Redundant Actuation Research Articles

Kineto-static analysis of a hybrid manipulator consisting of rigid and flexible limbs with locking function for planar shape morphing

The incorporation of lockable passive backbones into active compliant morphing systems efficiently results in lightweight, high-load, and large deformation systems. However, there exist challenges in kineto-static analysis due to the interaction between rigid reconfigurable kinematic constraints and the nonlinear deformation of actuated flexible limbs. This paper addresses these issues by developing a kineto-static method to analyze the motion in a novel planar 3-DOF shape-morphing manipulator. The manipulator features two actuated flexible limbs with a lockable variable geometry truss (LVGT). In this study, two isostatic topologies are selected for reconfigurable motion control under external tip loads. A multi-step sequential control strategy is proposed to maneuver the manipulator's platform for desired poses. Then, a constrained-trajectory-based kinematic model is proposed for an inverse kinematic solution considering motion planning. Subsequently, a kineto-static model is introduced, considering constraints from rigid and flexible limbs, aiming to distribute distributing redundant actuation forces. Finally, nonlinear finite element analysis (FEA) and experiments are carried out to validate the effectiveness of the proposed method.

A novel type of parallel manipulator with flexible morphing platform

Abstract Parallel manipulators with flexible morphing platform (FMP) provide potential solution in various application fields, such as shape-morphing underwater robot, deformable wings, and human–machine interfaces. However, there is still lack of effective approach for the design and analysis of such novel type of parallel manipulator. In this article, a 9-UPS redundant actuation parallel manipulator with flexible morphing moving platform is designed as a representative of this kind of manipulator. Correspondingly, a deformation estimation and shape control approach for the FMP is presented. The proposed deformation estimation approach is designed based on the bending energy, which can achieve high calculation efficiency and avoid complex mechanical definition and calculation. And the proposed shape control approach is realized by utilizing a nonrigid ICP match algorithm, which can continuously deform the morphing platform to an arbitrary target surface. A prototype of the 9-UPS parallel manipulator is fabricated and analyzed as verification. The experiment results show that the proposed approach offers a promising avenue for the deformation estimation and shape control of the morphing platform.

Hybrid force-position coordinated control of a parallel mechanism with the number of redundant actuators equal to its DOF

To address the demands for precision and load-bearing capacity in the installation of building panels, a hybrid force-position driven robot with redundant actuation has been developed. The mechanical performance of this robot is primarily governed by its central parallel mechanism, which is equipped with redundant actuators matching the degrees of freedom. Non-redundant and redundant actuators are respectively responsible for position and force control. The inclusion of redundant force-driven joints has increased the internal coupling within the mechanism. To enhance the coordination between position and force control, a hybrid synchronized control method based on cross-coupling has been proposed. Initially, kinematic and dynamic models of the parallel structure were established. Under predefined trajectories, the theoretical inputs for each actuator were calculated using inverse kinematics and dynamics. Subsequently, employing the principle of cross-coupling, the torque output from the position actuators was used as feedback. This feedback was processed by a synchronized coordination controller to adjust the output force of the redundant force-driven joints. By adjusting the output force of the redundant actuators, the torque burden on the position actuators was effectively reduced, thereby enhancing the precision of position control. Additionally, under the same load conditions, smaller power actuators could be utilized for position control, reducing the overall weight of the robot and improving its load-to-weight ratio. To validate the effectiveness of the proposed control strategy, a robotic simulation environment was established. The simulation results demonstrated significant reductions in the average torque required by the position actuators across three different trajectories, with reductions of 91.43%, 54.56%, and 80.6% respectively. Finally, the load-bearing capacity and the load-to-weight ratio of the prototype were assessed. Experimental results confirmed that the prototype achieved a load-to-weight ratio of 15.83%, validating the effectiveness of the hybrid synchronized control method based on cross-coupling.

Concomitant Observer-Based Multi-Level Fault-Tolerant Control for Near-Space Vehicles with New Type Dissimilar Redundant Actuation System

This paper presents a concomitant observer-based multi-level fault-tolerant control (FTC) for near-space vehicles (NSVs) with a new type dissimilar redundant actuation system (NT-DRAS). When NSV flight control system faults occur in NT-DRAS and attitude-corresponding sensors, the NSV hybrid output states, including the concomitant observer usable states and the real system states, are applied to solve the FTC gain by using the linear quadratic regulator (LQR) technique. Furthermore, since NT-DRAS is used in NSVs, a multi-level (actuation system level and flight control level) FTC strategy integrating NT-DRAS channel switching and flight control LQR is proposed for complex and worsening fault cases. The most important finding is that though the proposed strategy is applicable for worsening fault cases in NSVs, systematic and accurate criteria for the process being performed are necessary and can improve the FTC efficiency with minimal FTC resources. Additionally, such criteria can improve the NSV’s responsiveness to comprehensive faults, provided that the real-time performance of the fault detection and diagnosis (FDD) scheme can be further optimized. The concomitant observer convergence and the multi-level FTC strategy have been verified by numerical simulations based on the Matlab/Simulink platform.

A multilayer neural‐network‐based fault estimation and fault tolerant control scheme for uncertain system

AbstractThis work introduces a new actuator fault estimation approach coupled with a fault‐tolerant control (FTC) strategy for uncertain systems in an output feedback framework. The proposed method involves constructing a Multi‐Layer Neural Network (MLNN) observer‐based fault estimation unit to accurately predict system states and potential faults in the actuator channel. An online control allocation (CA) scheme is then developed, utilizing the derived estimates to actively reconfigure the virtual control signals among the healthy redundant actuators in the event of actuator malfunction. Furthermore, an adaptive neural network‐based output integral sliding mode control scheme is designed based on the virtual control. This integration enhances the overall system's robustness and significantly reduces the chattering effect. The stability analysis of the proposed fault estimations scheme is initially performed using MLNN structure, followed by a comprehensive closed‐loop stability analysis to establish the stability of the entire system. Finally, the effectiveness of the proposed method is validated on a nonlinear six‐degree‐of‐freedom model of multirotor unmanned aerial vehicle aircraft. Numerical simulations under different fault and failure scenarios validate the efficacy of the proposed method. The comparative analysis of the proposed scheme is conducted with the static output feedback control allocations and adaptive allocation strategy. This analysis focuses on evaluating performance using metrics such as root mean square error and mean square deviation, particularly in the presence of faults and failures. The results demonstrate the superior performance of the proposed scheme in fault/failure conditions.

On the analysis and control of a bipedal legged locomotion model via partial feedback linearization

In this study, we introduce a new model for bipedal locomotion that enhances the classical spring-loaded inverted pendulum (SLIP) model. Our proposed model incorporates a damping term in the leg spring, a linear actuator serially interconnected to the leg, and a rotary actuator affixed to the hip. The distinct feature of this new model is its ability to overcome the non-integrability challenge inherent in the conventional SLIP models through the application of partial feedback linearization. By leveraging these actuators, our model enhances the stability and robustness of the locomotion mechanism, particularly when navigating across varied terrain profiles. To validate the effectiveness and practicality of this model, we conducted detailed simulation studies, benchmarking its performance against other recent models outlined in the literature. Our findings suggest that the redundancy in actuation introduced by our model significantly facilitates both open-loop and closed-loop walking gait, showcasing promising potential for the future of bipedal locomotion, especially for bio-inspired robotics applications in outdoor and rough terrains.

Design and Analysis of a Symmetric Joint Module for a Modular Wire-Actuated Robotic Arm with Symmetric Variable-Stiffness Units

Collaborative robots are used in scenarios requiring interaction with humans. In order to improve the safety and adaptability of collaborative robots during human–robot interaction, this paper proposes a modular wire-actuated robotic arm with symmetric variable-stiffness units. The variable-stiffness unit is employed to extend the stiffness-adjustment range of the robotic arm. The variable-stiffness unit is designed based on flexure, featuring a compact and simple structure. The stiffness–force relationship of the variable-stiffness unit can be fitted by a quadratic function with an R-squared value of 0.99981, indicating weak nonlinearity. Based on the kinematics and stiffness analysis of the symmetric joint module of the robotic arm, the orientation of the joint module can be adjusted by regulating the length of the wires and the stiffness of the joint module can be adjusted by regulating the tension of the wires. Because of the actuation redundancy, the orientation and stiffness of the joint module can be adjusted synchronously. Furthermore, a direct method is proposed for the stiffness-oriented wire-tension-distribution problem of the 1-DOF joint module. A simulation is carried out to verify the proposed method. The simulation result shows that the deviation between the calculated stiffness and the desired stiffness was less than 0.005%.

Electro-hydraulic SBW fault diagnosis method based on novel 1DCNN-LSTM with attention mechanisms and transfer learning

The Electro-hydraulic Steer-by-Wire (EH-SBW) is the future trend of commercial vehicle steering systems due to their dual actuator redundancy. The accurate fault diagnosis of the EH-SBW is an important basis for ensuring the safety of commercial vehicles. However, due to the strong coupling between dual actuators, rapid load changes and difficult sampling lead to the difficulty of existing techniques to meet the fault diagnosis of EH-SBW. Therefore, we propose the EH-SBW fault diagnosis method based on novel 1DCNN-LSTM with attention mechanisms and transfer learning, which contains a fault type diagnosis neural network (FTDNN) model and a fault degree estimation neural network (FDENN) model. The steering feature extractor (SFE) in the FDENN model fuses the driver’s long time-domain driving trend signals and the short time-domain signals from the steering system to extract the feature map, so that the feature map have more valid information in the case of fast load changes. Based on the result of the FTDNN and the feature map, the scaling attention layer in the FDENN amplifies the features that are favourable to the fault degree estimation accuracy and suppresses the expression of unfavourable features, which reduces the impact of strong coupling relationships on the fault degree estimation accuracy. The dynamic sample allocation strategy performs transfer learning of the SFE and scaling attention layer through a small number of samples, which enables the FDENN model to effectively estimate the degree of failure of the hydraulic mechanism. The experimental results show that the diagnostic accuracy of the proposed method is 94% when the electric mechanism fails. The diagnostic accuracy of the proposed method is 92% when the hydraulic mechanism fails.

Motion Learning and Generalization of Musculoskeletal Robot Using Gain Primitives

Organisms have an innate ability to rapidly produce diverse and flexible movement. Biological motor systems are composed of highly redundant muscle actuators and have strongly nonlinear dynamic characteristics. How organisms cope with such complex system is still an open question. Motor primitive theory proposed by neuroscientists has provided a convincing explanation for this extraordinary ability and have supported by several biological evidence. In this paper, based on the gain primitive model of cortical network proposed by neuroscientists, new algorithms for learning and combining gain primitives were designed to control musculoskeletal and robotic system with highly redundant actuators. It can realize dimensionality reduction of controll from the number of actuators to the number of primitives. A parameter adaptation algorithm inspired by monoamines modulation mechanism was applied to improve the learning efficiency of gain primitive. Learned motion experiences and primitives were introduced to effectively generate new movements. Validation experiments were carried out on a musculoskeletal model with 9 muscles and an articulated robot Baxter with redundant actuators. The experimental results demonstrated that the recurrent neural network modulated by gain primitives can generate high-dimensional control signals and realize efficient motion generalization for practical systems. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This paper applied a biologically plausible control method with gain primitive to practical systems with highly redundant actuators. To reduce the cumulative error occurring in learning gain primitive for control networks with high dimensional output, a bioinspired Hebbian learning rule was applied to train a recurrent neural network for optimizing the gain primitive. During the training process, systematic entropy was introduced as an index to adaptively adjust learning parameters, so as to reduce the exploration cost and improve the learning stability of the system. To realize easy implementation and generalization, prior motion experiences were introduced into the initialization of the optimization algorithm, and motion generalization was realized by using the particle swarm optimization algorithm. The algorithms designed in this paper are applicable to complex systems with highly redundant actuators such as humanoid robots.

Performance evaluation and dimensional optimization design of planar 6R redundant actuation parallel mechanism

AbstractAiming at the problems of small good workspace, many singular configurations, and limited carrying capacity of non-redundant parallel mechanisms, a full-redundant drive parallel mechanism is designed and developed, and its performance evaluation, good workspace identification, and scale optimization design are studied. First, the kinematics analysis of the planar 6R parallel mechanism is completed. Then, the motion/force transmission performance evaluation index of the mechanism is established, and the singularity analysis of the mechanism is completed. Based on this, the fully redundant driving mode of the mechanism is determined, and the good transmission workspace of the mechanism in this mode is identified. Then, the mapping relationship between the performance and scale of the mechanism is established by using the space model theory, and the scale optimization of the mechanism is completed. Finally, the robot prototype is made according to the optimal scale, and the performance verification is carried out based on the research of dynamics and control strategy. The results show that the fully redundant actuation parallel mechanism obtained by design optimization has high precision and large bearing capacity. The position repeatability and position accuracy are 0.053 mm and 0.635 mm, respectively, and the load weight ratio can reach 15.83%. The research results of this paper complement and improve the performance evaluation and scale optimization system of redundantly actuated parallel mechanisms.

A pair of two-stage Kalman filters to detect and isolate failures in aircraft with redundant actuators

As critically-safe systems, aircraft must be able to adapt if a failure occurs. A fast failure detection may reduce the possibility of loss of control in-flight. Model-based Failure Detection and Isolation Systems (FDIS) have been proposed to identify actuation failures in aircraft, being most of them based on Multiple Models approaches. An additional challenge in FDIS strategies for aircraft is the high level of actuation redundancy. This article proposes to use only a pair of Two-Stage Kalman Filters (TSKF) for detecting and isolating failures. A Feed-Forward Differential (FFD) is introduced to facilitate the fault isolation process, providing further information with low perturbation during flight. The TSKF results are interpreted by a Supervisory Loop, which automatically diagnoses the aircraft for actuation failures. The proposed solution is validated using the Boeing 747 GARTEUR RECOVER Benchmark Simulator. The proposed solution has an overall better response when compared to a multiple-models solution of the literature with an 85% reduction in the computational time.

Design of Low-Cost Modular Bio-Inspired Electric-Pneumatic Actuator (EPA)-Driven Legged Robots.

Exploring the fundamental mechanisms of locomotion extends beyond mere simulation and modeling. It necessitates the utilization of physical test benches to validate hypotheses regarding real-world applications of locomotion. This study introduces cost-effective modular robotic platforms designed specifically for investigating the intricacies of locomotion and control strategies. Expanding upon our prior research in electric-pneumatic actuation (EPA), we present the mechanical and electrical designs of the latest developments in the EPA robot series. These include EPA Jumper, a human-sized segmented monoped robot, and its extension EPA Walker, a human-sized bipedal robot. Both replicate the human weight and inertia distributions, featuring co-actuation through electrical motors and pneumatic artificial muscles. These low-cost modular platforms, with considerations for degrees of freedom and redundant actuation, (1) provide opportunities to study different locomotor subfunctions-stance, swing, and balance; (2) help investigate the role of actuation schemes in tasks such as hopping and walking; and (3) allow testing hypotheses regarding biological locomotors in real-world physical test benches.

Singularity-Free 3-Leg 6-DOF Spatial Parallel Robot with Actuation Redundancy

Singularity-Free 3-Leg 6-DOF Spatial Parallel Robot with Actuation Redundancy

Fault-tolerant controller design based on adaptive backstepping for tower cranes with actuator faults

Due to the widespread application and significant investment required for a single crane, there is an increased emphasis on crane safety and service life. Fault-tolerant control as an effective solution to unexpected faults has been widely studied recently. However, most fault-tolerant control methods require redundant actuators or a complex design process, which is unsuitable for the tower crane. Following these problems, a fault-tolerant controller based on an adaptive backstepping technique is proposed. Firstly, the system states are reconstructed and written as a cascade system. Secondly, a fixed-time convergence optimized backstepping controller is proposed to achieve smooth control of the tower crane without generating sudden or abrupt values. Then, an adaptive approach has been proposed to update fault parameters for the crane system in case of a sudden fault occurrence. Finally, after conducting comparison tests, it has been determined that the proposed controller not only performs exceptionally well in terms of position accuracy and swing elimination, but also maintains a satisfactory control performance when faced with sudden faults.

Fractional-order PID controller tuned by particle swarm optimization algorithm for a planar CDPR control

The use of cable-driven parallel robots (CDPRs) has been steadily increasing across various sectors due to their expansive workspaces, impressive payload-to-mass ratios, and cost-effective designs. Controlling these robots, particularly those with substantial actuation redundancy, can present challenges. This research paper proposes the implementation of a fractionalorder proportional-integral-derivative (FOPID) controller to effectively regulate the end-effector of a planar CDPR with four actuation cables. The parameters of the controller are fine-tuned using the particle swarm optimization (PSO) algorithm to ensure optimal performance. The proposed controller's performance is evaluated through two numerical experiments: target tracking and trajectory tracking using a point-to-point approach. Furthermore, a comparative study is conducted to highlight the controller's performance, comparing the proposed FOPID controller with both the classical PID controller and an optimized PID controller. The achieved results demonstrate that the proposed controller exhibits superior performance in terms of tracking accuracy and smoothness of control signals when compared to the other controllers under investigation. As a result, the proposed controller design represents a substantial advancement in control performance and can be regarded as a promising control strategy for CDPRs.

Performance Analysis and Optimization Design of a Dual-Mode Reconfigurable Ankle Joint Parallel Rehabilitation Mechanism

In this study, a dual-mode reconfigurable parallel ankle joint rehabilitation mechanism is proposed to meet the needs of patients in different ankle rehabilitation stages. This mechanism can switch between the 1T2R (where R represents rotation and T represents translation) and 2T1R motion modes. The screw theory and the modified G-K formula were used to analyze and verify the degree of freedom of the mechanism. The non-parasitic motion characteristics were analyzed by examining the topological structure of the mechanism. An inverse kinematics model was established using the closed-loop vector method, and the mechanism’s singularity was analyzed based on the Jacobian matrix. The Jacobian matrix and the numerical method were used to compare and analyze the workspace index, the rotational dexterity index and the load capacity performance index before and after the introduction of branched chains with actuation redundancy. A particle swarm optimization algorithm was used to optimize the geometric dimensional parameters of the mechanism. The results show that the mechanism exhibits the characteristics of a parallel mechanism without parasitic motion in the two motion modes. Using branched chains with actuation redundancy can significantly improve the rotational dexterity and load capacity performance index, without affecting the workspace index. Compared to the original mechanism, the kinematic performance of the optimized mechanism is significantly improved. It is concluded that the proposed mechanism can meet the needs of ankle joint activity training in the 1T2R motion mode and the needs of ankle joint proprioception training in the 2T1R motion mode, which can better meet the needs of patients in different rehabilitation stages.

Controlling Musculoskeletal System with Spinal Cord-Inspired Constraint Force Field Primitives

Organisms naturally have extraordinary motor ability. They can autonomously control the highly redundant and nonlinear musculoskeletal system to complete fast and flexible movements. Motor primitive theory proposed that a complex movement can be realized by modular organization of simple movement patterns. This theory provides a convincing theoretical basis for explaining the extraordinary motor ability of organisms, and has been supported by plenty of experimental evidences in neurosciences. As a kind of typical motor primitive related to the modular regulation of the spinal motor system, convergent force field was thought to be significant for reducing the complexity of controlling numerous muscles and joints simultaneously. Inspired by the primitive property of convergent force field, in this paper, we proposed a new algorithm to efficiently construct constraint force field on musculoskeletal system with highly redundant actuators by taking optimized parameters of constraint force field as motor primitives. Compared with previous methods of constructing constraint force field, the proposed algorithm is able to reduce the dimension of the solution space so as to effectively improve the computational efficiency of constructing constraint force field in musculoskeletal system with redundant muscles. Validation experiments were carried out in a musculoskeletal system with 10 redundant muscles. The number of optimization iterations and computational time required for constructing constraint force field at a new position were significantly reduced. The system can accurately reach the target position using constant activations. This work may bring in new inspiration for realizing efficient and accurate motion control of musculoskeletal robots with limited feedback accuracy.

Fault Mode Analysis and Convex Optimization-Based Fault-Tolerant Control for New Type Dissimilar Redundant Actuation System of Near Space Vehicle

A new type dissimilar redundant actuation system (NT-DRAS), which is composed of an electro-hydrostatic actuator (EHA) and an electro-mechanical actuator (EMA), is applied in high value unmanned aerial vehicles such as the future near space vehicles to improve their reliability and performance index simultaneously. Further improvement in the flight safety is achieved with the fault-tolerant control (FTC) technique which deals with system faults. This paper proposes a novel convex optimization-based fault-tolerant control (CO-FTC) strategy for the NT-DRAS subject to gradual faults which are included in the state space representation of the system. A convex analysis-based treatment for system uncertainty caused by gradual faults is applied to determine the control gain matrix. The existence condition of the control gain matrix is optimized in the linear matrix inequality (LMI) form. Finally, the determined subsystems based on the novel technique is used to solve the modeled robust FTC problem. Case studies of NT-DRAS subject to different gradual faults have been accomplished to illustrate the FTC necessity for NT-DRAS. Furthermore, the effectiveness of the proposed CO-FTC strategy is validated by comparative analysis of the simulation results.

Time‐optimal trajectory planning of a redundantly actuated planar parallel robot driven with series elastic actuators

AbstractThis paper addresses the time‐optimal trajectory planning of a redundantly actuated parallel robot driven by series elastic actuators. Here, the actuation redundancy allows prestressing the elastic components of the drives and enables more degrees of freedom in the trajectory planning. To this end, a time‐optimal control problem is formulated that takes also the physical constraints into account, that is actuation torques, task and joint space limits and self‐collisions. A flatness‐based parametrization of the dynamics is used in the nonlinear optimization problem and solved with the multiple shooting technique within the CasADi framework using IPOPT as a solver. Results are shown and compared to time‐optimal trajectories, both redundantly and non‐redundantly actuated with rigid drives.

Progress in Redundant Electromechanical Actuators for Aerospace Applications

The power to move aircraft control surfaces has advanced from being manually generated (by the pilot and transmitted via rods and links) to electrically transmitted (via wires) to operate control surface actuators. Various hydraulic, electromagnetic, and electromechanical architectures have been developed to provide the necessary power and to maintain the expected redundancy. Numerous aircraft actuator system designs have been proposed in the past decades, but a comprehensive review has yet to be undertaken. This review paper aims to fill this gap by providing a critical review of the actuation system designs developed for a variety of aircraft. The review focuses on aircraft actuator system designs, namely: electrohydraulic actuator systems, electromechanical actuator systems, and the force-fighting effect in redundant actuation systems. The significance and operational principle of each actuator system are critically analysed and discussed in the review. The paper also evaluates the solution proposed to address force-fight equalization (or force-fight cancelation) in force or torqued-summed architectures. Future trends in redundant actuation system development with reduced force-fighting effect in aircraft actuator systems are also addressed.