Intuitive Control Strategy Research Articles

User Evaluation of a Shared Robot Control System Combining BCI and Eye Tracking in a Portable Augmented Reality User Interface.

This study evaluates an innovative control approach to assistive robotics by integrating brain-computer interface (BCI) technology and eye tracking into a shared control system for a mobile augmented reality user interface. Aimed at enhancing the autonomy of individuals with physical disabilities, particularly those with impaired motor function due to conditions such as stroke, the system utilizes BCI to interpret user intentions from electroencephalography signals and eye tracking to identify the object of focus, thus refining control commands. This integration seeks to create a more intuitive and responsive assistive robot control strategy. The real-world usability was evaluated, demonstrating significant potential to improve autonomy for individuals with severe motor impairments. The control system was compared with an eye-tracking-based alternative to identify areas needing improvement. Although BCI achieved an acceptable success rate of 0.83 in the final phase, eye tracking was more effective with a perfect success rate and consistently lower completion times (p<0.001). The user experience responses favored eye tracking in 11 out of 26 questions, with no significant differences in the remaining questions, and subjective fatigue was higher with BCI use (p=0.04). While BCI performance lagged behind eye tracking, the user evaluation supports the validity of our control strategy, showing that it could be deployed in real-world conditions and suggesting a pathway for further advancements.

Implementations of efficient non-blocking crossbar network with novel multiplexer design in quantum-dot cellular automata

As an emerging nanotechnology, Quantum-dot Cellular Automata (QCA) has attracted extensive attentions due to its characteristics of high density, high speed, and low energy consumption. Modern high-performance System-on-Chips (SoCs) with multiple processors require interconnection networks to connect each core for improving data throughput and reducing latency, while the crossbar network is broadly used as a non-blocking interconnection architecture with high efficiency. In this paper, a method to design an efficient QCA-based N × N crossbar network utilizing optimally designed N:1 multiplexers (MUXs) is proposed, followed by a multi-layer and a single-layer implementation of its 8 × 8 design. A simplified matrix model is then introduced to provide a concise and intuitive switch control strategy, and expressions for cell count, area, latency, QCA cost, and QCA complexity of the proposed crossbar networks are derived according to the size N. Experimental results manifest that the proposed 8 × 8 crossbar networks have significant advantages on most performance parameters compared with other existing QCA-based networks.

Model predictive control of non-interacting active Brownian particles.

Active matter systems are strongly driven to assume non-equilibrium distributions owing to their self-propulsion, e.g., flocking and clustering. Controlling the active matter systems' spatiotemporal distributions offers exciting applications such as directed assembly, programmable materials, and microfluidic actuation. However, these applications involve environments with coupled dynamics and complex tasks, making intuitive control strategies insufficient. This necessitates the development of an automatic feedback control framework, where an algorithm determines appropriate actions based on the system's current state. In this work, we control the distribution of active Brownian particles by applying model predictive control (MPC), a model-based control algorithm that predicts future states and optimizes the control inputs to drive the system along a user-defined objective. The MPC model is based on the Smoluchowski equation with a self-propulsive convective term and an actuated spatiotemporal-varying external field that aligns particles with the applied direction, similar to a magnetic field. We apply the MPC framework to control a Brownian dynamics simulation of non-interacting active particles and illustrate the controller capabilities with two objectives: splitting and juggling sub-populations, and polar order flocking control.

Intuitive, Myoelectric Control of Adaptive Sports Equipment for Individuals with Tetraplegia.

This research aims to develop safe, robust, and easy-to-use adaptive technology for individuals with tetraplegia. After a debilitating spinal cord injury, clinical care focuses on improving quality of life. Participation in adaptive sports has been shown to improve several aspects of participants' well-being. The TetraSki is a power-assisted ski chair that allows individuals with tetraplegia to participate in downhill skiing by sipping and puffing air on an integrated straw to turn their skis. Here, we introduce a new intuitive and dexterous control strategy for the TetraSki using surface electromyography (sEMG) from the neck and shoulder muscles. As an initial assessment, six healthy participants completed a virtual ski racecourse using sEMG and Sip-and-Puff control. Participants also completed a detection response task of cognitive load and the NASA-TLX survey of subjective workload. No significant differences were observed between the performance of sEMG control and the performance of Sip-and-Puff control. However, sEMG control required significantly less cognitive load and subjective workload than Sip-and-Puff control. These results indicate that sEMG can effectively control the equipment and is significantly more intuitive than traditional Sip-and-Puff control. This suggests that sEMG is a promising control method for further validation with individuals with tetraplegia. Ultimately, long-term use of sEMG control may promote neuroplasticity and drive rehabilitation.

A Perceptually Evaluated Signal Model: Collisions Between a Vibrating Object and an Obstacle

The collision interaction mechanism between a vibrating string and a non-resonant obstacle is at the heart of many musical instruments. This paper focuses on the identification of perceptually salient auditory features related to this phenomenon. The objective is to design a signal-based synthesis process, with an eye towards developing intuitive control strategies. To this end, a database of synthesized sounds is assembled through physics-based emulation of a string/obstacle collision, in order to characterize the effect of collisions on time-frequency content. The investigation of this database reveals characteristic time-frequency patterns related to the position of the obstacle during the interaction. In particular, a frequency shift of certain modes is apparent for strong interactions, which, alongside the generation of new frequency components, leads to increased perceived roughness and inharmonicity. These observations enable the design of a real-time compatible signal-based sound synthesis process, with a mapping of synthesis parameters linked to the perceived location of the obstacle. The accuracy of the signal model with respect to the physical model sound output and recorded sounds was evaluated through listening tests: time-frequency patterns reproduced by the signal model enabled listeners to precisely recognize the transverse location of the obstacle.

Cooperative Hand Therapy via a Soft, Wearable, and Unilateral Telerobotic System.

Functional rehabilitation of the hand is a complex and difficult process involving a large number of degrees of freedom (DOFs). Soft wearable hand-rehabilitation robots have assisted hand movements with a compact structural design, but effective rehabilitation requires an intuitive control scheme that can manage many DOFs and incorporate interaction with an occupational therapist, which has yet to be developed for this type of device. Thus, we present a soft wearable unilateral telerobotic system that enables various grasping tasks and cooperative interaction between the patient and therapist. The presented system consists of a sensor glove that measures the hand postures of the occupational therapist and a soft robotic glove that assists 4-DOF movements of the patient's hand, including adjustments of the interjoint coordination of the finger and 2-DOF movements of the thumb (flexion/extension and opposition/reposition). The soft robotic glove effectuates hand movements based on the measurements from the sensor glove. A telerobotic impedance-control scheme provides intuitive guidance of various hand postures, along with a fingertip-force vector. The feasible workspace and control performance of the system were evaluated on a healthy recruit and a poststroke patient. The presented system allowed the therapist to increase the patient's thumb workspace by 400% in palmar-dorsal direction and to control the fingertip-force direction at -30°∼10° range by enabling control of interjoint coordination of the proximal-interphalangeal and metacarpophalangeal joints. These features facilitate patients to perform various postures for stable object grasping. The presented rehabilitation system is suitable for noncontact telehealthcare that facilitates patient-therapist interactions.

Seamless and intuitive control of a powered prosthetic leg using deep neural network for transfemoral amputees.

Powered prosthetic legs are becoming a promising option for amputee patients. However, developing safe, robust, and intuitive control strategies for powered legs remains one of the greatest challenges. Although a variety of control strategies have been proposed, creating and fine-tuning the system parameters is time-intensive and complicated when more activities need to be restored. In this study, we developed a deep neural network (DNN) model that facilitates seamless and intuitive gait generation and transitions across five ambulation modes: level-ground walking, ascending/descending ramps, and ascending/descending stairs. The combination of latent and time sequence features generated the desired impedance parameters within the ambulation modes and allowed seamless transitions between ambulation modes. The model was applied to the open-source bionic leg and tested on unilateral transfemoral users. It achieved the overall coefficient of determination of 0.72 with the state machine-based impedance parameters in the offline testing session. In addition, users were able to perform in-laboratory ambulation modes with an overall success rate of 96% during the online testing session. The results indicate that the DNN model is a promising candidate for subject-independent and tuning-free prosthetic leg control for transfemoral amputees.

IMC-based diesel oxidation catalyst outlet temperature control with extended state predictor observer

It is of significant importance in the thermal regeneration process of diesel particulate filter that the diesel oxidation catalyst (DOC)-outlet gas temperature is accurately controlled to ensure efficient and safe regenerations. However, this is challenging because the DOC is a nonlinear and time-varying system which has complex oxidation reactions and large time delay, together with uncertainties and high frequency disturbances in real applications. To this end, an intuitive and effective control strategy which integrates the internal model control (IMC)-based design with the extended state predictor observer (ESPO) is proposed in this work. Firstly, a reduced first-order plus delay model, which is derived from a control-oriented one-dimensional DOC model, is deployed and experimentally validated to facilitate the model-based control design. Then, an IMC-based proportional–integral–derivative feedback control strategy, and an active disturbance rejection control (ADRC)-based ESPO are explored and designed. The IMC-based controller is designed for required set-point tracking and the ESPO is introduced for disturbance estimation and mitigation. In addition, as the regeneration is usually performed in real driving cycles, a feedforward control law based on the principle of energy balance is also well developed and integrated for compensation for the measurable transient disturbances. Simulation results show that a reduction of about 15% in recovery time can be achieved by introducing the ESPO into the IMC-based design. Performance of the proposed control strategy is finally validated on the engine bench and in on-vehicle real driving conditions under the worldwide harmonized light vehicles test cycle with new and aged DOCs. Experimental results show that, when the ESPO is introduced, it is more accurate to provide adequate set-point tracking in real applications where both the external disturbances and internal parameter uncertainties are presented.

Intuitive real-time control strategy for high-density myoelectric hand prosthesis using deep and transfer learning

Myoelectric hand prostheses offer a way for upper-limb amputees to recover gesture and prehensile abilities to ease rehabilitation and daily life activities. However, studies with prosthesis users found that a lack of intuitiveness and ease-of-use in the human-machine control interface are among the main driving factors in the low user acceptance of these devices. This paper proposes a highly intuitive, responsive and reliable real-time myoelectric hand prosthesis control strategy with an emphasis on the demonstration and report of real-time evaluation metrics. The presented solution leverages surface high-density electromyography (HD-EMG) and a convolutional neural network (CNN) to adapt itself to each unique user and his/her specific voluntary muscle contraction patterns. Furthermore, a transfer learning approach is presented to drastically reduce the training time and allow for easy installation and calibration processes. The CNN-based gesture recognition system was evaluated in real-time with a group of 12 able-bodied users. A real-time test for 6 classes/grip modes resulted in mean and median positive predictive values (PPV) of 93.43% and 100%, respectively. Each gesture state is instantly accessible from any other state, with no mode switching required for increased responsiveness and natural seamless control. The system is able to output a correct prediction within less than 116 ms latency. 100% PPV has been attained in many trials and is realistically achievable consistently with user practice and/or employing a thresholded majority vote inference. Using transfer learning, these results are achievable after a sensor installation, data recording and network training/fine-tuning routine taking less than 10 min to complete, a reduction of 89.4% in the setup time of the traditional, non-transfer learning approach.

Task-Oriented Muscle Synergy Extraction Using An Autoencoder-Based Neural Model

The growing interest in wearable robots opens the challenge for developing intuitive and natural control strategies. Among several human–machine interaction approaches, myoelectric control consists of decoding the motor intention from muscular activity (or EMG signals) with the aim of driving prosthetic or assistive robotic devices accordingly, thus establishing an intimate human–machine connection. In this scenario, bio-inspired approaches, e.g., synergy-based controllers, are revealed to be the most robust. However, synergy-based myo-controllers already proposed in the literature consider muscle patterns that are computed considering only the total variance reconstruction rate of the EMG signals, without taking into account the performance of the controller in the task (or application) space. In this work, extending a previous study, the authors presented an autoencoder-based neural model able to extract muscles synergies for motion intention detection while optimizing the task performance in terms of force/moment reconstruction. The proposed neural topology has been validated with EMG signals acquired from the main upper limb muscles during planar isometric reaching tasks performed in a virtual environment while wearing an exoskeleton. The presented model has been compared with the non-negative matrix factorization algorithm (i.e., the most used approach in the literature) in terms of muscle synergy extraction quality, and with three techniques already presented in the literature in terms of goodness of shoulder and elbow predicted moments. The results of the experimental comparisons have showed that the proposed model outperforms the state-of-art synergy-based joint moment estimators at the expense of the quality of the EMG signals reconstruction. These findings demonstrate that a trade-off, between the capability of the extracted muscle synergies to better describe the EMG signals variability and the task performance in terms of force reconstruction, can be achieved. The results of this study might open new horizons on synergies extraction methodologies, optimized synergy-based myo-controllers and, perhaps, reveals useful hints about their origin.

Effects of MEG-based neurofeedback for hand rehabilitation after tetraplegia: preliminary findings in cortical modulations and grip strength

Objective. Neurofeedback (NF) trains people to volitionally modulate their cortical activity to affect a behavioral outcome. We evaluated the feasibility of using NF to improve hand function after chronic cervical-level spinal cord injury (SCI) using biologically-relevant visual feedback of motor-related brain activity and an intuitive control scheme. Approach. The NF system acquired magnetoencephalography (MEG) data in real-time to provide feedback of event-related desynchronization (ERD) measured over the sensorimotor cortex during attempted hand grasping. During brain control, stronger ERD resulting from attempted grasping drove the virtual hand towards a more closed grasp, while less ERD drove the hand more open. Main results. Eight individuals with partial or complete hand impairment due to chronic SCI controlled the NF to perform a grasping task that increased in difficulty as the participants achieved success. During their first NF session, participants achieved an average success rate of 63.7 ± 6.4% (chance level of 13.9%). After as few as one intervention session, four of the seven individuals evaluated for ERD changes had significantly strengthened ERD and three of the four participants with measurable grip strength prior to NF had increased grip strength. Interestingly, both individuals who participated in a longer-term study (i.e. >8 NF sessions) had improved grip strength and significantly strengthened ERD. Significance. This study demonstrates that MEG-based NF training can change brain activity in individuals with hand impairment due to SCI and has the potential to induce acute changes in grip strength. Future studies will evaluate whether neuroplasticity induced with long term NF can improve hand function for those with moderate impairment.

Intelligent Control of a Spinal Prosthesis to Restore Walking After Neural Injury: Recent Work and Future Possibilities

This review focuses on the development of intelligent, intuitive control strategies for restoring walking using an innovative spinal neural prosthesis called intraspinal microstimulation (ISMS). These control strategies are inspired by the control of walking by the nervous system and are aimed at mimicking the natural functionality of locomotor-related sensorimotor systems. The work to date demonstrates how biologically inspired control strategies, some including machine learning methods, can be used to augment remaining function in models of complete and partial paralysis developed in anesthetized cats. This review highlights the advantages of learning predictions to produce automatically adaptive control of over-ground walking. This review also speculates on the possible future applications of similar machine learning algorithms for challenging walking tasks including navigating obstacles and traversing difficult terrain. Finally, this review explores the potential for plasticity and motor recovery with long-term use of such intelligent control systems and neural interfaces.

Human–robot shared control for humanoid manipulator trajectory planning

Purpose This paper aims to propose an intuitive shared control strategy to control a humanoid manipulator that can fully combine the advantages of humans and machines to produce a stronger intelligent form. Design/methodology/approach The working space of an operator’s arm and that of a manipulator are matched, and a genetic algorithm that limits the position of the manipulator’s elbow joint is used to find the optimal solution. Then, the mapping of the operator’s action to that of manipulators is realized. The controls of the human and robot are integrated. First, the current action of the operator is input. Second, the target object is predicted according to the maximum entropy hypothesis. Third, the joint angle of the manipulator is interpolated based on time. Finally, the confidence and weight of the current moment are calculated. Findings The modified weight adjustment method is the optimal way to adjust the weight during the task. In terms of time and accuracy, the experimental results of single target obstacle avoidance grabbing and multi-target predictive grabbing show that the shared control mode can provide full play to the advantages of humans and robots to accomplish the target task faster and more accurately than the control merely by a human or robot on its own. Originality/value A flexible and highly anthropomorphic human–robot action mapping method is proposed, which provides operator decisions in the shared control process. The shared control between human and the robot is realized, and it enhances the rapidity and intelligence, paving a new way for a novel human–robot collaboration.

Online Data-Driven Energy Management of a Hybrid Electric Vehicle Using Model-Based Q-Learning

The energy management strategy of a hybrid electric vehicle directly determines the fuel economy of the vehicle. As a supervisory control strategy to divide the required power into its multiple power sources, engines and batteries, many studies have been conducting using rule-based and optimization-based approaches for energy management strategy so far. Recently, studies using various machine learning techniques have been conducted. In this paper, a novel control framework implementing Model-based Q-learning is developed for the optimal control problem of hybrid electric vehicles. As an online energy management strategy, a new approach could learn the characteristics of a current given driving environment and adaptively change the control policy through learning. Especially, for the proposed algorithm, the internal powertrain environment and external driving environment are separated so they can be learned via the reinforcement learning framework, which results in a simpler and more intuitive control strategy that can be explained using the vehicle state approximation model. The proposed algorithm is tested and verified through simulations, and the simulation results present near optimal solution. The simulation results are compared with conventional rule-based strategies and optimal control solutions acquired from Dynamic Programming.

Robust output-feedback cascaded tracking controller for spatial motion of anisotropically-actuated vehicles

The paper presents the description, analysis, and validation of a cascaded trajectory-tracking control system designed for rigid-body vehicles moving in a 3-dimensional task space with non-banked maneuvering. In particular, we consider the anisotropically-actuated vehicles characterized by the presence of some privileged actuation directions and, simultaneously, actuation authority limitations along other directions. By combining the two specific control design methodologies, that is, the Vector-Field-Orientation (VFO) approach in the outer (kinematic-level) loop and the Active-Disturbance-Rejection (ADR) approach in the inner (dynamic-level) loop, we provide the resultant VFO-ADR control system characterized by an intuitive geometrical control strategy and robustness to a wide range of matched disturbances and substantial uncertainties of a vehicle model. To facilitate its practical applicability, the VFO-ADR control law has been designed in a way which requires feedback only from the positional configuration variables of a vehicle body. The results of experimental trials, obtained with the VFO-ADR control law applied to a laboratory-scale in-door airship, reveal its practical usefulness also in the case of underactuated vehicles under some specific motion conditions.

Proprioceptive Sonomyographic Control: A novel method for intuitive and proportional control of multiple degrees-of-freedom for individuals with upper\xa0extremity limb loss

Technological advances in multi-articulated prosthetic hands have outpaced the development of methods to intuitively control these devices. In fact, prosthetic users often cite "difficulty of use" as a key contributing factor for abandoning their prostheses. To overcome the limitations of the currently pervasive myoelectric control strategies, namely unintuitive proportional control of multiple degrees-of-freedom, we propose a novel approach: proprioceptive sonomyographiccontrol. Unlike myoelectric control strategies which measure electrical activation of muscles and use the extracted signals to determine the velocity of an end-effector; our sonomyography-based strategy measures mechanical muscle deformation directly with ultrasound and uses the extracted signals to proportionally control the position of an end-effector. Therefore, our sonomyography-based control is congruent with a prosthetic user’s innate proprioception of muscle deformation in the residual limb. In this work, we evaluated proprioceptive sonomyographic control with 5 prosthetic users and 5 able-bodied participants in a virtual target achievement and holding task for 5 different hand motions. We observed that with limited training, the performance of prosthetic users was comparable to that of able-bodied participants and thus conclude that proprioceptive sonomyographic control is a robust and intuitive prosthetic control strategy.

Gait Phase Identification During Level, Incline and Decline Ambulation Tasks Using Portable Sonomyographic Sensing.

Clinical viability of powered lower-limb assistive devices requires reliable and intuitive control strategies. Stance and swing are the main phases of the gait cycle across different locomotion tasks. Hence, a reliable method to accurately identify these phases can decrease sensing complexity and assist in enabling high-level control of assistive devices. Ultrasound (US) imaging has recently been introduced as a new sensing modality that may provide a solution for intuitive device control. US images of the rectus femoris and vastus intermedius muscles were collected in humans during level, incline, and decline ambulation tasks. Five low-level static (i.e. time-independent) features of US images were measured with respect to a reference image, including correlation coefficient, sum of absolute differences, structural similarity index, sum of squared differences, and image echogenicity. Time-derivatives of the static features were also calculated as temporal features. Support vector machine classifiers were trained using these static features to identify the gait phase both dependent and independent of the ambulation tasks. The results indicate an accuracy of 88.3% in identifying the gait phases for task-independent classifiers when trained using only the static features. Performance of the classifiers improved significantly to 92.8% after using the temporal features (p $\lt0.01)$. The algorithm was efficient and the average processing speed was faster than 100 Hz. This study is the first demonstration on use of US imaging to provide continuous estimates of ambulation phase, and on multiple surfaces. These findings suggest task-independent approaches may reliably identify the main phases of the gait cycle. Advancements in this area of study may provide simpler intuitive strategies for high-level assistive device control and increase their clinical relevance.

Guest Editorial: Special Section on Predictive Control in Power Electronics, Electrical Drives and Industrial Applications

The features and potential of predictive control techniques in power electronics applications have been demonstrated and widely studied during the last 15 years. The most remarkable advantages are the intuitive control scheme, great flexibility to cover a variety of degrees of freedom, and the straight-forward incorporation of nonlinear variables and constraints. Although this proposal with very attractive characteristics dates back to the 1970s, the experimental validation has only been possible recently, thanks to the development of modern digital controllers due to the relatively high computational burden associated with this method. The great effort of the researchers enabled a large number of publications in journals and dedicated conferences that has positioned this strategy as a viable alternative to the classical controllers. Thus, implementations based on this approach in proposals such as variable-speed drives, current, voltage, and power regulation, renewable energy generation systems, microgrids, energy storage, or electric vehicles (EVs) can be found in the literature. It is expected that the pace of developments with the predictive control keeps the current trend, because of the great opportunities in the research and industrial fields.

A Linear Approach to Optimize an EMG-Driven Neuromusculoskeletal Model for Movement Intention Detection in Myo-Control: A Case Study on Shoulder and Elbow Joints.

The growing interest of the industry production in wearable robots for assistance and rehabilitation purposes opens the challenge for developing intuitive and natural control strategies. Myoelectric control, or myo-control, which consists in decoding the human motor intent from muscular activity and its mapping into control outputs, represents a natural way to establish an intimate human-machine connection. In this field, model based myo-control schemes (e.g., EMG-driven neuromusculoskeletal models, NMS) represent a valid solution for estimating the moments of the human joints. However, a model optimization is needed to adjust the model's parameters to a specific subject and most of the optimization approaches presented in literature consider complex NMS models that are unsuitable for being used in a control paradigm since they suffer from long-lasting setup and optimization phases. In this work we present a minimal NMS model for predicting the elbow and shoulder torques and we compare two optimization approaches: a linear optimization method (LO) and a non-linear method based on a genetic algorithm (GA). The LO optimizes only one parameter per muscle, whereas the GA-based approach performs a deep customization of the muscle model, adjusting 12 parameters per muscle. EMG and force data have been collected from 7 healthy subjects performing a set of exercises with an arm exoskeleton. Although both optimization methods substantially improved the performance of the raw model, the findings of the study suggest that the LO might be beneficial with respect to GA as the latter is much more computationally heavy and leads to minimal improvements with respect to the former. From the comparison between the two considered joints, it emerged also that the more accurate the NMS model is, the more effective a complex optimization procedure could be. Overall, the two optimized NMS models were able to predict the shoulder and elbow moments with a low error, thus demonstrating the potentiality for being used in an admittance-based myo-control scheme. Thanks to the low computational cost and to the short setup phase required for wearing and calibrating the system, obtained results are promising for being introduced in industrial or rehabilitation real time scenarios.

Active attenuation of a trailing vortex inspired by a parabolized stability analysis

Designing effective control for complex three-dimensional flow fields proves to be non-trivial. Often, intuitive control strategies lead to suboptimal control. To navigate the control space, we use a linear parabolized stability analysis to guide the design of a control scheme for a trailing vortex flow field aft of a NACA0012 half-wing at an angle of attack $\unicode[STIX]{x1D6FC}=5^{\circ }$ and a chord-based Reynolds number $Re=1000$ . The stability results show that the unstable mode with the smallest growth rate (fifth wake mode) provides a pathway to excite a vortex instability, whereas the principal unstable mode does not. Inspired by this finding, we perform direct numerical simulations that excite each mode with body forces matching the shape function from the stability analysis. Relative to the uncontrolled case, the controlled flows show increased attenuation of circulation and peak streamwise vorticity, with the fifth-mode-based control set-up outperforming the principal-mode-based set-up. From these results, we conclude that a rudimentary linear stability analysis can provide key insights into the underlying physics and help engineers design effective physics-based flow control strategies.