitability in real-time applications. [ABSTRACT FROM PUBLISHER]

In this paper, we consider the friction compensation problem for a class of mechanical systems. The friction behavior is described by a nonlinear dynamical model. Since it is difficult to know the nonlinear parts in the frictional model accurately, two neural networks (NNs) are employed in the proposed intelligent controller. Due to the learning capability of the NNs, the designed NN controller can compensate the effects of the nonlinear friction. Stability of the thus proposed learning control system is guaranteed by a rigid proof. Simulation and experimental results are provided to verify the effectiveness of the proposed intelligent scheme. [ABSTRACT FROM PUBLISHER]

Machine prognosis is a significant part of condition-based maintenance and intends to monitor and track the time evolution of a fault so that maintenance can be performed or the task can be terminated to avoid a catastrophic failure. A new prognostic method is developed in this paper using adaptive neuro–fuzzy inference systems (ANFISs) and high-order particle filtering. The ANFIS is trained via machine historical failure data. The trained ANFIS and its modeling noise constitute an mth-order hidden Markov model to describe the fault propagation process. The high-order particle filter uses this Markov model to predict the time evolution of the fault indicator in the form of a probability density function. An online update scheme is developed to adapt the Markov model to various machine dynamics quickly. The performance of the proposed method is evaluated by using the testing data from a cracked carrier plate and a faulty bearing. Results show that it outperforms classical condition predictors. [ABSTRACT FROM AUTHOR]

On-road sensor systems installed on freeways are used to capture traffic flow data for short-term traffic flow predictors for traffic management, to reduce traffic congestion and improve vehicular mobility. This paper intends to tackle the impractical time-invariant assumptions which underlie the methods currently used to develop short-term traffic flow predictors: 1) the characteristics of current data captured by on-road sensors are assumed to be time invariant with respect to those of the historical data, which is used to developed short-term traffic flow predictors; and 2) the configuration of the on-road sensor systems is assumed to be time-invariant. In fact, both assumptions are impractical in the real world, as the current traffic flow characteristics can be very different from the historical ones, and also the on-road sensor systems are time varying in nature due to damaged sensors or component wear. Therefore, misleading forecasting results are likely to be produced when short-term traffic flow predictors are designed using these two time-invariant assumptions. To tackle these time-invariant assumptions, an intelligent particle swarm optimization (IPSO) algorithm is proposed to develop short-term traffic flow predictors by integrating the mechanisms of PSO, neural network and fuzzy inference system, to adapt to the time-varying traffic flow characteristics and the time-varying configurations of the on-road sensor systems. The proposed IPSO was applied to forecast traffic flow conditions on a section of freeway in Western Australia, whose traffic flow information can be captured on-line by the on-road sensor system. These results clearly demonstrate the effectiveness of using the proposed IPSO for real-time traffic flow forecasting based on traffic flow data captured by on-road sensor systems. [ABSTRACT FROM PUBLISHER]

This paper attempts to improve the dynamic performance of a shunt-type active power filter. The predictive and adaptive properties of artificial neural networks (ANNs) are used for fast estimation of the compensating current. The dynamics of the dc-link voltage is utilized in a predictive controller to generate the first estimate followed by convergence of the algorithm by an adaptive ANN (adaline) based network. Weights in adaline are tuned to minimize the total harmonic distortion of the source current. Extensive simulations and experimentations confirm the validity of the proposed scheme for all kinds of load (balanced and unbalanced) for a three-phase three-wire system. [ABSTRACT FROM AUTHOR]

Artificial neural network models have been successfully applied to solve a job-shop scheduling problem (JSSP) known as a Nonpolynomial (NP-complete) constraint satisfaction problem. Our main contribution is an improvement of the algorithm proposed in the literature. It consists in using a procedure optimizing the initial value of the starting time. The aim is to speed a Hopfield Neural Network (HNN) and therefore reduce the number of searching cycles. This new heuristic provides several advantages; mainly to improve the searching speed of an optimal or near optimal solution of a deterministic JSSP using HNN and reduce the makespan. Simulation results of the proposed method have been performed on various benchmarks and compared with current algorithms such as genetic algorithm, constraint satisfaction adaptive neural networks, simulated annealing, threshold accepting, flood method, and priority rules such as shortest processing time (SPT) to mention a few. As the simulation results show, and Brandts algorithm, combined with the proposed heuristic method, is efficient with respect to the resolution speed, quality of the solution, and the reduction of the computation time. [ABSTRACT FROM AUTHOR]