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An advanced pitch controller is proposed for the load mitigation of wind turbines. This study focuses on the nacelle acceleration feedback control and lidar-based feedforward control, and discusses how these controllers contribute to reduce the load on wind turbines. The nacelle acceleration feedback control increases the damping ratio of the first mode of wind turbines, but it also increases the fluctuation in the rotor speed and thrust force, which results in the optimum gain value. The lidar-based feedforward control reduces the fluctuation in the rotor speed and the thrust force by decreasing the fluctuating wind load on the rotor, which reduces the fluctuating load on the tower. The combination of the nacelle acceleration feedback control and the lidar-based feedforward control successfully reduces both the response of the tower first mode and the fluctuation in the rotor speed at the same time.

The dynamics modeling and trajectory optimization of a segmented linkage cable-driven hyper-redundant robot (SL-CDHRR) become more challenging, since there are multiple couplings between the active cables, passive cables, joints and end-effector. To deal with these problems, this paper proposes a dynamic modeling and trajectory tracking control methods for such type of CDHRR, i.e., SL-CDHRR. First, the multi-coupling kinematics equation (i.e., cable-joint-end) of the hyper-redundant robot is derived. Then, according to the transmission characteristics of the hybrid active/passive segmented linkage, the dynamic equation of series–parallel coupling is derived. It consists of parallel-active dynamics and series-passive dynamics. Furthermore, using the tension of active cables and the pose of the end-effector as optimization indicators, a trajectory tracking framework was constructed by the combination of dynamic feedforward control and PD control. The multi-objective particle swarm optimization method is used to achieve the simultaneous optimization of the energy indicator and control accuracy indicator during the trajectory tracking process. Finally, a MATLAB/SimMechanics co-simulation system is built, and the proposed methods are verified by the built co-simulation system.

This study investigated the relationship between different disfluency types (i.e., repetitions, prolongations, and blocks) and resting state functional connectivity in the feedback (FB) and feedforward (FF) control networks in 20 adults who stutter. Frequency of each disfluency type was coded in speech samples derived from the Stuttering Severity Instrument, and functional connectivity between brain regions of interest was derived from resting state functional magnetic resonance imaging scans. We used LASSO regressions to identify the connections that most strongly predicted each disfluency type. Both repetitions and prolongations were significantly associated with increased connectivity in left ventral motor cortex - right ventral premotor cortex, which is hypothesized to be involved in FB control of speech. In contrast, blocks were significantly associated with reduced connectivity in right anterior cerebellum - left ventral lateral thalamic nucleus and increased connectivity in left presupplementary motor area - left posterior inferior frontal sulcus, both of which are hypothesized to be involved in FF control of speech. Our findings suggest that repetitions and prolongations may be associated with increased reliance on FB-based corrective mechanisms, whereas blocks may be associated with disrupted FF-based initiation mechanisms. These neural underpinnings may correspond to different challenges in terminating or initiating motor commands and underscore the nuanced neurobiological processes underlying speech disfluencies.

Piezoelectric ceramic actuators show nonlinear hysteresis characteristics due to material properties. In order to modify the inverse piezoelectric effect as an ideal linear execution, the classical Prandtl–Ishlinskii (PI) model is usually used on compensation by feedforward control. The PI model performs well on the simple hysteresis characteristics. However, when the output requirements are complex, the PI model has uneven compensation accuracy on the complex hysteresis characteristics and cannot achieve the accuracy as same as the simple hysteresis. This paper proposes a simplification of the complex hysteresis: Separated level-loop PI (SLPI) model. Firstly, use a loop separation logic algorithm simplification of the complex hysteresis characteristics to obtain hysteresis single loops with loop levels and vertexes. Secondly, hysteresis characteristics of each loop are independently modeled using the PI model. Finally, the inverse model is reconstructed by the rollback method to restore a positive sequence of the feedforward voltage and then input the feedforward voltage as a compensation to achieve higher and more uniform accuracy. Experiments and discussions show that the SLPI model can effectively improve the compensation results of complex hysteresis characteristics by 50.3%, and the average compensation accuracy difference between single hysteresis loops is reduced by 53.7%.

This paper proposes a nonlinear intelligent control of a two link robot arm by considering human voluntary components. In general, human arm viscoelastic properties are regulated in different manners according to various task requirements. The viscoelasticity consists of joint stiffness and viscosity. The research of the viscoelasticity can improve the development of industrial robots, rehabilitation and sports etc. So far, some results have been shown using filtered human arm viscoelasticity measurements. That is, human motor command is removed. As a result, the dynamics of human voluntary component during movements is omitted. In this paper, based on the feedforward characteristics of human multi joint arm, a model is obtained by considering human voluntary components using a support vector regression technique. By employing the learned model, a nonlinear intelligent control of two link robot arm is proposed. Experimental results confirm the effectiveness of this proposal.

The article considers the step and impulse response of second-order linear systems with a positive zero. A particular parameterization of the system equations is proposed which enables good assessment of the influence of its parameters on transients. Expressions missing in the literature are derived for step response parameters such as the values of undershoot, overshoot, time of inverse response, rise time and settling time, as well as of impulse response. Based on them, a precise time-domain approach to design feedforward, feedback and mixed feedback– feedforward control structures for nonminimum phase objects is presented that considers both setpoint tracking and disturbance rejection.

Active control of structural response is the most practical and effective approach to mitigate helicopter vibration and enhance ride quality. In this paper, adaptive feedforward vibration control is constructed for the helicopter cabin floor driven by piezoelectric stack actuators (PSAs). A scale helicopter airframe model, preserving the local geometric similarity of the cabin floor structure, is developed and optimized to capture the low‐order global dynamic characteristics of a reference airframe. The model of PSA is integrated into the attached beam element based on the conditions of force equilibrium and displacement compatibility, and adaptive feedforward control is implemented by the filtered‐x least mean square (Fx‐LMS) algorithm. Simulations and experimental studies under diverse excitations have been carried out. Results indicate that the adaptive PSA‐driven ride quality improvement system can effectively reduce the cabin floor vibration, and the responses under multidirection excitations can also be reduced by more than 90%. It is also observed that the responses at different control points exhibit inconsistent convergence due to the interference of modes under multidirection excitations.

The distributed optimal output synchronization problem for the leaderless heterogeneous multi-agent system with a general global cost function is investigated for the first time by linear quadratic (LQ) optimal control theory. Conventional algorithms for heterogeneous systems are quite complex, requiring the design of a virtual reference generator and the solving of regulation equations. This paper presents a novel distributed asymptotically optimal controller by incorporating the design of distributed observer and feedforward controller. A general form of the distributed controller is obtained by solving an augmented algebraic Riccati equation, which is parallel to classical optimal control theory. The optimal topology is an arbitrary directed graph containing only a spanning tree. It is shown that the proposed algorithms outperform the traditional consensus methods in the convergence speed by selecting proper observer gain matrices, and eliminate the reliance on the nonzero eigenvalues of Laplacian matrix. Simulation example further demonstrates the effectiveness of the proposed scheme and a faster superlinear convergence speed than the existing algorithm.

Background Current commercial prosthetic hand controllers limit patients’ ability to fully engage high Degree-of-Freedom (DoF) prosthetic hands. Available feedforward controllers rely on large training data sets for controller setup and a need for recalibration upon prosthesis donning. Recently, an intuitive, proportional, simultaneous, regression-based 3-DoF controller remained stable for several months without retraining by combining chronically implanted electromyography (ciEMG) electrodes with a K-Nearest-Neighbor (KNN) mapping technique. The training dataset requirements for simultaneous KNN controllers increase exponentially with DoF, limiting the realistic development of KNN controllers in more than three DoF. We hypothesize that a controller combining linear interpolation, the muscle synergy framework, and a sufficient number of ciEMG channels (at least two per DoF), can allow stable, high-DoF control. Methods Two trans-radial amputee subjects, S6 and S8, were implanted with percutaneously interfaced bipolar intramuscular electrodes. At the time of the study, S6 and S8 had 6 and 8 bipolar EMG electrodes, respectively. A Virtual Reality (VR) system guided users through single and paired training movements in one 3-DoF and four different 4-DoF cases. A linear model of user activity was built by partitioning EMG feature space into regions bounded by vectors of steady state movement EMG patterns. The controller evaluated online EMG signals by linearly interpolating the movement class labels for surrounding trained EMG movements. This yields a simultaneous, continuous, intuitive, and proportional controller. Controllers were evaluated in 3-DoF and 4-DoF through a target-matching task in which subjects controlled a virtual hand to match 80 targets spanning the available movement space. Match Percentage, Time-To-Target, and Path Efficiency were evaluated over a 10-month period based on subject availability. Results and conclusions In 3-DoF, S6 and S8 matched most targets and demonstrated stable control after 8 and 10 months, respectively. In 4-DoF, both subjects initially found two of four 4-DoF controllers usable, matching most targets. S8 4-DoF controllers were stable, and showed improving trends over 7–9 months without retraining or at-home practice. S6 4-DoF controllers were unstable after 7 months without retraining. These results indicate that the performance of the controller proposed in this study may remain stable, or even improve, provided initial viability and a sufficient number of EMG channels. Overall, this study demonstrates a controller capable of stable, simultaneous, proportional, intuitive, and continuous control in 3-DoF for up to ten months and in 4-DoF for up to nine months without retraining or at-home use with minimal training times.

In view of the characteristic of large transformer load fluctuations, a feedforward fuzzy control strategy using the startup and shutdown of cooler groups as the executive means is proposed to meet the transformer temperature control requirements. Based on the research on the mechanism of oil-immersed forced oil circulation air-cooled (OFAF) coolers, fuzzy control rules are summarized to realize the closed-loop regulation of transformer oil temperature. Meanwhile, feedforward control is used to compensate for the impact of transformer power disturbances on oil temperature. A simulation control model of the OFAF cooler is built on the Simulink platform, which verifies the feasibility and effectiveness of the feedforward fuzzy control strategy for the transformer OFAF cooler. Furthermore, by comparing the results under two control execution modes, the advantages of using the startup and shutdown of cooler groups as the executive means are analyzed.