Search results

The purpose of this study is to present a novel hybrid closed-loop control method together with its performance validation for the dexterous prosthetic hand.

The hybrid closed-loop control is composed of a high-level closed-loop control with the user in the closed loop and a low-level closed-loop control for the direct robot motion control. The authors construct the high-level control loop by using electromyography (EMG)-based human motion intent decoding and electrical stimulation (ES)-based sensory feedback. The human motion intent is decoded by a finite state machine, which can achieve both the patterned motion control and the proportional force control. The sensory feedback is in the form of transcutaneous electrical nerve stimulation (TENS) with spatial-frequency modulation. To suppress the TENS interfering noise, the authors propose biphasic TENS to concentrate the stimulation current and the variable step-size least mean square adaptive filter to cancel the noise. Eight subjects participated in the validation experiments, including pattern selection and egg grasping tasks, to investigate the feasibility of the hybrid closed-loop control in clinical use.

The proposed noise cancellation method largely reduces the ES noise artifacts in the EMG electrodes by 18.5 dB on average. Compared with the open-loop control, the proposed hybrid closed-loop control method significantly improves both the pattern selection efficiency and the egg grasping success rate, both in blind operating scenarios (improved by 1.86 s, p < 0.001, and 63.7 per cent, p < 0.001) or in common operating scenarios (improved by 0.49 s, p = 0.008, and 41.3 per cent, p < 0.001).

The proposed hybrid closed-loop control method can be implemented on a prosthetic hand to improve the operation efficiency and accuracy for fragile objects such as eggs.

The primary contribution is the proposal of the hybrid closed-loop control, the spatial-frequency modulation method for the sensory feedback and the noise cancellation method for the integrating of the myoelectric control and the ES-based sensory feedback.

The purpose of this paper is to explore the improvements in speed and precision achievable using straightforward closed-loop control for the gantry motion in additive manufacturing machines. The authors designed and built an economically viable demonstration system to quantify the performance improvement.

The authors develop and evaluate a low-cost closed-loop controller for the X and Y axes of an entry-level three-dimensional (3D) printer. The system developed captures and compensates for the dynamics of the motor and the belt-driven stage and detects mechanical errors, such as skipped motor steps.

The system produces path-following precision improvements of 40 and 75 per cent for two different sample trajectories. Correcting for skipped steps increases reliability and allows for more aggressive tuning of motion parameters; time savings of up to 25 per cent are seen by doubling acceleration rate.

The system presented provides an appropriate platform for further investigation into more complex, application-specific controllers and inclusion of more details of the printer dynamics that could produce still greater improvements in speed and accuracy.

The performance, low cost (40 USD/axis) and applicability to the majority of sub-2000USD 3D printer designs make this work of practical significance.

The CNC machining industry has for many years used similar approaches, but application to 3D printers has not been explored in the literature. This paper demonstrates the value of even a simple controller applicable to almost any 3D printer, while maintaining cost-effectiveness of the solution in a competitive market.

This new paper aims to extend the authors’ previous contributions about open-loop aircraft flutter test to closed-loop aircraft flutter test by virtue of the proposed direct data–driven strategy. After feeding back the output signal to the input and introducing one feedback controller in the adding feedback loop, two parts, i.e. unknown aircraft flutter model and unknown feedback controller, exist in this closed-loop aircraft flutter system, simultaneously, whose input and output are all corrupted with external noise. Because of the relations between aircraft flutter model parameters and the unknown aircraft model, direct data–driven identification is proposed to identify that aircraft flutter model, then some identification algorithms and their statistical analysis are given through the authors’ own derivations. As the feedback controller can suppress the aircraft flutter or guarantee the flutter response converge to one desired constant value, the direct data–driven control is applied to design that feedback controller only through the observed data sequence directly. Numerical simulation results have demonstrated the efficiency of the proposed direct data–driven strategy. Generally, during our new information age, direct data–driven strategy is widely applied around our living life.

First, consider one more complex closed loop stochastic aircraft flutter model, whose input–output are all corrupted with external noise. Second, for the identification problem of closed-loop aircraft flutter model parameters, new identification algorithm and some considerations are given to the corresponding direct data–driven identification. Third, to design that feedback controller, existing in that closed-loop aircraft flutter model, direct data–driven control is proposed to design the feedback controller, which suppresses the flutter response actively.

A novel direct data–driven strategy is proposed to achieve the dual missions, i.e. identification and control for closed-loop aircraft flutter test. First, direct data–driven identification is applied to identify that unknown aircraft flutter model being related with aircraft flutter model parameters identification. Second, direct data–driven control is proposed to design that feedback controller.

To the best of the authors’ knowledge, this new paper extends the authors’ previous contributions about open-loop aircraft flutter test to closed-loop aircraft flutter test by virtue of the proposed direct data–driven strategy. Consider the identification problem of aircraft flutter model parameters within the presented closed loop environment, direct data–driven identification algorithm is proposed to achieve the identification goal. Direct data–driven control is proposed to design the feedback controller, i.e. only using the observed data to design the feedback controller.

New applications of solid freeform fabrication (SFF) are arising, such as functional rapid prototyping and in situ fabrication, which push SFF to its limits in terms of geometrical fidelity due to the applications' inherent process uncertainties. Current closed‐loop feedback control schemes monitor and manipulate SFF techniques at the process level, e.g. envelope temperature, feed rate. “Closing the loop” on the process level, instead of the overall part geometry level, leads to limitations in the types of errors that can be detected and corrected. The purpose of this paper is to propose a technique called greedy geometric feedback (GGF) control which “closes the loop” on the overall part geometry level.

The overall part geometry is monitored throughout the print and, using a greedy algorithm, real‐time decisions are made to serially determine the locations of subsequent droplets, i.e. overall part geometry is directly manipulated. A computer simulator and a physical experimental platform were developed to compare the performance of GGF to an open‐loop control scheme. Root mean square surface height errors were measured under controlled uncertainties in droplet height, droplet radius of curvature, droplet positioning and mid‐print part deformations.

The GGF technique outperformed open‐loop control under process uncertainties in droplet shape, droplet placement and mid‐print part deformations. The disparity between performances is dependant on the nature and extent of the imposed process uncertainties.

Future research will focus on improving the performance of GGF for specific cases by designing more complex greedy algorithmic scoring heuristics. Also, the technique will be generalized beyond heightmap representations of 3D spaces.

The GGF technique is the first to “close the loop” on the overall part geometry level. GGF, therefore, can compensate for a broader range of errors than existing closed‐loop feedback control schemes. Also, since the technique only requires the real‐time update of a very limited set of heights, the technique is computationally inexpensive and widely applicable. By developing a closed‐loop feedback scheme that addressed part geometry‐level errors, SFF can be applied to more challenging in situ fabrication scenarios with less conventional materials.

The aim of this paper is to undertake analysis and comparison of the closed‐loop and sensorless control systems sensitivity to the broken rotor for diagnostic purposes. For the same vector control system induction motor drive analysis concerning operation with the asymmetric motor, broken rotor fault handling and operation were investigated. Reliability, range of stable operation, fault symptoms and application of diagnosis methods based on control system variables utilization was analyzed.

Induction motor drive vector control system synthesis was applied using the multiscalar variables of the machine model with nonlinear feedback linearization applied to use classical cascaded PI controllers for the speed‐torque and flux decoupled control. Speed observer was applied for the rotor flux and rotor speed estimation for the sensorless control system synthesis.

Relative sensitivity of the state and control system variables to broken rotor fault based on experimental results for the closed‐loop and sensorless control systems is presented and compared. Drawbacks of using the MCSA analysis for the rotor fault diagnosis in the closed‐loop and sensorless control systems are pointed. Advantages and drawbacks of the state space estimators filtering characteristics in the sensorless control system are described.

Asymmetric IM motor drive handling and diagnosis. Broken rotor range diagnosis inconsistency using the popular MCSA method should be considered in the closed‐loop and sensorless control system of the induction motor drive. Depending on the IM motor drive application and the operation requirements the results can be used for asymmetric machine proper handling, choosing proper control system structure and control system variables for rotor fault early diagnosis.

Sensitivity of the state and control system variables to broken rotor fault based on experimental results for the closed‐loop and sensorless control systems is presented, which implies motor handling procedures and fault diagnosis.

To devise a new technique to synthesise optimal feedback control law for non‐linear dynamic systems through fuzzy logic.

The proposed methodology utilizes the open‐loop optimal control solutions (OCSs) of the non‐linear systems for the training of the fuzzy system in the process of developing closed‐loop fuzzy logic guidance (FLG). This is achieved through defining a set of non‐dimensionalised variables related to the system states.

FLG is capable of generating closed‐loop control law for the non‐linear problem investigated. Since the proposed fuzzy structure is independent of the system equations, the approach is potentially applicable to other non‐linear system. Introduction of the non‐dimensional variables in place of the regular states has effectively increased the fuzzy training performance and greatly reduced the number of fuzzy rule bases required to produce accurate solutions for other untrained scenarios.

There exist many complex non‐linear problems in guidance and control of aerospace vehicles. Determination of optimal control laws for such systems is usually a difficult task even in an open‐loop form and in a noise‐free off‐line environment. On the other hand, closed‐loop OCSs are highly desirable for their robust characteristics in actual operating environments, so are more suitable for online applications, but can seldom be realized for complex non‐linear systems. Even though a few researchers have worked in the area of non‐linear optimal control and application of fuzzy logic on such systems, non‐have dealt with closed‐loop optimal fuzzy controllers. This research proposes a new strategy for the determination of optimal feedback control laws for non‐linear systems, which can be utilized in many spacecraft mission applications.

The purpose of this paper is to present the importance of model accuracy in closed loop control by the help of parallel finite element model of a voltage-fed solenoid with iron core.

The axisymmetric formulation of the domain decomposition-based circuit-coupled finite element method (FEM) is embedded in a closed loop control system. The control parameters for the proportional-integral (PI) controller were estimated using the step response of the analytical, static and dynamic model of the solenoid. The controller measures the error of the output of the model after each time step and controls the applied voltage to reach the steady state as fast as possible.

The results of the closed loop system simulation show why the model accuracy is important in the stage of the controller design. The FEM offers higher accuracy that the analytic model attained with magnetic circuit theory, because the inductance and resistance variation already take into account in the numerical calculation. Furthermore, parallel FEM incorporating domain decomposition to reduce the increased computation time.

A closed loop control with PI controllers is applied for a voltage driven finite element model. The high computation time of the numerical model in the control loop is decreased by the finite element tearing and interconnecting method with direct and iterative solver.

The purpose of this paper is analysis of the sensorless control system of induction machine with broken rotor for diagnostic purposes. Increasing popularity of sensorless controlled variable speed drives requires research in area of reliability, range of stable operation, fault symptoms and application of diagnosis methods.

T transformation used for conversion of instantaneous rotor currents electrical circuit representation to space vector components is investigated to apply with closed‐loop modeling algorithm. Evaluation of the algorithm is based on analysis of asymmetry influence to the orthogonal and zero components of space vector representation. Multiscalar model of the machine and selected structures of state observers are used for sensorless control system synthesis. Proposed method of frequency characteristics calculation is used for state observers analysis in open‐loop operation.

New algorithm of applying the T transformation allows for closed‐loop and sensorless control system simulation with asymmetric machine due to broken rotor. Compensating effect of the closed‐loop control system with speed measurements and diagnosis information in control system variables are identified. Proposed frequency analysis of state observers is presented and applied. Variables with amplified characteristic frequency components related to rotor asymmetry are compared for selected structures of state observers and with closed‐loop and open‐loop operation. Method of improving the sensorless system stability is proposed.

In closed‐loop and sensorless control system rotor fault can be diagnosed by using PI output controllers variables. Compensating effect of mechanical variables sets limitation to specified diagnosis methods. Rotor asymmetry affects sensorless control system stability depending on estimator structure.

This paper concentrates upon sensorless control system operation with machine asymmetry and indicates rotor fault symptoms.

In survey interviews information is transferred to the researchers via a communication process between interviewers and respondents. This process is controlled directly by the interviewers, and indirectly by the researchers who constructed the questionnaire and instructed and supervised the interviewers. In spite of these control activities, errors occur. This paper investigates the sources of these errors.

In order to investigate the sources of these errors, transcripts of 200 interviews were analyzed using a detailed coding scheme.

In 30 percent of all question‐answer sequences interviewer and respondent stick to the “script” designed by the researcher. In these “paradigmatic” sequences the open loop control by the researcher works well. In 25 percent of the sequences this control is not sufficient, but additional closed loop control, via “repair” activities of the interviewers, appears to be successful. In the remaining sequences both the open loop control of the researcher and the closed loop control by the interviewer failed.

The recently developed systematic analysis of question‐answer sequences in survey interviews, employed in this research, offers detailed insight into the errors occurring during the interview process, and illustrates the need for improved question design and improved training of interviewers.

The purpose of this paper is to extend the authors’ previous contributions on aircraft flutter model parameters identification. Because closed-loop condition is more widely used in today’s practice, a closed-loop stochastic model of the aircraft flutter test is constructed to model the aircraft flutter process, whose input–output signals are all corrupted by the observed noises. Through using a rational transfer function, the equivalent property between the aircraft flutter model parameters and polynomial coefficients is established, and then the problem of aircraft flutter model parameters identification is turned to one closed-loop identification problem. An iterative identification algorithm is proposed to identify the unknown polynomial coefficients, being benefit for the latter flutter model parameter identification. Furthermore, as the closed-loop output corresponds to the flutter amplitude, so from the point of the minimization with respect to the variance of the closed-loop output, the optimal input signal and optimal feedback controller are all derived to achieve the zero flutter, respectively, for example, the optimal input spectrum and the detailed form for optimal feedback controller.

First, model parameter identification for aircraft flutter is reviewed as one problem of parameter identification and this aircraft flutter model corresponds to one closed-loop stochastic model, whose input signal and output are corrupted by external noises. Second, for aircraft flutter closed-loop statistical model with statistical noise, an iterative identification algorithm is proposed to identify the unknown model parameters. Third, from the point of minimizing with respect to the variance of the closed-loop output, the optimal input signal and optimal feedback controller are all derived to achieve the zero flutter, respectively, for example, the optimal input spectrum and the detailed form for optimal feedback controller.

This aircraft flutter model corresponds to one closed-loop stochastic model, whose input signal and output are corrupted by external noises. Then, identification algorithm and optimal input signal design are studied for aircraft flutter model parameter identification with statistical noise, respectively. It means the optimal input signal and optimal feedback controller are useful for the aircraft flutter model parameter identification within the constructed new closed-loop stochastic model.

To the best of the authors’ knowledge, this problem of the model parameter identification for aircraft flutter is proposed by their previous work, and they proposed many identification strategies to identify these model parameters. This paper proposes a new closed-loop stochastic model to construct the aircraft flutter test, and some related topics are considered about this closed-loop identification for aircraft flutter model parameter identification in the framework of closed-loop condition.