Giuseppe Leonardo Cascella

A Fast Adaptive Memetic Algorithm for Online and Offline Control Design of PMSM Drives

A fast adaptive memetic algorithm (FAMA) is proposed which is used to design the optimal control system for a permanent-magnet synchronous motor. The FAMA is a memetic algorithm with a dynamic parameter setting and two local searchers adaptively launched, either one by one or simultaneously, according to the necessities of the evolution. The FAMA has been tested for both offline and online optimization. The former is based on a simulation of the whole system-control system and plant-using a model obtained through identification tests. The online optimization is model free because each fitness evaluation consists of an experimental test on the real motor drive. The proposed algorithm has been compared with other optimization approaches, and a matching analysis has been carried out offline and online. Excellent results are obtained in terms of optimality, convergence, and algorithmic efficiency. Moreover, the FAMA has given very robust results in the presence of noise in the experimental system

An Adaptive Multimeme Algorithm for Designing HIV Multidrug Therapies

This paper proposes a period representation for modeling the multidrug HIV therapies and an adaptive multimeme algorithm (AMmA) for designing the optimal therapy. The period representation offers benefits in terms of flexibility and reduction in dimensionality compared to the binary representation. The AMmA is a memetic algorithm which employs a list of three local searchers adaptively activated by an evolutionary framework. These local searchers, having different features according to the exploration logic and the pivot rule, have the role of exploring the decision space from different and complementary perspectives and, thus, assisting the standard evolutionary operators in the optimization process. Furthermore, the AMmA makes use of an adaptation which dynamically sets the algorithmic parameters in order to prevent stagnation and premature convergence. The numerical results demonstrate that the application of the proposed algorithm leads to very efficient medication schedules which quickly stimulate a strong immune response to HIV. The earlier termination of the medication schedule leads to lesser unpleasant side effects for the patient due to strong antiretroviral therapy. A numerical comparison shows that the AMmA is more efficient than three popular metaheuristics. Finally, a statistical test based on the calculation of the tolerance interval confirms the superiority of the AMmA compared to the other methods for the problem under study

Optimization of Delayed-State Kalman-Filter-Based Algorithm via Differential Evolution for Sensorless Control of Induction Motors

This paper proposes the employment of the differential evolution (DE) to offline optimize the covariance matrices of a new reduced delayed-state Kalman-filter (DSKF)-based algorithm which estimates the stator-flux linkage components, in the stationary reference frame, to realize sensorless control of induction motors (IMs). The DSKF-based algorithm uses the derivatives of the stator-flux components as mathematical model and the stator-voltage equations as observation model so that only a vector of four variables has to be offline optimized. Numerical results, carried out using a low-speed training test, show that the proposed DE-based approach is very promising and clearly outperforms a classical local search and three popular metaheuristics in terms of quality of the final solution for the problem considered in this paper. A novel simple stator-flux-oriented sliding mode (SFO-SM) control scheme is online used in conjunction with the optimized DSKF-based algorithm to improve the robustness of the sensorless IM drive at low speed. The SFO-SM control scheme has closed loops of torque and stator-flux linkage without proportional-plus-integral controllers so that a minimum number of gains has to be tuned.

Sliding Mode Neuro-Adaptive Control of Electric Drives

An innovative variable-structure-systems-based approach for online training of neural network (NN) controllers as applied to the speed control of electric drives is presented. The proposed learning algorithm establishes an inner sliding motion in terms of the controller parameters, leading the command error towards zero. The outer sliding motion concerns the controlled electric drive, the state tracking error vector of which is simultaneously forced towards the origin of the phase space. The equivalence between the two sliding motions is demonstrated. In order to evaluate the performance of the proposed control scheme and its practical feasibility in industrial settings, experimental tests have been carried out with electric motor drives. Crucial problems such as adaptability, computational costs, and robustness are discussed. Experimental results illustrate that the proposed NN-based speed controller possesses a remarkable learning capability to control electric drives, virtually without requiring a priori knowledge of the plant dynamics and laborious startup procedures

Surrogate assisted local search in PMSM drive design

Purpose – This paper aims to propose a reliable local search algorithm having steepest descent pivot rule for computationally expensive optimization problems. In particular, an application to the design of Permanent Magnet Synchronous Motor (PMSM) drives is shown.Design/methodology/approach – A surrogate assisted Hooke‐Jeeves algorithm (SAHJA) is proposed. The SAHJA is a local search algorithm with the structure of the Hooke‐Jeeves algorithm, which employs a local surrogate model dynamically constructed during the exploratory move at each step of the optimization process.Findings – Several numerical experiments have been designed. These experiments are carried out both on the simulation model (off‐line) and at the actual plant (on‐line). Moreover, the off‐line experiments have been considered in non‐noisy and noisy cases. The numerical results show that use of the SAHJA leads to a saving in terms of computational cost without requiring any extra hardware components.Originality/value – The surrogate approa...

Prudent-Daring vs tolerant survivor selection schemes in control design of electric drives

This paper proposes and compares two approaches to defeat the noise due the measurement errors in control system design of electric drives. The former is based on a penalized fitness and two cooperative-competitive survivor selection schemes, the latter is based on a survivor selection scheme which makes use of the tolerance interval related to the noise distribution. These approaches use adaptive rules in parameter setting to execute both the explicit and the implicit averaging in order to obtain the noise defeating in the optimization process with a relatively low number of fitness evaluations. The results show that the two approaches differently bias the population diversity and that the first can outperform the second but requires a more accurate parameter setting.

Adaptive sliding-mode observer for field oriented sensorless control of SPMSM

This paper proposes a new scheme to guarantee speed sensorless control and optimal field orientation of surface permanent magnet synchronous motors (SPMSMs) even if parameter deviations occur and initial rotor position is unknown. A novel adaptive sliding-mode observer is used for field oriented speed sensorless control. First the induced e.m.f. components are observed using the sliding-mode technique with an adaptive switching gain. Then a low-pass filter with an adaptive amplitude compensator, that works as derivative observer, is used to get rid of the chattering noise of estimated e.m.f. components and to calculate rotor speed and position. The current components in rotor reference frame are estimated from the active and reactive electromagnetic torques calculated in both the stationary reference frame and the estimated rotor one. The latter is also used as control reference frame. Inverter, filters and observer cause unavoidable and unpredictable time-delay in rotor position estimation, so that the control reference frame is delayed as to the actual rotor one. As a consequence, field orientation and maximum torque/current ratio do not occur when the desired field oriented current references are imposed in the control reference frame. An original field orientation PI controller is used to provide the appropriate current references in the control reference frame. Test results are presented to prove the approach effectiveness.

On-line simplex-genetic algorithm for self-commissioning of electric drives

This paper deals with the self-commissioning of electric drives. To improve the performance of self-commissioning on currently available on industrial drives, an on-line auto-tuning based on a hybrid genetic algorithm is proposed. This strategy integrates the simplex method, local searcher, in a genetic framework, global searcher, in order to speed up the convergence. Moreover it is very reliable because experimentally tests each possible solution, consequently the final result is not affected by the accuracy of the motor model. Finally, the proposed on-line hybrid optimization can be embedded as a fully-automated tool without any extra-hardware on industrial drives. Extensive experimental results prove the effectiveness of the proposed approach not only in comparison with conventional commissioning, but also when compared with further accurate hand-calibration

Adaptive Control of Electric Drives Using Sliding-Mode Learning Neural Networks

New sliding mode control theory-based method for on-line learning in multilayer neural controllers as applied to the speed control of electric drives is presented. The proposed algorithm establishes an inner sliding motion in terms of the controller parameters, leading the command error towards zero. The outer sliding motion concerns the controlled electric drive, the state tracking error vector of which is simultaneously forced towards the origin of the phase space. The equivalence between the two sliding motions is demonstrated. In order to evaluate the performance of the proposed control scheme and its practical feasibility in industrial settings, experimental tests have been carried out with electric motor drives.

Application of Memetic Differential Evolution frameworks to PMSM drive design

This paper proposes the application of Memetic Algorithms employing Differential Evolution as an evolutionary framework in order to achieve optimal design of the control system for a permanent-magnet synchronous motor. Two Memetic Differential Evolution frameworks have been considered in this paper and their performance has been compared to a standard Differential Evolution, a standard Genetic Algorithm and a Memetic Algorithm presented in literature for solving the same problem. All the algorithms have been tested on a simulation of the whole system (control system and plant) using a model obtained through identification tests. Numerical results show that the Memetic Differential Evolution frameworks seem to be very promising in terms of convergence speed and has fairly good performance in terms of final solution detected for the real-world problem under examination. In particular, it should be remarked that the employment of a meta-heuristic local search component during the early stages of the evolution seems to be very beneficial in terms of algorithmic efficiency.