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A high-performance electrical drive with a permanent magnet synchronous motor (PMSM) should ensure superior dynamic properties, reasonable load torque compensation, and robustness against parameter variations. The performance investigation related to the dynamic properties is usually made using the step response indicators and integral performance indexes. The robustness evaluation is more complex than performance analysis since there are no simple tools for this purpose, especially if it is done experimentally to consider nonlinearities and unmodeled dynamics of the plant. In this article, sensitivity functions suitable for investigating the closed-loop control systems in the time and the frequency domain are proposed, explained, and experimentally verified. Three control schemes designed for the speed control of PMSM are analyzed: a cascade control structure, robust two-degrees-of freedom speed controller, and a cascade-free constrained state feedback controller. Studies are conducted for experimental responses of the drive in the time and the frequency domain to investigate the sensitivity of the PMSM drive against the moment of inertia and load variations. Analysis of nonlinearities influence on the developed sensitivity functions is also presented. [ABSTRACT FROM AUTHOR]

The application of optimization algorithms to adaptive motion control is proposed in this paper. In order to provide optimal system response, optimization algorithm is used as adjustment mechanism of controller coefficients. Most of optimization algorithms are not able to work in continuous optimization mode and with non-constant search space (i.e. dataset). For this reason, the introduction of a novel approach, Adaptive Procedure for Optimization Algorithms (APOA), that allows to apply most of optimization algorithms to adaptation process seems to be necessary. Such an algorithm is innovative, due to its universality in terms of applied optimization algorithm. Its application allows to obtain optimal motion control in modern electric drives. The proposed APOA is presented together with the novel desired-response adaptive system (DRAS) approach for repetitive processes. This solution provides unchanged system response regardless of plant parameters variation or external disturbances. The developed adaptive approach based on optimization algorithm is implemented in permanent magnet synchronous motor (PMSM) drive to adapt state feedback speed controller during moment of inertia variations. Since the proposed DRAS is model-free approach, the adaptation procedure is immune to issues related to plant parameters mismatch. The obtained results prove that proposed adaptive speed controller for PMSM assures desired system response regardless of the moment of inertia variation. • A novel adaptation procedure for optimization algorithms has been proposed. • Analysis of objective function in coefficient domain was presented. • The comparison of local- and global-optimization algorithms was included. • Proposed approach allows for a constant response regardless of plant parameters. [ABSTRACT FROM AUTHOR]

This paper presents constrained state feedback speed control of a permanent-magnet synchronous motor (PMSM). Based on classical control theory, nonlinear state-space model of PMSM is developed. A simple linearization procedure is employed to design a linear state feedback controller (SFC). Digital redesign of a SFC is carried out to achieve discrete form suitable for implementation in a DSP. Model predictive approach is used to a posteriori constraint introduction into control system. It overcomes limitations of motion control system with nonconstrained SFC resulting in low dynamic properties. The novel concept utilizes machine voltage equation model to calculate the boundary values of control signals which provide permissible values of the future state variables. Secondary control objectives such as zero $d$-axis current are included. Simulation and experimental results are presented to validate the proposed constrained state feedback control algorithm in comparison to nonconstrained state feedback control and cascade control structure, respectively. [ABSTRACT FROM PUBLISHER]

This article focuses on the application of nature-inspired optimization algorithm for adaptive speed control of permanent synchronous motor (PMSM) drive with variable parameters. In the proposed approach, a state feedback controller (SFC) is utilized for speed control of the PMSM, while on-line adaptation of its coefficients is made with the help of Artificial Bee Colony (ABC) algorithm. Since ABC is the first time applied for adaptation of SFC, its necessary modifications are depicted with details. In order to assure stability and robustness of the considered control scheme, a linear–quadratic optimization method is employed during adaptation. To ensure repeatable response of the plant regardless of parameter's variation, a model reference adaptive system (MRAS) is used. The proposed approach is examined in simulation and experimental studies, including variable moment of inertia, non-measurable load torque and unmodelled friction. These confirm that adaptive SFC based on ABC noticeably improves control performance in comparison to a non-adaptive one. • A novel approach for adaptive speed control of PMSM drive has been proposed. • Artificial Bee Colony algorithm was extended for continuous adaptive operation. • Proposed approach improves control performance in comparison to non-adaptive one. [ABSTRACT FROM AUTHOR]