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.

Energy efficiency is one of the most important parameters in transportation electrification. It allows to improve the production rate due to longer operation without charging or decrease the cost related to transportation. To provide collision-free operation in unknown or various environment, the local path planning algorithm should be considered. An Artificial Potential Field (APF) algorithm is commonly used for this task, however it provides unsmooth and oscillating motion of autonomous ground vehicle (AGV), and is prone to being trapped in a local minimum, e.g, dead-end. In such a case, the energy used to achieve the goal position is higher than necessary. In this paper, the energy-efficient local path planning algorithm is proposed. Future movement prediction has been introduced to APF to allow AGV to bypass obstacles in advance. A novel method for local minimum avoidance is introduced. It is based on placement of virtual obstacles called top quarks in critical areas. These obstacles provide additional repulsive force for the APF based path planner. Considering the predicted stagnation-free path of the AGV, the new temporary goal for APF is selected. Such a combination allows to reduce traveled route length, improve its smoothness, and bypass local minima. The proposed Predictive Artificial Potential Field (PAPF) algorithm has been examined using Husarion ROSbot 2.0 PRO mobile robot, and the obtained results in form of videos are also attached as supplementary files. In comparison to original APF, the proposed path planning algorithm allows to reduce the used electric power by 21.4 %. PAPF provided a shorter path by up to 8.73 % and shorter time to reach the goal position by up to 40.23 %. The movement of the AGV is also much smoother in a case of usage of the proposed algorithm, and the proposed top quarks -based local minimum avoidance mechanism allows to bypass the local minima.

Mobile robots in industry are commonly used in warehouses and factories. To achieve the highest production rate, requirements for path planning algorithms have caused researchers to pay significant attention to this problem. The artificial potential field algorithm, which is a local path planning algorithm, has been previously modified to obtain higher smoothness of path, to solve the stagnation problem and to jump off the local minimum. The last itemized problem is taken into account in this paper—local minimum avoidance. Most of the modifications of artificial potential field algorithms focus on a mechanism to jump off a local minimum when robots stagnate. From the efficiency point of view, the mobile robot should bypass the local minimum instead of jumping off it. This paper proposes a novel artificial potential field supported by augmented reality to bypass the upcoming local minimum. The algorithm predicts the upcoming local minimum, and then the mobile robot’s perception is augmented to bypass it. The proposed method allows the generation of shorter paths compared with jumping-off techniques, due to lack of stagnation in a local minimum. This method was experimentally verified using a Husarion ROSbot 2.0 PRO mobile robot and Robot Operating System in a laboratory environment.

This paper focuses on application of novel nature-inspired optimization algorithms to auto-tuning state feedback speed controller (SFC) for permanent magnet synchronous motor (PMSM). An artificial bee colony (ABC) algorithm and a flower pollination algorithm (FPA) are employed to obtain weighting matrices Q and R elements in linear quadratic regulator (LQR) optimization process. Information concerning principles of operation and influence of the control parameters on the both optimization algorithms are presented. Necessary modifications of ABC and FPA required for their successful application in a field of automatic control of electrical drive are depicted, including introduction of constraint-handling method that assures proper and safety operation of the motor and construction of performance indexes to provide the fulfillment of specified requirements. To the best our knowledge, this is the first time that FPA is applied to auto-tuning process of SFC. On the basis of numerous tests, including simulation and experimental results, it is shown that the ABC is the most useful among the two optimization algorithms to obtain a better performance of the PMSM drive.