This article proposes a novel type of the 2nd and 4th harmonic torque suppression methods during conventional single-phase open-circuit fault-tolerant operation when considering the 3rd harmonic back-EMF of permanent magnet synchronous motor (PMSM). These suppression methods are based on injecting the 3rd harmonic currents into the two remaining healthy phases, which produces harmonic torque of opposite phase to cancel the original one. The suppression can have three different constraints: maintaining the average torque, eliminating the 2nd harmonic torque, and eliminating the 4th harmonic torque. They cannot be achieved simultaneously. Combining any two of them leads to a particular injection method. Three particular methods are developed and their performance are investigated by theoretical analysis and compared against the conventional one. The proposed fault tolerant control strategies are applicable to various fault-tolerant inverter topologies by using these novel phase current references, whereas the current tracking algorithms could be different. To ease validation, a four-leg inverter topology is selected to implement such injection methods to a three-phase star-connected 10-pole/9-slot PMSM. The experiments validate that tracking the proposed current references can effectively suppress the harmonic torque, which confirms the theoretical analysis.

This paper proposes adaptive torque ripple suppression methods by utilizing resonant controllers during single-phase open-circuit fault-tolerant operation of three-phase star-connected permanent magnet synchronous motor (PMSM). A zero sequence current is supposed to be injected to maintain the post-fault torque same as pre-fault torque. According to post-fault PMSM model, feedforward voltage compensation can be adopted to inject the zero sequence current and hence attenuate the post-fault speed and torque ripples in vector control framework. However, the feedforward voltage compensation which is related to phase resistance and leakage inductance may be inaccurate due to temperature variation, etc., and hence an unexpected 2nd harmonic torque ripple still exists. In this paper, adaptive suppression methods of the 2nd harmonic torque are proposed: the first one constructs a novel closed current loop of zero axis and the second one modifies the controllers in dq axis current loop. These methods are based on resonant controllers which can adaptively compensate the voltage needed for the zero sequence current injection. Experiments are implemented based on a four-leg inverter, and the performance comparison between feedforward compensation and adaptive compensation validate that the proposed methods can effectively suppress the post-fault torque and speed ripple.

The surging popularity of adopting industrial robots in smart manufacturing has led to an increasing trend in the simultaneous improvement of the energy costs and operational efficiency of motion trajectory. Motivated by this, multi-objective trajectory planning subject to kinematic and dynamic constraints at multiple levels has been considered as a promising paradigm to achieve the improvement. However, most existing model-based multi-objective optimization algorithms tend to come out with infeasible solutions, which results in non-zero initial and final acceleration. Popular commercial software toolkits applied to solve multi-objective optimization problems in actual situations are mostly based on the fussy conversion of the original objective and constraints into strict convex functions or linear functions, which could induce a failure of duality and obtain results exceeding limits. To address the problem, this paper proposes a time-energy optimization model in a phase plane based on the Riemann approximation method and a solution scheme using an iterative learning algorithm with neural networks. We present forward-substitution interpolation functions as basic functions to calculate indirect kinematic and dynamic expressions introduced in a discrete optimization model with coupled constraints. Moreover, we develop a solution scheme of the complex trajectory optimization problem based on artificial neural networks to generate candidate solutions for each iteration without any conversion into a strict convex function, until minimum optimization objectives are achieved. Experiments were carried out to verify the effectiveness of the proposed optimization solution scheme by comparing it with state-of-the-art trajectory optimization methods using Yalmip software. The proposed method was observed to improve the acceleration control performance of the solved robot trajectory by reducing accelerations exceeding values of joints 2, 3 and 5 by 3.277 rad/s², 26.674 rad/s², and 7.620 rad/s², respectively.

Visual tracking with a mobile manipulator is required to continuously keep a moving target near the center of the camera’s field of view. Conventional methods generally employ either the mobile base or the manipulator alone for target tracking. However, base-only tracking provides limited task-space motion capability, whereas arm-only tracking is restricted by the finite workspace and joint limits of the manipulator. To address this problem, this paper proposes integrating base–camera alignment and gain-scheduling visual servoing controller into a unified whole-body control frame to generate coordinated motion of the mobile base and the robotic arm for visual tracking. The mobile base and six-joint manipulator are represented as an eight-degree of freedom kinematic system, and their velocities are determined simultaneously by a quadratic program incorporating joint and velocity constraints. Auxiliary objectives regulate arm manipulability, transient motion allocation, and base–camera alignment. The proposed method enables the robot to reactively track a moving target in a three-dimensional workspace while maintaining the desired target distance. Experiments on a mobile manipulator demonstrate coordinated base–arm motion for the tested target-tracking tasks. In an illustrative controller-level step response, gain scheduling reduces the settling time from 7.99 s to 2.39 s relative to the previous fixed-gain configuration.