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The energy finance sector is characterized by its complexity and volatility, driven by fluctuating commodity prices, regulatory changes and evolving market dynamics. Energy companies face financial, regulatory and operational risks, relying on historical data and static risk assessment frameworks that may not accurately reflect real-time market changes. This research aims to develop an energy finance risk compliance early warning system, leveraging a machine learning approach to enhance the early detection of compliance risks, enabling proactive decision-making and improving organizational resilience. Initially, data were collected from various sources, including historical financial records, market trends and regulatory frameworks. These data are essential for developing an early warning system that aims to enhance compliance risk detection in the energy sector. The collected data are preprocessed using cleaning and normalization and prepared for analysis. Exploratory Data Analysis (EDA) was conducted using statistical methods such as correlation analysis and regression analysis to identify patterns and relationships between variables. The study proposes a novel Revenue Optimizer with a weighted Support Vector Machine (RO-WSVM) model to enhance risk detection and ensure regulatory compliance in energy finance. Optimizing revenue while adhering to compliance standards provides proactive insights for effective risk management and decision-making. The result demonstrates that the application of the proposed RO-WSVM model works successfully and this is because RO-WSVM has higher accuracy (90%), error value (0.1%) and less time taken to process the data than any other model (6s). The study highlights that the innovative approach RO-WSVM enhances the prediction model in the finance risk compliance early warning of energy finance.

This paper contains an adjustment of the stiffness and control algorithm of an ankle joint to maintain the posture of a humanoid biped walking robot using an IMU (Inertial measurement Unit) in single support phase. One of the difficulties in maintaining the balance of a robot in single support phase involves the sole of the robot, which is easy to separate from the ground. This phenomenon is caused by an external disturbance or when the sole lands on uneven terrain. A method of adjusting the compliance in the ankle joint is introduced to reduce the tendency of this type of occurrence. In order to maintain the posture of a robot with a compliant ankle joint, a posture controller composed of a body balancing controller and a vibration reduction controller is adopted. A walking experiment is implemented using a compliant ankle joint and the posture controller. The proposed method is shown to enable rapid walking as well as walking on uneven terrain.

Biomechanics research shows that the ability of the human locomotor system depends on the functionality of a highly compliant motor system that enables a variety of different motions (such as walking and running) and control paradigms (such as flexible combination of feedforward and feedback controls strategies) and reliance on stabilizing properties of compliant gaits. As a new approach of transferring this knowledge into a humanoid robot, the design and implementation of the first of a planned series of biologically inspired, compliant, and musculoskeletal robots is presented in this paper. Its three-segmented legs are actuated by compliant mono- and biarticular structures, which mimic the main nine human leg muscle groups, by applying series elastic actuation consisting of cables and springs in combination with electrical actuators. By means of this platform, we aim to transfer versatile human locomotion abilities, namely running and later on walking, into one humanoid robot design. First experimental results for passive rebound, as well as push-off with active knee and ankle joints, and synchronous and alternate hopping are described and discussed. BioBiped1 will serve for further evaluation of the validity of biomechanical concepts for humanoid locomotion.

Enhancing energy efficiency of bipedal walking is an important research problem that has been approached by design of recently developed compliant bipedal robots such as CoMan. While compliance leads to energy efficiency, it also complicates the walking control system due to further under-actuated degrees of freedom (DoF) associated with the compliant actuators. This problem becomes more challenging as the constrained motion of the robot in double support is considered. In this paper this problem is approached from a multi-variable geometric control aspect to systematically account for the compliant actuators dynamics and constrained motion of the robot in double support phase using a detailed electro-mechanical model of CoMan. It is shown that the formulation of constraint subspace is non-trivial in the case of non-rigid robots. A step-wise numerical algorithm is provided and the effectiveness of the proposed method is illustrated via simulation, using a ten DoF model of CoMan.

Compliance control is highly relevant to human safety in human–robot interaction (HRI). This paper presents a review of various compliance control techniques. The paper is aimed to provide a good background knowledge for new researchers and highlight the current hot issues in compliance control research. Active compliance, passive compliance, adaptive and reinforcement learning-based compliance control techniques are discussed. This paper provides a comprehensive literature survey of compliance control keeping in view physical human robot interaction (pHRI) e.g., passing an object, such as a cup, between a human and a robot. Compliance control may eventually provide an immediate and effective layer of safety by avoiding pushing, pulling or clamping in pHRI. Emerging areas such as soft robotics, which exploit the deformability of biomaterial as well as hybrid approaches which combine active and passive compliance are also highlighted.

Compliance control is highly relevant to human safety in human–robot interaction (HRI). This paper presents multi-dimensional compliance control of a humanoid robot arm. A dynamic model-free adaptive controller with an anti-windup compensator is implemented on four degrees of freedom (DOF) of a humanoid robot arm. The paper is aimed to compliment the associated review paper on compliance control. This is a model reference adaptive compliance scheme which employs end-effector forces (measured via joint torque sensors) as a feedback. The robot's body-own torques are separated from external torques via a simple but effective algorithm. In addition, an experiment of physical human robot interaction is conducted employing the above mentioned adaptive compliance control along with a speech interface. The experiment is focused on passing an object (a cup) between a human and a robot. Compliance is providing an immediate layer of safety for this HRI scenario by avoiding pushing, pulling or clamping and minimizing the effect of collisions with the environment.

Most robots are today controlled as being entirely rigid. But often, as for HRP-2 robot, there are flexible parts, intended for example to absorb impacts. The deformation of this flexibility modifies the orientation of the robot and endangers balance. Nevertheless, robots have usually inertial sensors inertial measurement units (IMUs) to reconstruct their orientation based on gravity and inertial effects. Moreover, humanoids have usually to ensure a firm contact with the ground, which provides reliable information on surrounding environment. We show in this study how important it is to take into account these information to improve IMU-based position/orientation reconstruction. We use an extended Kalman filter to rebuild the deformation, making the fusion between IMU and contact information, and without making any assumption on the dynamics of the flexibility. We show how, with this simple setting, we are able to compensate for perturbations and to stabilize the end-effector's position/orientation in the world reference frame. We show also that this estimation is reliable enough to enable a closed-loop stabilization of the flexibility and control of the center of mass (CoM) position with the simplest possible model.

When executing tasks, robots are required to demonstrate compliance to unexpected external disturbances or human–robot interactions, and return to the demanded posture when the disturbances or contacts are removed. Traditional Virtual Model Control (VMC) requires precise gravitational compensation to accurately control the posture of a robot. Hence, load variations or other uncertain unmodeled factors in the robot will result in offsets to its balance posture, which makes the robot deviate from the demanded posture when it is in a static state. To reject this offset without sacrificing the compliance of the robot, an adaptive controller is proposed in this paper to implement adaptive compliance on the robot, which makes the robot robust to the variations in gravitational loads in the double leg support phase. The adaptive controller is a combination of the VMC controller and an online gravitational loads estimator, in which the estimator is derived in the double leg support phase to estimate the values of these parameters and obtains an online updating law based on a Kalman optimal estimator. Then, a Lyapunov function is designed to modify and combine the controller and the online gravitational loads estimator. The experiments are conducted on a 4 DoF bipedal robot in the sagittal plane to validate the effectiveness of the controller and show that, by estimating the gravitational loads of the robot, the effects of load variations on balance posture are rejected without sacrificing compliance.

Joints’ backdrivability is desired for robots that perform tasks contacting the environment, in addition to the high torque and fast response property. The electro-hydrostatic actuator (EHA) is an approach to realize force-sensitive robots. To experimentally confirm the performance of a biped robot driven by EHAs, we developed the fully electro-hydrostatically driven humanoid robot Hydra. In this paper, we evaluate the whole-body control performance realized by integrating encoders, pressure sensors, and IMU through a high-speed communication bus to the distributed whole-body control system. We report the first example of bipedal locomotion by an EHA-driven robot in both position-controlled and torque-controlled approaches. The robot could keep the balance even when the ground condition was changing impulsively and utilize its high joint backdrivability to absorb a disturbance by the null space compliance. We also report practical challenges in implementing compliant control in real hardware with limitations in parameter accuracy, torque, and response. We experimentally confirmed that the resolved viscoelasticity control (RVC), which has indirect feedback of operational space tasks by projecting the operational space feedback gain to the joint space one, was effective to tune a proper gain to stabilize the center-of-mass motion while avoiding joint-level oscillation invoked by the control bandwidth limitation. The attached multimedia file includes the video of all experiments presented in the paper.

The energetic effects of knee locking and addition of linear elastic members to different joints of a seven-link fully actuated planner bipedal robot were studied. The focus was on the reduction of energy consumption during walking. An impactless walking gait was studied and the energetic cost of walking was determined without joint stiffness and knee locking as a baseline for comparison. The gait trajectory was then optimized by adding spring to different joints, energetic cost of walk was then calculated at different walking speeds. Support knee was then mechanically locked and gait was optimized to find the walking cost. The energetic cost of walking determined for the above two cases was then compared to the baseline cost. It was observed that addition of torsional springs at both hips can reduce the walking cost up to 38%, support hip up to 40% with spring stiffness as an optimization parameter for both cases while mechanically locking the support knee can reduce the cost of walking up to 92% at slow walking speeds with gait and knee locking angle optimized.

We propose a mechanical design for the knee joints of a planar bipedal robot. Each knee joint is composed of cross four-bar with springs. The dynamic model of a planar bipedal robot with cross four-bar knees and a parametric optimization problem are presented to produce a set of optimal reference trajectories. We use these trajectories to compare the performance of the bipedal robot with respect to different physical characteristics of the knee joints.