This article proposes an adaptive sliding-mode control (ASMC) with a modified reduced-order proportional integral observer (MROPIO) to provide speed control for a permanent magnet synchronous motor against unknown disturbances, such as system parameter uncertainties and external load torque variation. The ASMC method, based on a new sliding-mode reaching law, improves chattering in the control signal and reduces the time required for the system trajectory to reach the sliding-mode surface. The MROPIO estimates unknown disturbances and provides feed-forward compensation for the ASMC, thus overcoming incorrect estimations of unknown disturbances during speed jumps can cause overshoots in the speed response. All the proposed methods were implemented on an industrial motor drive. The experimental results confirm the validity of the proposed method for practical applications.

Due to the fast dynamic response and ability to handle the constraints, model predictive control (MPC) is becoming an exciting and widely applied approach for permanent magnet synchronous motor. However, the control performance of a conventional MPC is significantly affected by the model parameter mismatches. In addition, the high computational volume is also a limiting factor of MPC. This article $^{\prime }$ s main objective is to solve these problems by proposing an advanced control structure. First, the parameter mismatches are estimated online by a discrete-time mechanical parameter observer, then a robust adaptive model predictive speed control (RA-MPSC) is designed to suppress the influence of parameter mismatches. Secondly, a recurrent neural network based algorithm is introduced to compute the RA-MPSC control law by solving an optimization problem in real-time. Lastly, the simulation and experimental results are presented to validate the performance of the proposed method.

This article proposes a robot calibration method using an extended Kalman filter (EKF) and an artificial neural network (ANN) based on a butterfly and flower pollination algorithm (ANN-BFPA) to improve the robot’s absolute pose (position and orientation) accuracy. After establishing a geometric error model, the EKF, a robust optimization algorithm for a nonlinear system with Gaussian noise, was used to estimate geometric parameter errors and compensate for geometric errors. However, nongeometric errors caused by joint clearance, gear backlash, and link deflection could still affect the pose accuracy and interfere with the correctness of the model. Therefore, the ANN-BFPA was proposed to compensate for these errors. The ANN model was used to establish the complex relationship between joint lengths and pose error. In addition, BFPA was used to optimize weights and bias of the neural network. The efficiency of the proposed calibration method was evaluated using a Stewart platform. Experimental results demonstrated that the proposed method significantly improved the robot’s pose accuracy and showed better performance than previous techniques.

This paper presents the design and implementation of a multiple-axis motion control chip using a field-programmable gate array (FPGA). This multiple-axis motion control chip is designed to control a multiple-axis motion system such as a robotic arm manipulator or a computer numerical control machine. The proposed motion control chip has many functions. These include velocity profile generation, interpolation calculation, inverse kinematics calculation, proportional-integral-derivative control, feedback count, pulse integration, data conversion, clock generation, and external interfacing. These functions are designed using the VHSIC hardware description language and implemented on an FPGA according to the electronic design automation design methodology. This allows for a highly sampled, accurate, flexible, compact, low-power, and low-cost motion control system. The detailed design of the proposed motion control chip is presented. A multiple-axis motion control system using this chip is implemented, and its performance is measured. The multiple-axis motion control system is implemented on a platform consisting of a chip-based multiple-axis motion controller, analog ac servo drivers, a selective compliant assembly robot arm robot, and a host personal computer.

Absolute magnetic encoders (AMEs) use two magnets: a ring multi-polar magnet generating highresolution and improving the accuracy for the encoder, and a bipolar magnet in the center calculating the number cycle of multi-polar magnet signals. The phase outputs of these AMEs are tracked from the sinusoidal signals of the multi-polar magnet. However, these sine/cosine signals are disturbed by amplitude differences, offsets, phase-shift, harmonic components, and random noise. In order to solve this problem, we present an adaptive linear neuron based on a third-order phase-locked loop (ALN-PLL) to improve the accuracy of AMEs. The proposed approach consists of two main parts: The first part is an ALN algorithm that uses the phase feedback of the third-order PLL in order to build the mathematical model of input signals, and then reject the disturbances. The second part is a third-order PLL that is designed based on a dominant pole approximation algorithm. The proposed PLL can reduce noise and eliminate DC-error during the phase step, frequency step, and frequency ramp. The simulation and experimental results demonstrate the effectiveness of the proposed approach.

To provide reliable position control for a permanent-magnet synchronous motor (PMSM) under conditions of lumped disturbances such as external load torque fluctuation and system parameter variation, an adaptive backstepping sliding-mode control (ABSMC) with a nonlinear disturbance observer (NDO) is proposed. An ABSMC is a non-cascade technique that employs a position-current single-loop control structure rather than a conventional cascade control structure for vector control of the PMSM. This method uses Lyapunov theory to design a control law to ensure the motor position reaches a desired value in a finite period of time and consequently achieves a rapid transient response. The proposed ABSMC incorporates an adaptive convergence gain to avoid a large overshoot under point-to-point position command. The NDO with a nonlinear observer gain is proposed to estimate unknown disturbances and provide feed-forward compensation for the ABSMC, improving robustness and reducing steady-state position error. The proposed methods are applied to an industrial motor drive to confirm their validity in real-world operating environments.