Motion Control Of Manipulators Research Articles

Neighborhood Socio-Economic Status Influences Motor Performance and Inhibitory Control in Kindergarten Children-Findings from the Cross-Sectional Kitafit Study.

Numerous studies have examined the role of socio-economic status on physical activity, obesity, and cognitive performance in youth or older adults, but few studies have examined the role of neighborhood socio-economic status (NSES) on motor or cognitive performance in kindergarten children. This study aimed to examine whether lower NSES (measured by the social data atlas) was associated with lower motor and inhibitory control performance in kindergarten children. One hundred twenty-nine preschoolers were recruited from eight kindergartens in low and high NSES areas in Stuttgart, one of Germany's largest metropolitan areas. Motor functioning (Movement Assessment Battery for Children, MABC-2; Manual Dexterity, Aiming and Catching, and Balance) and inhibitory control (Flanker Task, Go/NoGo Task) were assessed in a sample of 3- to 6-year-old children within a cross-sectional study. Children from a low NSES background showed the expected difficulties in inhibitory control and motor performance, as indicated by poorer performance than children from a high NSES background. Sex-specific analysis revealed girls from low NSES areas to have the lowest fine motor control; children with low NSES reach a Developmental Coordination Disorder at-risk status of 13% (boys and girls), in contrast to children with high SES (boys 9.1%, girls 0.0%). Motor performance and inhibitory control correlated positively with regard to the group from a low NSES background. Researchers and practitioners are advised to develop a more nuanced picture of motor and academic achievement in heterogeneous neighborhoods when designing early intervention programs, particularly with regard to sex differences, with the most significant disadvantage to girls with lower NSES.

Deep Learning-Based Trajectory Planning and Control for Autonomous Ground Vehicle Parking Maneuver

In this paper, a novel integrated real-time trajectory planning and tracking control framework capable of dealing with autonomous ground vehicle (AGV) parking maneuver problems is presented. In the motion planning component, a newly-proposed idea of utilizing deep neural networks (DNNs) for approximating optimal parking trajectories is further extended by taking advantages of a recurrent network structure. The main aim is to fully exploit the inherent relationships between different vehicle states in the training process. Furthermore, two transfer learning strategies are applied such that the developed motion planner can be adapted to suit various AGVs. In order to follow the planned maneuver trajectory, an adaptive learning tracking control algorithm is designed and served as the motion controller. By adapting the network parameters, the stability of the proposed control scheme, along with the convergence of tracking errors, can be theoretically guaranteed. In order to validate the effectiveness and emphasize key features of our proposal, a number of experimental studies and comparative analysis were executed. The obtained results reveal that the proposed strategy can enable the AGV to fulfill the parking mission with enhanced motion planning and control performance. <i>Note to Practitioners</i>&#x2014;This article was motivated by the problem of optimal automatic parking planning and tracking control for autonomous ground vehicles (AGVs) maneuvering in a restricted environment (e.g., constrained parking regions). A number of challenges may arise when dealing with this problem (e.g., the model uncertainties involved in the vehicle dynamics, system variable limits, and the presence of external disturbances). Existing approaches to address such a problem usually exploit the merit of optimization-based planning/control techniques such as model predictive control and dynamic programming in order for an optimal solution. However, two practical issues may require further considerations: 1). The nonlinear (re)optimization process tends to consume a large amount of computing power and it might not be affordable in real-time; 2). Existing motion planning and control algorithms might not be easily adapted to suit various types of AGVs. To overcome the aforementioned issues, we present an idea of utilizing the recurrent deep neural network (RDNN) for planning optimal parking maneuver trajectories and an adaptive learning NN-based (ALNN) control scheme for robust trajectory tracking. In addition, by introducing two transfer learning strategies, the proposed RDNN motion planner can be adapted to suit different AGVs. In our follow-up research, we will explore the possibility of extending the developed methodology for large-scale AGV parking systems collaboratively operating in a more complex cluttered environment.

Anti-Inertia Disturbance Control of Permanent Magnet Synchronous Motor Based on Integral Time-Varying Fast Terminal Sliding Mode

To improve the speed control performance of a permanent magnet synchronous motor (PMSM) when the moment of inertia changes during operation, this paper studies an anti-inertia disturbance method based on integral time-varying fast terminal sliding mode control (ITFTSMC). First, the mechanical motion equation of the permanent magnet synchronous motor is established to obtain the inverse relationship between the moment of inertia and the rate of speed change. Second, the extended state observer is designed to identify the moment of inertia online and improve real-time tracking accuracy. Third, to solve the motor speed fluctuation caused by the sudden inertia change, an integral time-varying fast terminal sliding mode control method is proposed, which improves both speed stability through integral time-varying and rapidity through the fast terminal. The identified moment of inertia is then updated to the integral time-varying fast terminal sliding mode controller in real time to meet the dynamic performance of the permanent magnet synchronous motor when said inertia changes. Finally, simulation and comparative experiments were used to verify the feasibility and effectiveness of the abovementioned proposed methods.

High-Precision Acquisition Method of Position Signal of Permanent Magnet Direct Drive Servo Motor at Low Speed

This paper studies a method for high-precision acquisition of position signals for permanent magnet direct drive servo motors at low speed. First of all, the problem of poor position feedback accuracy and sensor feedback delay in the low-speed operation of the permanent magnet direct drive servo motor is analyzed. Secondly, through analysis and simulation, it is found that the interpolation method can play a certain role in compensating the rotor position signal. However, when the speed is close to 0, the output signal of the sensor will fluctuate in a short time, which will affect the speed control accuracy. Therefore, this paper uses the observer method to achieve high-precision acquisition of the position signal of the permanent magnet direct drive servo motor at low speed. The observer method adopts the idea of combining the system model and closed-loop control. Additionally, it makes full use of the parameter information of the motor system. The control performance of the motor can be better guaranteed through the design of the observer parameters and the accuracy of the rotor position estimation result has been greatly improved. Finally, an experimental platform for permanent magnet direct drive servo motors is built, and the rotor position signal acquisition method based on the observer method is verified to have good performance through simulation and experiments. Not only the accuracy of the rotor position estimation result is improved, but also the motor control performance is improved, realizing the stable operation of the permanent magnet direct drive servo motor at low speed.

An Improved Robust Deadbeat Predictive Current Control for the Consequent-Pole Hybrid Excitation Motor

This paper proposes an improved robust deadbeat predictive current control (DPCC) to optimize the current loop control performance of the hybrid excitation motor (HEM) drive system, which is a multi-variable and strongly coupled system. First, the topology and mathematical model of a consequent-pole hybrid excitation machine (CPHEM) are introduced. In order to optimize the current performance of HEM drive system, a DPCC strategy is proposed, followed by a proposed stability analysis method for parameter mismatch. In addition, a robust deadbeat predictive current control (RDPCC) based on extended state observer (ESO) is proposed to suppress the effects of parameter mismatch in DPCC. Furthermore, in the predictive control of HEMs, the divergence of current prediction is caused by certain combinations of controller parameters, which is not resolved by RDPCC. To deal with this issue, the estimation equations of the virtual parameter are introduced into the current prediction of RDPCC. Finally, the effectiveness of proposed control strategy is verified in experiments.

Motion Strategy of Drilling Anchor Manipulator Based on Intelligent Optimization Algorithm

The drilling and anchoring robot is an important equipment to realize the intellectualization of fully mechanized coal mining. The control effect of its manipulator directly affects the safety and efficiency of the support operation of the drilling and anchoring robot. The control of drilling anchor manipulator usually adopts PID controller, but due to the limitation of integer order PID control algorithm and traditional empirical parameter adjustment, it is difficult to find a group of parameters with the best control effect in a short time, resulting in the failure of timely and accurate positioning of the end of the manipulator. In this paper, the combination of numerical modeling and simulation analysis is used to adjust the parameters of fractional order PIλDμ controller by using search algorithm (GPS) and applied to the motion control of manipulator; Based on the independent joint control theory, the single input single output system model of hydraulic cylinder at the joint of manipulator is established by using Matlab-Simulink software and fractional PIλDμ control technology. The fractional order control system is regulated by input shaping feedforward control technology(IS). The step response effect of the hydraulic system is further analyzed by setting input feedforward controllers with different gain ratios. The numerical simulation results show that the control strategy of “Input Shaping(IS) + Intelligent optimization algorithm (GPS) parameter adjustment + Fractional PIλDμ control technology” can effectively improve the spatial accurate positioning of mining manipulator. The research expand the application of intelligent control technology in the field of mining equipment.

Balancing-Prioritized Anti-Slip Control of a Two-Wheeled Inverted Pendulum Robot Vehicle on Low-Frictional Surfaces with an Acceleration Slip Indicator

When a two-wheeled inverted pendulum (TWIP) robot vehicle travels on slippery roads, the occurrence of wheel slip extremely threatens its postural stability owing to the loss of wheel traction. If a severe wheel slip happens between the driving wheels and contact surfaces, no control techniques can guarantee the driving performance and stability of the TWIP robots in the absence of an extra wheel slip control strategy. In this paper, a TWIP-compatible countermeasure against the wheel slip phenomena is investigated for enhancing the reliability of the vehicle and the robustness of the motion control performance on low-frictional surfaces. To this end, we propose a balancing-prioritized anti-slip control method based on the maximum transmissible torque estimation, which is activated only when a wheel slip is detected by the acceleration slip indicator utilizing accessible data from the IMU and wheel encoders. It is proved that the TWIP vehicles applying the proposed method can successfully cope with low frictional surfaces while maintaining postural stability. Finally, comparative simulations and experiments demonstrate the effectiveness and feasibility of the proposed scheme.

Oromotor Nonverbal Performance and Speech Motor Control: Theory and Review of Empirical Evidence.

This position paper offers a perspective on the long-standing debate concerning the role of oromotor, nonverbal gestures in understanding typical and disordered speech motor control secondary to neurological disease. Oromotor nonverbal tasks are employed routinely in clinical and research settings, but a coherent rationale for their use is needed. The use of oromotor nonverbal performance to diagnose disease or dysarthria type, versus specific aspects of speech production deficits that contribute to loss of speech intelligibility, is argued to be an important part of the debate. Framing these issues are two models of speech motor control, the Integrative Model (IM) and Task-Dependent Model (TDM), which yield contrasting predictions of the relationship between oromotor nonverbal performance and speech motor control. Theoretical and empirical literature on task specificity in limb, hand, and eye motor control is reviewed to demonstrate its relevance to speech motor control. The IM rejects task specificity in speech motor control, whereas the TDM is defined by it. The theoretical claim of the IM proponents that the TDM requires a special, dedicated neural mechanism for speech production is rejected. Based on theoretical and empirical information, the utility of oromotor nonverbal tasks as a window into speech motor control is questionable.

A hybrid HS-LS conjugate gradient algorithm for unconstrained optimization with applications in motion control and image recovery

This article presents a new hybrid conjugate gradient (CG) algorithm for solving unconstrained optimization problem. The search direction is defined as a combination of Hestenes–Stiefel (HS) and the Liu–Storey (LS) CG parameters and is close to the direction of the memoryless Broyden–Fletcher–Goldferb–Shanno (BFGS) quasi-Newton direction. In addition, the search direction is descent and bounded. The global convergence of the algorithm is obtained under the Wolfe-type and Armijo-type line searches. Numerical experiments on some benchmark test problems is carried out to depict the efficiency and robustness of the hybrid algorithm. Furthermore, a practical application of the algorithm in motion control of robot manipulator and image restoration is provided.

Research on internal model control and hybrid modulation strategy of a MW-level direct-drive PMSM for traction drives in electric locomotive

Aiming at the problem of motor control performance degradation caused by the cross-coupling and low switching frequency condition in the high-power direct-drive permanent magnet synchronous motor (PMSM) control system, a robust current control of PMSM based on internal model control (IMC) and hybrid pulse width modulation (PWM) is proposed and applied to the control of a MW-level direct-drive PMSM in electric locomotive traction system. Firstly, combined with the characteristics of the low switching frequency of the electric locomotive inverter, a hybrid PWM strategy is adopted. Secondly, the IMC of PMSM is designed, and the IMC is adopted to solve the problems of more control parameters and cross-coupling of direct-drive PMSM in the d-q axis under low switching frequency, which improves the robustness of the whole locomotive system. Finally, the experiments have been carried out on a 1.2 MW direct-drive PMSM for traction system in electric locomotive, the correctness and effectiveness of IMC and hybrid PWM strategy are verified.

Double three-phase permanent magnetic motor based on the smallest copper consumption and maximum torque single phase lack of phase compromise and tolerance error control

In multiphase motors, the consumption of stator copper and the output of torque are a pair of constraints affecting motor control performance. According to the constraint relationship between the them, this paper proposes a method to balance the consumption of copper and the output of torque by using the weight coefficient. Through this method, the efficient control of fault-tolerant motors can be realized, and the balance of output torque and efficiency can be realized. Finally, the accuracy of the control strategy is verified by the motor loading platform.

Improvements on the Dynamical Behavior of a HiL-Steering System Test Bench

Shorter available development times and fewer available vehicle prototypes have increased the subsystem-based investigation on test rigs within the automotive development process. Steering systems exhibit a direct interface to the driver, therefore, posing high requirements to the control performance of a test bench, especially for the perception of steering feel. This work proposes three approaches to improve the force control performance of permanent magnet linear motors incorporated on a steering test bench. The first method improves control accuracy when a harmonic force signal is introduced into the steering system by adjusting the reference force signal based on the identified peak values of the measured and reference forces. The second method allows the inclusion of the actuator’s inertia and the occurring ratios between steering wheel angle and rack displacement into the control scheme to reduce performance deterioration due to inertia. The third approach considers delay time in the actuator control and estimates its future position for delay compensation. A validation of the proposed methods is conducted, displaying an improvement for all three applications. The proposed methods extend the applicability of a steering test bench within the automotive development process by enabling more accurate and reproductible control performance.

An improved global fast terminal high-order sliding mode control strategy based on novel reaching law for improving PMLSM dynamic performance

Due to its better power density and efficiency, permanent magnet linear synchronous motor (PMLSM) is ideal for generating electricity with wave power device (WPD). Nevertheless, when the PMLSM for the WPD control system is impacted by changes in motor characteristics and interference from the outside environment, it typically is unable to deliver high-quality dynamic performance on schedule. Although sliding mode control (SMC) has been introduced to improve the robustness of the system greatly in recent decades, traditional methods still have problems such as slow dynamic response, poor anti-disturbance performance, large chattering amplitude, and phase delay. To solve the above defects in SMC, an improved global fast terminal high-order SMC strategy based on novel reaching law for improving PMLSM dynamic performance is proposed in this paper, as well as the sliding mode surface and control law of the speed controller is designed based on this control strategy. The proposed strategy would not only shorten the dynamic response time but also weaken the chattering magnitude in the sliding mode stage, improving the dynamic performance of both speed and current. The simulation results demonstrate that in comparison to the traditional PI and SMC control methods, the improved strategy proposed in this paper can further enhance the control performance of the linear motor, demonstrating the method’s enormous potential in the double closed-loop vector control system of PMLSM for WPD.

Data Normalization Methods of Hybridized Multi-Stage Feature Selection Classification for 5G Base Station Antenna Health Effect Detection

It is essential to assess human exposure to Fifth Generation (5G) Radiofrequency Electromagnetic Field (RF-EMF) signal from Base Station (BS) sources operating at Low Band 5G at 700 MHz, Sub-6 Band 5G at 3.5 GHz, and Millimeter Wave (mmWave) 5G at 28 GHz. This assessment will help determine whether 5G technology is safe for people. Inconsistent results were found in previous epidemiological studies on the health effects of radiation exposure from Mobile Phones (MP) and BS when normalization methods were used to prepare the data for Machine Learning (ML), which could lead to misclassification because of the dataset's quality. The effects on adult health are assessed in terms of physiological parameters (body temperature, heart rates, and blood pressure), cognitive performance (brain memory, motor control, and attention), Visual Analogue Scales (VAS) for well-being parameter, and Electromagnetic Field (EMF) perception parameter. The purpose of the research was to identify changes in the physiological parameters of adult individuals before, during, or after exposure to 5G signals, including Sham (No Exposure). 12 normalization methods are selected which are Z-score normalization (z score), Linear scaling (LS), Binary normalization (BNN), Bipolar normalization (BPN), Min-Max scaling (MMS), t-score normalization (t score), Decimal Inverse Logarithmic Scaled Normalization (DILSN), Relative Mean Normalization (RMN), Relative Standard Deviation Normalization (RSDN), Variation Normalization (VN), Robust Normalization (RN) and Relative Interquartile Normalization (RIN) and based on their F-value and p-value analyses, the three best normalization techniques are selected in this research. The original distribution of each parameter data variable is different, these techniques are beneficial for converting data so that it is dimensionless and has equivalent distributions. Based on the selection criteria for the hybridized Multi-Stage Feature Selection (MSFS) classification for 5G base station antenna health effect detection, BNN, MMS, and RSDN were named as the top three normalization techniques.

AN ASSESSMENT OF OCULAR DOMINANCE: A TERTIARY LEVEL HEALTH CARE CENTRE OBSERVATIONAL STUDY

Background: Ocular Dominance was first described by Rosenbach. Ocular Dominance is the tendency to physiological preference of one eye over another eye. However, the role of Ocular Dominance in processing of visual memory and analytic tasks is unknown. Research has showed significant effects of Ocular Dominance on visual perception, motor control, color vision and sports performance. The aim of present study was to determine Ocular Dominance by the Gundogan Method which is Near Far Alignment Test. Method:This study attempts to assess Ocular Dominance and the relationship between Ocular Dominance, Gender and cerebral laterality. It is a cross sectional, comparative and analytic type of study which is carried out on 170 healthy young adults with normal visual acuity. Among them 101 subjects were male and 69 subjects were female. Detection of Ocular Dominance was done by Gundogan Test. Result: Ocular Dominance was present in human beings. Ocular Dominance was most commonly present in Right Eye. Right Eye was dominant in (118) 69.4 % subjects and Left Eye was dominant in (52)30.6 % subjects. Gender based distribution of Right eye dominancy was 70.3%(n=71) for males and 68.1%(n=47) for females. Gender distribution For Left eye dominancy, was 29.7%(n=30) for males and 31.8%(n=22) for females (p value = 0.89). Conclusion: Ocular Dominance markedly observed in human beings, and Right eye dominancy is more preponderant than Left eye. It was also concluded that gender is not a major factor to affect eye dominancy. We insinuate that Gundogen Performance Test was appear to be a more practical and fluently applicable performance Test.

Research on Dynamic Performance of Independent Metering Valves Controlling Concrete-Placing Booms Based on Fuzzy-LADRC Controller

Motion control of truck-mounted concrete pump booms is an important part of concrete construction work. The quality of the boom movement will affect the efficiency and safety of construction operations. To make the boom move quickly and steadily, its hydraulic control system needs to be improved. In this article, we firstly combine independent metering control (IMC) and active disturbance rejection control (ADRC). The hydraulic system that controls the boom is meliorated by replacing the traditional proportional directional spool valve with four cartridge flow control valves to realize IMC. Meanwhile, a fuzzy linear ADRC algorithm is proposed for a new IMC hydraulic system where ADRC is linearized and combined with fuzzy logic to achieve better motion control performance of boom sections. According to the results of experiments conducted using all six boom sections, compared with the PID control algorithm, the fuzzy linear active disturbance rejection control (FLADRC) proposed in this article can improve the stability and rapidity of valves controlled by hydraulic cylinders by 10.0% to 66.7% in different working conditions.

Is the robotic rehabilitation that is added to intensive body rehabilitation effective for maximization of upper extremity motor recovery following a stroke? A randomized controlled study.

Trunk stabilization, which is a factor that directly affects the performance of affected upper-limb movements in stroke patients, is of critical importance in the performance of selective motor control. This study aimed to investigate the effects on upper-limb motor function of the addition of robotic rehabilitation (RR) and conventional rehabilitation (CR) to intensive trunk rehabilitation (ITR). A total of 41 subacute stroke patients were randomly allocated to two groups: RR and CR. Both groups received the same ITR procedure. Following ITR, a robot-assisted rehabilitation program of 60min, 5days a week, for 6weeks, was applied to the RR group, and an individualized upper-limb rehabilitation to the CR group. Assessments were made at baseline and after 6weeks using the Trunk Impairment Scale (TIS), Fugl-Meyer Upper Extremity Motor Evaluation Scale (FMA-UE), and Wolf Motor Function Test (WMFT). Improvements were obtained in the TIS, FMA-UE, and WMFT scores for both groups (p < 0.001), with no superiority detected between the groups (p > 0.05). The RR group scores were relatively high, but not to a statistically significant. When added to intensive trunk rehabilitation, the robot-assisted systems, which are recommended as a stand-alone therapy method, produced similar results to conventional therapies. This technology can be used as an alternative to conventional methods under appropriate conditions of clinical opportunity, access, time management, and staff limitations. However, when RR is combined with traditional interventions such as intensive trunk rehabilitation, it is essential to investigate if the real effect is due to the robotic rehabilitation or the accumulation of positive effects of excessive movement or force spread associated with trained muscles. This trial was retrospectively registered in the ClinicalTrials.gov with NCT05559385 registration number (25/09/2022).

RJVS: A novel, compact and integrated rotating joint with variable stiffness

A variable stiffness joint is a key element for structural deformation, which is widely used in morphing mechanisms including mechanical arms, folding wings, etc. By adjusting the stiffness of the joint according to the external environment and/or system instruction, the motion response of the mechanical system can be controlled and its security and stability can be guaranteed. However, to develop a variable stiffness joint with small size and large bearing capacity is still challenging. Also, an accurate mathematical model and control method to describe the variable stiffness joint is very important. In this paper, a novel, compact and integrated rotating joint with variable stiffness is developed, whose rotational stiffness and motion can be real-time controlled. A new configuration of a stiffness-adjusting module with superior mechanical properties is proposed by changing the arm length of cantilever leaf springs. Then a universal mechanical model of stiffness which adapts to a wide range of deformation is proposed. The status of the stiffness-adjusting module is changed by rope driven in order to minize the structure. Angular displacement sensors and torque sensors are integrated to observe the static and dynamic performance of the joint. According to the established rotational stiffness model, the stiffness of the joint is adjusted by reverse calculation of the motor’s rotating angle. A nonlinear Proportion-Integral-Derivative method based on a Genetic Algorithm is proposed for motion control. The experimental results show that the proposed joint, with large bearing capacity and a wide range of stiffness adjustment, has integration functions of driving and sensing, and can achieve a good motion control performance.

Reinforcement Learning-Based Prescribed Performance Motion Control of Pneumatic Muscle Actuated Robotic Arms With Measurement Noises

Featured with high power density, excellent flexibility, shock absorption capacity, etc., pneumatic muscles (PMs) promote the development of exoskeleton robots and rehabilitation equipment. However, the complex nonlinearities of PMs limit efficiency optimization in closed-loop control, while the force-displacement coupling, soft materials, deficient workspace, etc., make it more difficult to simultaneously increase motion speeds and ensure the safety of multiple PM-actuated (PMA) robots. Although force sensors can currently be replaced by applying state estimation techniques, the amplification effects of measurement noises still compromise control accuracy and stability in practice. To this end, this article proposes a reinforcement learning-based robust motion control method with the prescribed performance, which achieves efficient and satisfactory tracking control for PMA robotic arms. In particular, by elaborately incorporating an integral term, a robust generalized proportional integral observer is used to eliminate measurement noises. Meanwhile, by using an actor–critic network to optimize control performance, an error-transformation-based continuous controller is designed to guarantee the uniformly ultimately boundedness of tracking errors. Compared with most existing methods, this article provides the first solution to restrict the entire transient and steady-state performance of PMA robotic arms, improve the noise suppression capability, and optimize the control efficiency simultaneously. Finally, complete stability analysis based on Lyapunov techniques is provided, and several groups of hardware experiments demonstrate the practicability and robustness of the proposed method.

Neural substrates of continuous and discrete inhibitory control

Inhibitory control dysfunctions play an important role in psychiatric disorders but the precise nature of these dysfunctions is still not well understood. Advances in computational modeling of real-time motor control using a proportion–integral–derivative (PID) control framework have parsed continuous motor inhibition into a preemptive drive component (signified by the Kp parameter) and a reactive damping component (signified by the Kd parameter). This investigation examined the relationship between inhibitory control processing during a stop signal task and continuous motor control during a simulated one-dimensional driving task in a transdiagnostic sample of participants. A transdiagnostic psychiatric sample of 492 individuals completed a stop signal task during functional magnetic resonance imaging and a simple behavioral motor control task, which was modeled using the PID framework. We examined associations between the Kp and Kd parameters and behavioral indices as well as neural activation on the stop signal task. Individuals with higher damping, controlling for a drive, on the driving task exhibited relatively less strategic adjustment after a stop trial (indexed by the difference in go trial reaction time and by stop trial accuracy) on the stop signal task. Individuals with higher damping, controlling for a drive, additionally exhibited increased activity in the frontal and parietal regions as well as the insula and caudate during response inhibition on the stop signal task. The results suggest that computational indices of motor control performance may serve as behavioral markers of the functioning of neural systems involved in inhibitory control.