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Successful sensorless collision detection by a robot depends on the accuracy with which the external force/torque can be estimated. Compared with collaborative robots, industrial robots often have larger parameter values of their dynamic models and larger errors in parameter identification. In addition, the friction inside a reducer affects the accuracy of external force estimation. The purpose of this paper is to propose a collision detection method for industrial robots. The proposed method does not require additional equipment, such as sensors, and enables highly sensitive collision detection while guaranteeing a zero false alarm rate.

The error on the calculated torque for a robot in stable motion is analyzed, and a typical torque error curve is presented. The variational characteristics of the joint torque error during a collision are analyzed, and collisions are classified into two types: hard and soft. A pair of envelope-like lines with an effect similar to that of the true envelope lines is designed. By using these envelope-like lines, some components of the torque calculation error can be eliminated, and the sensitivity of collision detection can be improved.

The proposed collision detection method based on envelope-like lines can detect hard and soft collisions during the motion of industrial robots. In repeated experiments without collisions, the false alarm rate was 0 per cent, and in repeated experiments with collisions, the rate of successful detection was 100 per cent. Compared with collision detection method based on symmetric thresholds, the proposed method has a smaller detection delay and the same detection sensitivity for different joint rotation directions.

A collision detection method for industrial robots based on envelope-like lines is proposed in this paper. The proposed method does not require additional equipment or complex algorithms, and highly sensitive collision detection can be achieved with zero false alarms. The proposed method is low in cost and highly practical and can be widely used in applications involving industrial robots.

The brushless direct current motor (BLDCM) is widely accepted and adopted by many industries instead of direct current motors due to high reliability during operation. Brushless direct current (BLDC) has outstanding efficiency as losses that arise out of voltage drops at brushes and friction losses are eliminated. The main factor that affects the performance is temperature introduced in the internal copper core windings. The control of motor speed generates high temperature in BLDC operation. The high temperature is due to presence of ripples in the operational current. The purpose is to present an effective controlling mechanism for speed management and to improve the performance of BLDCM to activate effective management of speed.

The purpose is to present an optimal algorithm based on modified moth-flame optimization algorithm over recurrent neural network (MMFO-RNN) for speed management to improve the performance. The core objective of the presented work is to achieve improvement in performance without affecting the design of the system with no additional circuitry. The management of speed in BLDCM has been achieved through reduction or minimization of ripples encircled with torque of the motor. The implementation ends in two stages, namely, controlling the loop of torque and controlling the loop of speed. The MMFO-RNN starts with error optimization, which arises from both the loops, and most effective values have been achieved through MMFO-RNN protocol.

The parameters are enriched with Multi Resolution Proportional Integral and Derivative (MRPID) controller operation to achieve minimal ripples for the torque of BLDC and manage the speed of the motor. The performance is increased by adopting this technique approximately 12% in comparison with the existing methodology, which is the main contributions of the presented work. The outcomes are analyzed with the existing methodologies through MATLAB Simulink tool, and the comparative analyses suggest that better performance of the proposed system produces over existing techniques, and proto type model is developed and cross verifies the proposed system.

The MMFO-RNN starts with error optimization, which arises from both the loops, and most effective values have been achieved through MMFO-RNN protocol. The parameters are enriched with MRPID controller operation to achieve nil or minimal ripples and to encircle the torque of Brushless Direct Current and manage the speed.

The paper aims to provide an adaptive neural network controller for permanent magnet synchronous motor (PMSM) under direct torque control (DTC) algorithm to minimize the torque ripple and EMI noise.

The design methodology is based on vector control used for electrical machines. MATLAB simulations supported with experimental study under C++ are used.

The simulated and experimental results show that considerable torque ripple as well as current ripple and EMI noise reduction can be achieved by utilizing adaptive neural switching algorithm to fire the inverter supplying the PMSM.

This research is limited to PMSM, however the research can be extended to include other AC motors as well. In addition, the following points can be studied: the effects of harmonics in control signals on the torque ripple can be analyzed; the actual mathematical relation between the torque and flux ripple can be studied to set the flux and torque bands width in reasonable value; different neural network algorithms can be applied to the system to solve the similar problems.

Based on existing DTC control system, it is only required to change the software switching algorithm, to provide smooth torque, given that the switching frequency of the inverter module is more than or equal to 15 MHz and the system is supplied with timers. In addition a relatively higher DC voltage may be required to achieve higher speed compared with the traditional DTC.

In this paper, the stator flux position, and errors due to deviations from reference values of the torque and stator flux are used to select two active vectors while at the same time the absolute value of the torque error and the stator flux position are used neural network structure to adapt the switching of the inverter in order to control the applied average voltage level in such a way as to minimize the torque ripple, so instead of fixed time table structure, a neural network controller is used to calculate the switching time for the selected vectors and no PI controller is used as the case in the traditional space vector modulation. This work is directed to motor drive system designers who seek highly smooth torque performance with EMI noise reduction.

The flexible mode transitions, multiple power sources and system uncertainty lead to challenges for mode transition control of four-wheel-drive hybrid powertrain. Therefore, the purpose of this paper is to improve dynamic performance and fuel economy in mode transition process for four-wheel-drive hybrid electric vehicles (HEVs), overcoming the influence of system uncertainty.

First, operation modes and transitions are analyzed and then dynamic models during mode transition process are established. Second, a robust mode transition controller based on radial basis function neural network (RBFNN) is proposed. RBFNN is designed as an uncertainty estimator to approximate lumped model uncertainty due to modeling error. Based on this estimator, a sliding mode controller (SMC) is proposed in clutch slipping phase to achieve clutch speed synchronization, despite disturbance of engine torque error, engine resistant torque and clutch torque. Finally, simulations are carried out on MATLAB/Cruise co-platform.

Compared with routine control and SMC, the proposed robust controller can achieve better performance in clutch slipping time, engine torque error, vehicle jerk and slipping work either in nominal system or perturbed system.

The mode transition control of four-wheel-drive HEVs is investigated, and a robust controller based on RBFNN estimation is proposed. Compared results show that the proposed controller can improve dynamic performance and fuel economy effectively in spite of the existence of uncertainty.

The purpose of this paper is to describe a new method for sensorless hysteresis direct torque control (HDTC) algorithm for permanent magnet synchronous motor to minimize torque ripple and electromagnetic interference (EMI) noises.

The design methodology is based on space vector modulation of electrical machines with digital vector control. MATLAB simulations supported with experimental study under C++ are used.

The simulation and experimental results of this proposed algorithm show adequate dynamic torque performance and considerable torque ripples reduction as well as lower current ripples, lower EMI noise level as compared to traditional HDTC.

This research is limited to PMSM, however the research can be extended to include induction motor as well. In addition, the actual mathematical relation between the torque ripple and flux ripple can be studied to set the flux and torque bands width in reasonable value, and this relationship can be used to select switching time of the active selected vectors.

The implementation of the proposed algorithm in microcontroller embedded systems is described. It requires no PI controller in the torque control loop In addition, based on existence direct torque control equipment, it is only required to change the software switching algorithm, to provide smooth torque, given that the switching frequency of the inverter module is more than or equal to 15 kHz and the system is supplied with timers.

The algorithm used in this work utilizes the output of two hysteresis controllers used in the traditional HDTC to determine two adjacent switching vectors per one sample time. The algorithm also uses the magnitude of the torque error, magnitude of the flux error and stator flux position to select the switching time for the selected vectors to control the applied average voltage level in such a way that the torque ripple is minimized. The selection of the switching time of the selected active vectors utilizes novel table structure which reduces the complexity of calculation. This work is directed to designers of ac motor drive system who seek smooth torque performance as well as low EMI noise level.

The aim of the paper is to conduct an analytical study of the two new methods of the permanent magnet synchronous motor torque and flux direct control with the predictive non‐linear torque and flux controller.

The method is based on the prediction of the torque and flux error vector in order to minimize the torque ripple and ensure the constant switching frequency.

The proposed methods ensure the torque and flux error vector minimization, reduction of the torque ripples, and constant switching frequency without deterioration of the dynamic properties of the standard direct torque control (DTC).

An innovative predictive DTC method is presented. The correctness of the analysis and main assumptions, as well as the expected final results have been verified in simulation.

Damage induced during drilling of polymer matrix composites depends upon torque during drilling. Modeling of torque with feed rate and its control becomes imminent for damage free drilling of composite laminates. Therefore, the purpose of this paper is to construct a transfer function between drilling torque and feed rate based upon experiments. Thereafter, the torque is controlled by using PID controller.

This paper presents step-by-step procedure to capture complex drilling dynamics of polymer matrix composites in a mathematical model. A glass fiber reinforced plastic (GFRP) composite laminate is drilled at constant feed rate during experimentation. The corresponding time response of torque is recorded. First order, second order and third order transfer functions between torque and feed rate are identified using system identification toolbox of Matlab®. These transfer functions are then converted into state-space models. Experimental verification is performed on GFRP composite laminate. PID controller is designed using Simulink® to track a given reference torque during drilling of polymer matrix composite. The controller is then validated using different reference torque trajectories.

Good match is observed between torque response from state-space models and experiments. Error analysis based on integral absolute error and integral squared error on experimental and simulated response show that third-order system represents the complex drilling dynamics in a better way than first and second-order systems. PID controller effectively tracks given reference trajectories.

Third-order model between torque and feed rate for drilling of composites not available in literature has been presented. PID controller has previously been applied successfully for drilling of conventional materials, this paper extends implementation of PID torque control for drilling of composites.

The principal intention behind the activity is to regulate the speed, current and commutation of the brushless DC (BLDC) motor. Thereby, the authors can control the torque.

In order to regulate the current and speed of the motor, the Multi-resolution PID (MRPID) controller is proposed. The altered Landsman converter is utilized in this proposed suppression circuit, and the obligation cycle is acclimated to acquire the ideal DC-bus voltage dependent on the speed of the BLDC motor. The adaptive neuro-fuzzy inference system-elephant herding optimization (ANFIS-EHO) calculation mirrors the conduct of the procreant framework in families.

Brushless DC motor's dynamic properties are created, noticed and examined by MATLAB/Simulink model. The performance will be compared with existing genetic algorithms.

The presented approach and performance will be compared with existing genetic algorithms and optimization of different structure of BLDC motor.

The direct torque control (DTC) of induction motor (IM) drive is featured by high ripples in the electromagnetic torque and stator flux profiles because they are controlled by two hysteresis regulators. Furthermore, the machine flux is not directly measurable. Hence, it is better to reconstitute it from the instantaneous electrical equations of the machine. Once the stator flux is estimated, we can guarantee a reliable sensorless DTC control. Thus, the purpose of this research work is to ensure fast response and full reference tracking of the IM under sensorless DTC strategy with desired dynamic behavior and low ripple levels.

In this work, an improved DTC strategy, which is DTC_SVM_3L, is suggested. The first step of the designed approach is to substitute the conventional inverter feeding the motor with a three-level inverter because it guarantees reduced switching losses, improved quality of voltage waveform and low-current total harmonic distortion rate. The second aim of this paper is to make the IM operate at a constant switching frequency by using the nearest three vectors-based space vector modulation (SVM) technique rather than hysteresis controllers. The third objective of this study is to conceive a sliding-mode stator flux observer, which can improve the control performances by using a sensorless algorithm to get an accurate estimation, and consequently, increase the reliability of the system and decrease the cost of using sensors. The stability of the proposed observer is demonstrated based on the Lyapunov theory. To overcome the load change disturbance in the proposed DTC control strategy, this paper exhibits a comparative assessment of four speed regulation methods: classical proportional and integral (PI) regulator, fuzzy logic PI controller, particle swarm optimization PI controller and backstepping regulator. The entire control algorithm was tested under different disturbances such as stator resistance and load torque variations.

It was ascertained that the IM, controlled with three-level inverter, exhibits good performances under the proposed DTC-SVM strategy based on a sliding-mode observer. The robustness of the suggested approach against parameter variations is also proved.

The theoretical development of the proposed control strategy is thoroughly described. Then, simulations using Matlab/Simulink software are launched to investigate the merits of the sensorless DTC-SVM command of three-level inverter-fed IM drive with different speed regulators.

This paper aims to propose a new linear parameter varying (LPV) controller for the robot tracking control problem. Using the identification of the robot dynamics in different work space points about modeling trajectory based on the least square of error algorithm, an LPV model for the robotic arm is extracted.

Parameter set mapping based on parameter component analysis results in a reduced polytopic LPV model that reduces the complexity of the implementation. An approximation of the required torque is computed based on the reduced LPV models. The state-feedback gain of each zone is computed by solving some linear matrix inequalities (LMIs) to sufficiently decrease the time derivative of a Lyapunov function. A novel smoothing method is used for the proposed controller to switch properly in the borders of the zones.

The polytopic set of the resulting gains creates the smooth switching polytopic LPV (SS-LPV) controller which is applied to the trajectory tracking problem of the six-degree-of-freedom PUMA 560 robotic arm. A sufficient condition ensures that the proposed controller stabilizes the polytopic LPV system against the torque estimation error.

Smoothing of the switching LPV controller is performed by defining some tolerances and creating some quasi-zones in the borders of the main zones leading to the compressed main zones. The proposed torque estimation is not a model-based technique; so the model variation and other disturbances cannot destroy the performance of the suggested controller. The proposed control scheme does not have any considerable computational load, because the control gains are obtained offline by solving some LMIs, and the torque computation is done online by a simple polytopic-based equation.

In this paper, a new SS-LPV controller is addressed for the trajectory tracking problem of robotic arms. Robot workspace is zoned into some main zones in such a way that the number of models in each zone is almost equal. Data obtained from the modeling trajectory is used to design the state-feedback control gain.