Control Of Biped Robot Research Articles

Bionic Walking Control of a Biped Robot Based on CPG Using an Improved Particle Swarm Algorithm

In the domain of bionic walking control for biped robots, optimizing the parameters of the central pattern generator (CPG) presents a formidable challenge due to its high-dimensional and nonlinear characteristics. The traditional particle swarm optimization (PSO) algorithm often converges to local optima, particularly when addressing CPG parameter optimization issues. To address these challenges, one improved particle swarm optimization algorithm aimed at enhancing the stability of the walking control of biped robots was proposed in this paper. The improved PSO algorithm incorporates a spiral function to generate better particles, alongside optimized inertia weight factors and learning factors. Evaluation results between the proposed algorithm and comparative PSO algorithms were provided, focusing on fitness, computational dimensions, convergence rates, and other metrics. The biped robot walking validation simulations, based on CPG control, were implemented through the integration of the V-REP (V4.1.0) and MATLAB (R2022b) platforms. Results demonstrate that compared with the traditional PSO algorithm and chaotic PSO algorithms, the performance of the proposed algorithm is improved by about 45% (two-dimensional model) and 54% (four-dimensional model), particularly excelling in high-dimensional computations. The novel algorithm exhibits a reduced complexity and improved optimization efficiency, thereby offering an effective strategy to enhance the walking stability of biped robots.

Balance and Walking Control for Biped Robot Based on Divergent Component of Motion and Contact Force Optimization

Balance and Walking Control for Biped Robot Based on Divergent Component of Motion and Contact Force Optimization

Human-Simulated Intelligent Walking Control for Biped Robots

Human-Simulated Intelligent Walking Control for Biped Robots

Adaptive robust control for biped walking under uncertain external forces

Adaptability and robustness are important expressions of the intelligent walking ability of biped robots. This paper is concerned with the problem of dynamical biped walking and robust control of biped robots under external forces. Due to the characteristics of strong coupling and hybrid, the robotic system is modeled as a rigid kinematic chain with Lagrange equations. A novel adaptive feedback controller is proposed based on sliding mode control (SMC) and hybrid zero dynamics. The novelty of the proposed work lies in taking the uncertainty of upper-bound error into consideration. The hybrid robust control is mentioned to approximate unknown dynamic functions with the adaptive weight. The restricted Poincare return map is utilized to analyze the stability of a nonlinear impulsive system. It ensures that the flow of the continuous subsystem can pass through the impact cross section. Finally, the simulation results illustrate that the proposed adaptive SMC control system can favorably track the reference trajectories, even when a fault occurs, which verifies the effectiveness of the proposed method.

Motor control insights on walking planner and its stability

The application of biomechanics and motor control models in the control of bipedal robots (humanoids and exoskeletons) has revealed limitations of our understanding of human locomotion. A recently proposed model uses the potential energy for bipedal structures to model the bipedal dynamics, allowing us to predict the system dynamics from its kinematics. This work proposes a task-space planner for human-like straight locomotion that targets application in rehabilitation robotics and computational neuroscience. The proposed architecture is based on the potential energy model and employs locomotor strategies from human data. The model generates Centre of Mass (CoM) trajectories, foot swing trajectories and the Base of Support (BoS) over time. The data show that the proposed architecture can generate behaviour in line with human walking strategies for both the CoM and the foot swing. Despite the CoM vertical trajectory not being as smooth as a human trajectory, the proposed model significantly reduces the error in the estimation of the CoM vertical trajectory compared to the inverted pendulum models. The proposed model can also assess stability based on the body kinematics embedding currently used in clinical practice. However, the model also implies a shift in the interpretation of the spatiotemporal parameters of the gait, which are now determined by the conditions for the equilibrium and not vice versa. In other words, locomotion is a dynamic reaching where the motor primitives are also determined by gravity.

Central Pattern Generator with Defined Pulse Signals for Compliant-Resistant Control of Biped Robots.

For biped robots, the ability to maintain balance under external forces is an essential requirement. Inspired by human beings' behaviors to resist external forces, a compliant-resistant balance-control method is proposed to keep the biped robot balance subjected to an external force. A model-free trajectory generator is designed based on the central pattern generator (CPG) to generate compliant-resistant human-like behavior. The CPG pattern generator generates the desired pulse signal utilizing Matsuoka's CPG. The signal modulator applies the defined signal to the robot's center of mass (CoM) to generate the workspace trajectory when standing on double feet. Moreover, when standing on single foot, the output signal of the CPG will directly act on the hip joint of the robot to generate the joint space trajectory. Furthermore, the motion engine calculates the workspace trajectory into joint sequence values. The proposed control strategy can generate defined pulse signals to realize compliant-resistant balance control for biped robots. The control strategy proposed in this paper is verified in the NAO simulation environment.

Reinforcement learning control for a three-link biped robot with energy-efficient periodic gaits

Reinforcement learning control for a three-link biped robot with energy-efficient periodic gaits

Balanced Standing on One Foot of Biped Robot Based on Three-Particle Model Predictive Control.

Balancing is a fundamental task in the motion control of bipedal robots. Compared to two-foot balancing, one-foot balancing introduces new challenges, such as a smaller supporting polygon and control difficulty coming from the kinematic coupling between the center of mass (CoM) and the swinging leg. Although nonlinear model predictive control (NMPC) may solve this problem, it is not feasible to implement it on the actual robot because of its large amount of calculation. This paper proposes the three-particle model predictive control (TP-MPC) approach. It combines with the hierarchical whole-body control (WBC) to solve the one-leg balancing problem in real time. The bipedal robot's torso and two legs are modeled as three separate particles without inertia. The TP-MPC generates feasible swing leg trajectories, followed by the WBC to adjust the robot's center of mass. Since the three-particle model is linear, the TP-MPC requires less computational cost, which implies real-time execution on an actual robot. The proposed method is verified in simulation. Simulation results show that our method can resist much larger external disturbance than the WBC-only control scheme.

Disturbance rejection for biped robots during walking and running using control moment gyroscopes

AbstractKeeping balance in movement is an important premise for biped robots to complete various tasks. Now, the balance control of biped robots mainly depends on the cooperation of various joints of the robot's body. When robots move faster, the adjustment allowance of joints is reduced, and the robot's anti‐disturbance ability will inevitably decline. To solve this problem, the control moment gyroscope (CMG) is creatively used as an auxiliary stabilisation device for fully actuated biped robots and the CMG assistance strategy, which can be integrated into the biped's balance control framework, is proposed. This strategy includes model predictive control module, distribution module, and CMG precession controller. Under the command of it, CMGs can effectively assist the robot in resisting impact and returning to initial positions in time. The results of anti‐impact simulation on the walking and running biped robot prove that, with the help of CMGs, the robot's ability to resist disturbance and remain stable is significantly improved.The cover image is based on the Original Article Disturbance rejection for biped robots during walking and running using control moment gyroscopes by Haochen Xu et al., https://doi.org/10.1049/csy2.12070.

A learning-based model predictive control scheme and its application in biped locomotion

This paper proposes a learning-based model predictive control scheme. This scheme divides the predictive model into a known nominal model and an unknown model residual. Model residual is learned using Gaussian process regression. The learned stochastic model is solved quickly using differential dynamic programming, taking into account control input constraints. The simulation results show that compared with state of art optimal control methods, this scheme has good robustness to model residual, accelerates the solution of high-dimensional problems, and can strictly constrain the control inputs according to the actual situation. Based on this learning-based model predictive control scheme, this paper also proposes an online learning gait generator for the uncertainty problem in the locomotion control of biped robots. The zero moment point is strictly constrained during training to ensure safety. The simulation results show that the gait generator is robust to unknown load and unknown external force.

Practical finite-time adaptive sliding mode control for 5-link biped robot in the presence of uncertainty

ABSTRACT In this paper, novel practical finite-time robust adaptive controllers are investigated for a 5-link single support phase (SSP) lower limb biped robot subjected to unknown physical and dynamical parameters, disturbances, and unknown input saturation. The developed adaptive approaches are based on a terminal sliding surface and a finite-time stability analysis to design controller and adaptation laws. The adaptive mechanisms deal with estimating unknown terms, the upper bound of the unknown term vector, or estimating the unknown limits of input saturation. Consequently, the proposed approach is applicable when the physical and dynamical parameters of the robot and the saturation input parameters are not known. In addition, the Lyapunov stability approach warranties that the tracking errors are robust finite-time uniformly ultimately bounded and provides the practical finite-time stability of the closed-loop system. Finally, numerical simulations are provided to show the feasibility, effectiveness, and performance enhancement of the proposed methods over state-of-the-art methods.

Modeling and Control of a Wheeled Biped Robot.

It is difficult to realize the stable control of a wheeled biped robot (WBR), as it is an underactuated nonlinear system. To improve the balance and dynamic locomotion capabilities of a WBR, a decoupled control framework is proposed. First, the WBR is decoupled into a variable-length wheeled inverted pendulum and a five-link multi-rigid body system. Then, for the above two simplified models, a time-varying linear quadratic regulator and a model predictive controller are designed, respectively. In addition, in order to improve the accuracy of the feedback information of the robot, the Kalman filter is used to optimally estimate the system state. The control framework can enable the WBR to realize changing height, resisting external disturbances, velocity tracking and jumping. The results obtained by simulations and physical experiments verify the effectiveness of the framework.

Toward Reactive Walking: Control of Biped Robots Exploiting an Event-Based FSM

Reactivity to unforeseen disturbances is one of the most crucial characteristics for biped robots to walk robustly in the real world. Nevertheless, conventional walking methods generally have limited capability for generating rapid reactions to disturbances, because in these methods it is necessary to wait until the end of the preplanned time period to proceed to the next phase. In this study, to improve reactivity, we develop an event-based finite-state machine (E-FSM) for walking pattern generation. Reactivity is enhanced by determining the state transition conditions of the E-FSM only with time-independent events based on the present robot state. Moreover, in the E-FSM, the robot can walk robustly even when the center of mass and the swing foot motion are disturbed, by employing the capture point concept combined with a new swing foot position constraint. Finally, we propose to control the walking robot by incorporating the E-FSM with an inverse dynamics-based motion/force controller to achieve compliant behavior. This can provide safe responses to external disturbances. The developed method is verified by experiments on a 12-degrees-of-freedom torque-controlled biped robot while it locomotes under irregular external disturbances applied to the upper body or swing leg.

Dual Beetle Antennae Search system for optimal planning and robust control of 5-link biped robots

In this paper, we presented an optimization-based control framework for the trajectory planning and the stable control of multiple non-linear systems, i.e., 5-link Biped Robot, inverted pendulum, and acrobot using the BAS (Beetle Antennae Search) algorithm with PID (Proportional–Integral–Derivative) controller. Traditional approaches treat path planning and stable control of the system as a separate problem, or multiple controls are employed to achieve both goals. Therefore, these approaches are complex, computationally, and time-wise expensive. The addition of disturbance to the system makes the control further intricate. In this paper, we proposed an optimization-based approach for the path planning and stable control of the non-linear systems by employing a hybrid controller, which includes BAS and PID. We devised a switching mechanism, where BAS will first perform the trajectory optimization through feedforward control. In the second phase, a disturbance is introduced in the system, and BAS is used to optimize the K values of PID for the control stability. In the simulation, we applied this technique on three non-linear systems, i.e., 5-link Biped Robot, inverted pendulum, and acrobot, and promising results are obtained. In addition we performed a comparison of dual Beetle Antennae Search algorithm with known swarm algorithms, i.e. PSO (Particle Swarm Optimization) and GA (Genetic Algorithm). The results showed that BAS and PSO are comparable. For example, in inverted pendulum the objective function converges to 7.566×10−4 and 0.029 by BAS and PSO respectively. Likewise, in 5-link Biped Robot the objective function converges to 0.0250 and 0.0351 by BAS and PSO respectively. In addition, the number of function evaluations in case of BAS increases linearly, i.e., 3 times of the iterations. However, the function evaluations increase exponentially in case of PSO and GA.

Hybrid Metric K-Nearest Neighbor Algorithm and Applications

The K-Nearest Neighbor (KNN) algorithm is a classical machine learning algorithm. Most KNN algorithms are based on a single metric and do not further distinguish between repeated values in the range of K values, which can lead to a reduced classification effect and thus affect the accuracy of fault diagnosis. In this paper, a hybrid metric-based KNN algorithm is proposed to calculate a composite metric containing distance and direction information between test samples, which improves the discriminability of the samples. In the experiments, the hybrid metric KNN (HM-KNN) algorithm proposed in this paper is compared and validated with a variety of KNN algorithms based on a single distance metric on six data sets, and an HM-KNN application method is given for the forward gait stability control of a bipedal robot, where the abnormal motion is considered as a fault, and the distribution of zero moment points when the abnormal motion is generated is compared. The experimental results show that the algorithm has good data differentiation and generalization ability for different data sets, and it is feasible to apply it to the walking stability control of bipedal robots based on deep neural network control.

Fuzzy Logic-Based Controller for Bipedal Robot

In this paper, the problem of controlling a human-like bipedal robot while walking is studied. The control method commonly applied when controlling robots in general and bipedal robots in particular, was based on a dynamical model. This led to the need to accurately define the dynamical model of the robot. The activities of bipedal robots to replace humans, serve humans, or interact with humans are diverse and ever-changing. Accurate determination of the dynamical model of the robot is difficult because it is difficult to fully and accurately determine the dynamical quantities in the differential equations of motion of the robot. Additionally, another difficulty is that because the robot’s operation is always changing, the dynamical quantities also change. There have been a number of works applying fuzzy logic-based controllers and neural networks to control bipedal robots. These methods can overcome to some extent the uncertainties mentioned above. However, it is a challenge to build appropriate rule systems that ensure the control quality as well as the controller’s ability to perform easily and flexibly. In this paper, a method for building a fuzzy rule system suitable for bipedal robot control is proposed. The design of the motion trajectory for the robot according to the human gait and the analysis of dynamical factors affecting the equilibrium condition and the tracking trajectory were performed to provide informational data as well as parameters. Based on that, a fuzzy rule system and fuzzy controller was proposed and built, allowing a determination of the control force/moment without relying on the dynamical model of the robot. For evaluation, an exact controller based on the assumption of an accurate dynamical model, which was a two-feedback loop controller based on integrated inverse dynamics with proportional integral derivative, is also proposed. To confirm the validity of the proposed fuzzy rule system and fuzzy controller, computation and numerical simulation were performed for both types of controllers. Comparison of numerical simulation results showed that the fuzzy rule system and the fuzzy controller worked well. The proposed fuzzy rule system is simple and easy to apply.

Online Control for Biped Robot with Incremental Learning Mechanism

In this paper, we develop a new online walking controller for biped robots, which integrates a neural-network estimator and an incremental learning mechanism to improve the control performance in dynamic environment. With the aid of an iteration algorithm for updating, some newly incoming data can be used straightforwardly to update into the original well-trained model, in order to avoid a time-consuming retraining procedure. On the other hand, how to maintain the zero-moment-point stability and counteract the effect of yaw moment simultaneously is also a key technical problem to be addressed. To this end, an interval type-2 fuzzy weight identifier is newly developed, which assigns weight for each walking sample to deal with the imbalanced distribution problem of training data. The effectiveness of the proposed control scheme has been verified through a full-dynamics simulation and a practical robot experiment.

Finite-time disturbance reconstruction and robust fractional-order controller design for hybrid port-Hamiltonian dynamics of biped robots

In this paper, disturbance reconstruction and robust trajectory tracking control of biped robots with hybrid dynamics in the port-Hamiltonian form is investigated. A new type of Hamiltonian function is introduced, which ensures the finite-time stability of the closed-loop system. The proposed control system consists of two loops: an inner and an outer loop. A fractional proportional–integral–derivative filter is used to achieve finite-time convergence for position tracking errors at the outer loop. A fractional-order sliding mode controller acts as a centralized controller at the inner-loop, ensuring the finite-time stability of the velocity tracking error. In this loop, the undesired effects of unknown external disturbance and parameter uncertainties are compensated using estimators. Two disturbance estimators are envisioned. The former is designed using fractional calculus. The latter is an adaptive estimator, and it is constructed using the general dynamic of biped robots. Stability analysis shows that the closed-loop system is finite-time stable in both contact-less and impact phases. Simulation studies on three types of biped robots (i.e., two-link walker, RABBIT biped robot, and flat-feet biped robot) demonstrate the proposed controller’s tracking performance and disturbance rejection capability.

Sliding Balance Control of a Point-Foot Biped Robot Based on a Dual-Objective Convergent Equation

The point-foot biped robot is highly adaptable to and can move rapidly on complex, non-structural and non-continuous terrain, as demonstrated in many studies. However, few studies have investigated balance control methods for point-foot sliding on low-friction terrain. This article presents a control framework based on the dual-objective convergence method and whole-body control for the point-foot biped robot to stabilize its posture balance in sliding. In this control framework, a dual-objective convergence equation is used to construct the posture stability criterion and the corresponding equilibrium control task, which are simultaneously converged. Control tasks are then carried out through the whole-body control framework, which adopts an optimization method to calculate the viable joint torque under the physical constraints of dynamics, friction and contact forces. In addition, this article extends the proposed approach to balance control in standing recovery. Finally, the capabilities of the proposed controller are verified in simulations in which a 26.9-kg three-link point-foot biped robot (1) slides over a 10∘ trapezoidal terrain, (2) slides on terrain with a sinusoidal friction coefficient between 0.05 and 0.25 and (3) stands and recovers from a center-of-mass offset of 0.02 m.

A new tracking walking adaptive control of uncertain biped robot by state feedback and output feedback

In this paper, a walking algorithm of a biped robot with unknown dynamics has been addressed by proposing a new adaptive control based on the Modified Function Approximation Technique (MFAT) augmented with backstepping control, using state feedback and output feedback. The classical ZMP, the preview controller on the 3- Dimensional Linear Inverted Pendulum Model (3D-LIPM), is a stable walking algorithm but does not allow high accuracy, high speed, and robustness due to the ground environment, and also due to the dynamics uncertainties caused by the complex structure of these kinds of robots. To improve the accuracy of the walking tracking performance of the biped robot and its robustness against dynamics uncertainty and external perturbations, a combination of backstepping control and MFAT strategy have been intended. Recursive Lyapunov function is developed to ensure the closed-loop system stability. Experiment simulation and comparative study were carried out with biped robot to verify the performance and the robustness of the developed control strategy.