feedforward control

In this paper, the robust adaptive optimal tracking control problem is addressed for the disturbed unmanned helicopter based on the time-varying gain extended state observer (TVGESO) and adaptive dynamic programming (ADP) methods. Firstly, a novel TVGESO is developed to tackle the unknown disturbance, which can overcome the drawback of initial peaking phenomenon in the traditional linear ESO method. Meanwhile, compared with the nonlinear ESO, the proposed TVGESO possesses easier and rigorous stability analysis process...

In light of the problem of trajectory tracking control in vehicle servo systems with system model uncertainty and external time-varying disturbance, an effective trajectory tracking control method that can handle system model uncertainty and external time-varying disturbances is proposed. To achieve this goal, a novel composite robust integral of the sign of the error (RISE) control method is introduced that combines a multi-layer neural network and an extended state observer. Specifically, multi-layer neural networks are utilized to approximate the uncertainty of the system model, while an extended state observer is employed to estimate the fitting near-error and the external time-varying interference, which are used as feedforward compensation...

The development of advanced prosthetic devices that can be seamlessly used during an individual's daily life remains a significant challenge in the field of rehabilitation engineering. This study compares the performance of deep learning architectures to shallow networks in decoding motor intent for prosthetic control using electromyography (EMG) signals. Four neural network architectures, including a feedforward neural network with one hidden layer, a feedforward neural network with multiple hidden layers, a temporal convolutional network, and a convolutional neural network with squeeze-and-excitation operations were evaluated in real-time, human-in-the-loop experiments with able-bodied participants and an individual with an amputation...

OBJECTIVE: Biological motion perception (BMP) correlating with a mirror neuron system (MNS) is attenuated in underage individuals with autism spectrum disorder (ASD). While BMP in typically-developing controls (TDCs) encompasses interconnected MNS structures, ASD data hint at segregated form and motion processing. This coincides with less fewer long-range connections in ASD than TDC. Using BMP and electroencephalography (EEG) in ASD, we characterized directionality and coherence (mu and beta frequencies)...

This paper proposes a composite sliding mode control (SMC) to optimize the tracking performance and the anti-disturbance performance of permanent magnet synchronous machine (PMSM) speed regulation systems. The differential term in the control law can magnify the measurement noise, resulting in more discontinuity. To filter out the high frequency noise and make the control law smoother, the first-order differentiator (FOD) is employed to estimate the speed error and its derivative. Since the feedforward compensation can improve the robustness of the system, a disturbance observer (DOB) based on the sliding mode observer (SMO) is designed to reinforce the dynamic performance under disturbance variation...

In this paper, a new approach involving the use of a mobile manipulator to assist humans with mobility impairments to walk is proposed. First, in order to achieve flexible interaction between humans and mobile manipulators, we propose a variable admittance controller that can adaptively regulate the virtual mass and damping parameters based on the interaction forces and the human motion intention predicted using the fuzzy theory. Moreover, a feedforward velocity compensator based on a designed state observer is proposed to decrease the inertia resistance of the manipulator, effectively enhancing the compliance of the human-robot interaction...

Soft robotics promises to achieve safe and efficient interactions with the environment by exploiting its inherent compliance and designing control strategies. However, effective control for the soft robot-environment interaction has been a challenging task. The challenges arise from the nonlinearity and complexity of soft robot dynamics, especially in situations where the environment is unknown and uncertainties exist, making it difficult to establish analytical models. In this study, we propose a learning-based optimal control approach as an attempt to address these challenges, which is an optimized combination of a feedforward controller based on probabilistic model predictive control and a feedback controller based on nonparametric learning methods...

RNA plays an indispensable role in mammalian cell functions. Cas13, a class of RNA-guided ribonuclease, is a flexible tool for modifying and regulating coding and non-coding RNAs, with enormous potential for creating new cell functions. However, the lack of control over Cas13 activity has limited its cell engineering capability. Here, we present the CRISTAL (Control of RNA with Inducible SpliT CAs13 Orthologs and Exogenous Ligands) platform. CRISTAL is powered by a collection (10 total) of orthogonal split inducible Cas13 effectors that can be turned ON or OFF via small molecules in multiple cell types, providing precise temporal control...

The benefits of cold have long been recognized in sport and medicine. However, it also brings costs, which have more rarely been investigated, notably in terms of sensorimotor control. We hypothesized that in addition to peripheral effects, cold slows down the processing of proprioceptive cues, which has an impact on both feedback and feedforward control. We therefore compared the performances of participants whose right arm had been immersed in either cold water (arm temperature: 14°C) or lukewarm water (arm temperature: 34°C)...

Parkinson's disease targets patients' cognitive and motor abilities, including postural control. Many studies have been carried out to introduce mathematical models for a better understanding of postural control in such patients and the relation between the model parameters and the clinical assessments. So far, these studies have addressed this connection merely in static tests, such as quiet stance. The aim of this study is to develop a model for voluntary lean, and as such, identify the model parameters for both PD patients and healthy subjects from experimental data...

OBJECTIVE: To assess the influence of feedforward control-based health education intervention on the compliance, visual function and self-perceived burden (SPB) among patients with diabetic retinopathy (DR). METHODS: Eighty-six DR patients were divided into feedforward control and control groups (n=43). The control group was given routine nursing intervention, based on which the feedforward control group received feedforward control-based health education intervention...

In manufacturing, musculoskeletal robots have gained more attention with the potential advantages of flexibility, robustness, and adaptability over conventional serial-link rigid robots. Focusing on the fundamental lifting tasks, a hybrid controller is proposed to overcome control challenges of such robots for widely applications in industry. The metaverse technology offers an available simulated-reality-based platform to verify the proposed method. The hybrid controller contains two main parts. A muscle-synergy-based radial basis function (RBF) network is proposed as the feedforward controller, which is able to characterize the phasic and the tonic muscle synergies simultaneously...

The prediction of gait motion intention is essential for achieving intuitive control of assistive devices and diagnosing gait disorders. To reduce the cost associated with using multimodal signals and signal processing, we proposed a novel method that integrates machine learning with musculoskeletal modelling techniques for the prediction of time-series joint angles, using only kinematic signals. Additionally, we hypothesised that a stacked long short-term memory (LSTM) neural network architecture can perform the task without relying on any ahead-of-motion features typically provided by electromyography signals...

In CA1 hippocampus, pyramidal cells (PCs) can be separated into two classes, deep or superficial, based on their radial position. In mice, superficial PCs receive fewer inhibitory synapses from parvalbumin (PV)-expressing interneurons than deep PCs, resulting in weaker feedforward inhibition of input from CA3 Schaffer collaterals. The molecular mechanisms that control PC differentiation and integration into hippocampal circuits remain unknown. Using mice, we found that the transcriptional regulator SATB2 is expressed in superficial PCs but absent from deep PCs at birth...

Across the globe, governments are developing policies and strategies to reduce carbon emissions to address climate change. Monitoring the impact of governments' carbon reduction policies can significantly enhance our ability to combat climate change and meet emissions reduction targets. One promising area in this regard is the role of artificial intelligence (AI) in carbon reduction policy and strategy monitoring. While researchers have explored applications of AI on data from various sources, including sensors, satellites, and social media, to identify areas for carbon emissions reduction, AI applications in tracking the effect of governments' carbon reduction plans have been limited...

Many motor control systems generate multiple movements using a common set of muscles. How are premotor circuits able to flexibly generate diverse movement patterns? Here, we characterize the neuronal circuits that drive the distinct courtship songs of Drosophila melanogaster. Male flies vibrate their wings toward females to produce two different song modes-pulse and sine song-which signal species identity and male quality. Using cell-type-specific genetic reagents and the connectome, we provide a cellular and synaptic map of the circuits in the male ventral nerve cord that generate these songs and examine how activating or inhibiting each cell type within these circuits affects the song...

This article considers the two-stage approach to solving a partially observable Markov decision process (POMDP): the identification stage and the (optimal) control stage. We present an inexact sequential quadratic programming framework for recurrent neural network learning (iSQPRL) for solving the identification stage of the POMDP, in which the true system is approximated by a recurrent neural network (RNN) with dynamically consistent overshooting (DCRNN). We formulate the learning problem as a constrained optimization problem and study the quadratic programming (QP) subproblem with a convergence analysis under a restarted Krylov-subspace iterative scheme that implicitly exploits the structure of the associated Karush-Kuhn-Tucker (KKT) subsystem...

Existing feedforward, or FF, controllers are based on differentially flat models. For double-acting single-rod cylinders, more compact than the double-rod ones, flat models could be constructed either under an incompressible-flow assumption for low-pressure, slow-response applications or for a matched valve-cylinder combination. Contrasting the existing models tackling only a single nonlinear aspect like valve leakage, oil compressibility or friction, a novel FF controller, generalized for different types of valve-cylinder-pump combinations, has been developed here...

Robots in space are necessarily extremely light and lack structural stiffness resulting in natural frequencies of resonance so low as to reside inside the attitude controller's bandwidth. A variety of input trajectories can be used to drive a controller's attempt to ameliorate the control-structural interactions where feedback is provided by low-quality, noisy sensors. Traditionally, step functions are used as the ideal input trajectory. However, step functions are not ideal in many applications, as they are discontinuous...

Knowing the site of drug action is important to optimize effectiveness and address any side effects. We used light-sensitive drugs to identify the brain region-specific role of mGlu5 metabotropic glutamate receptors in the control of pain. Optical activation of systemic JF-NP-26, a caged, normally inactive, negative allosteric modulator (NAM) of mGlu5 receptors, in cingulate, prelimbic and infralimbic cortices and thalamus inhibited neuropathic pain hypersensitivity. Systemic treatment of alloswitch-1, an intrinsically active mGlu5 receptor NAM, caused analgesia, and the effect was reversed by light-induced drug inactivation in in the prelimbic and infralimbic cortices, and thalamus...