A novel methodology to explore the periodic gait of a biped walker under uncertainty using a machine learning algorithm

Robotica ◽  
2021 ◽  
pp. 1-16
Author(s):  
Namjung Kim ◽  
Bongwon Jeong ◽  
Kiwon Park
Keyword(s):  
Limit Cycle ◽  
Systematic Approach ◽  
Learning Algorithm ◽  
Initial Conditions ◽  
Angular Displacement ◽  
External Disturbance ◽  
Rehabilitation Robotics ◽  
Stable Region ◽  
Gait Rehabilitation ◽  
External Disturbances

Abstract In this paper, we present a systematic approach to improve the understanding of stability and robustness of stability against the external disturbances of a passive biped walker. First, a multi-objective, multi-modal particle swarm optimization (MOMM-PSO) algorithm was employed to suggest the appropriate initial conditions for a given biped walker model to be stable. The MOMM-PSO with ring topology and special crowding distance (SCD) used in this study can find multiple local minima under multiple objective functions by limiting each agent’s search area properly without determining a large number of parameters. Second, the robustness of stability under external disturbances was studied, considering an impact in the angular displacement sampled from the probabilistic distribution. The proposed systematic approach based on MOMM-PSO can find multiple initial conditions that lead the biped walker in the periodic gait, which could not be found by heuristic approaches in previous literature. In addition, the results from the proposed study showed that the robustness of stability might change depending on the location on a limit cycle where immediate angular displacement perturbation occurs. The observations of this study imply that the symmetry of the stable region about the limit cycle will break depending on the accelerating direction of inertia. We believe that the systematic approach developed in this study significantly increased the efficiency of finding the appropriate initial conditions of a given biped walker and the understanding of robustness of stability under the unexpected external disturbance. Furthermore, a novel methodology proposed for biped walkers in the present study may expand our understanding of human locomotion, which in turn may suggest clinical strategies for gait rehabilitation and help develop gait rehabilitation robotics.

scholarly journals Analysis on Multistable Motion Characteristics of Supercavitating Vehicle

2017 ◽  
Vol 2017 ◽  
pp. 1-11 ◽  
Author(s):  
Tianhong Xiong ◽  
Yipin Lv ◽  
Wenjun Yi
Keyword(s):  
Limit Cycle ◽  
Initial Conditions ◽  
External Disturbance ◽  
Domain Of Attraction ◽  
Stable Equilibrium Point ◽  
Initial Condition ◽  
Supercavitating Vehicle ◽  
Vehicle Motion ◽  
Motion Characteristics ◽  
Engineering Practices

Due to complex underwater environment, when the initial condition of launching is subjected to low external disturbance, the motion trace of a supercavitating vehicle might display many different motion states during underwater navigation. With the aim of addressing this problem, based on the dynamic map, in the present work the multistable phenomena of attractor coexistence of the supercavitating vehicle system under various initial conditions were analyzed and the initial condition effects on the multistable motion characteristics were investigated through the domains of attraction, time, and frequency. The results demonstrated that, unlike the ordinary dynamic systems, a supercavitating vehicle demonstrates multistable phenomena, such as the coexistence of the stable equilibrium point and the limit cycle and the coexistence of the limit cycle and the chaotic attractor, along with the coexistence of diversified limit cycles; under fixed system parameters, as the initial condition of launching varied, the vehicle displayed various motion states; in engineering practices, the initial condition of launching could be adjusted according to the domain of attraction, in order for the vehicle motion stability to be enhanced.

scholarly journals Optimal Fractional-Order Active Disturbance Rejection Controller Design for PMSM Speed Servo System

Entropy ◽  
2021 ◽  
Vol 23 (3) ◽  
pp. 262
Author(s):  
Pengchong Chen ◽  
Ying Luo ◽  
Yibing Peng ◽  
Yangquan Chen
Keyword(s):  
Fractional Order ◽  
Disturbance Rejection ◽  
Pid Controller ◽  
External Disturbance ◽  
Servo System ◽  
Stable Region ◽  
Speed Tracking ◽  
Active Disturbance Rejection ◽  
Load Disturbance ◽  
Tuning Strategy

In this paper, a fractional-order active disturbance rejection controller (FOADRC), combining a fractional-order proportional derivative (FOPD) controller and an extended state observer (ESO), is proposed for a permanent magnet synchronous motor (PMSM) speed servo system. The global stable region in the parameter (Kp, Kd, μ)-space corresponding to the observer bandwidth ωo can be obtained by D-decomposition method. To achieve a satisfied tracking and anti-load disturbance performance, an optimal ADRC tuning strategy is proposed. This tuning strategy is applicable to both FOADRC and integer-order active disturbance rejection controller (IOADRC). The tuning method not only meets user-specified frequency-domain indicators but also achieves a time-domain performance index. Simulation and experimental results demonstrate that the proposed FOADRC achieves better speed tracking, and more robustness to external disturbance performances than traditional IOADRC and typical Proportional-Integral-Derivative (PID) controller. For example, the JITAE for speed tracking of the designed FOADRC are less than 52.59% and 55.36% of the JITAE of IOADRC and PID controller, respectively. Besides, the JITAE for anti-load disturbance of the designed FOADRC are less than 17.11% and 52.50% of the JITAE of IOADRC and PID controller, respectively.

scholarly journals Control of an IPMC Soft Actuator Using Adaptive Full-Order Recursive Terminal Sliding Mode

Actuators ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 33
Author(s):  
Romina Zarrabi Ekbatani ◽  
Ke Shao ◽  
Jasim Khawwaf ◽  
Hai Wang ◽  
Jinchuan Zheng ◽  
...  
Keyword(s):  
Sliding Mode ◽  
External Disturbance ◽  
Tracking Error ◽  
Working Environment ◽  
Parametric Uncertainties ◽  
Metal Composite ◽  
External Disturbances ◽  
Terminal Sliding Mode ◽  
Positioning Accuracy ◽  
Soft Actuator

The ionic polymer metal composite (IPMC) actuator is a kind of soft actuator that can work for underwater applications. However, IPMC actuator control suffers from high nonlinearity due to the existence of inherent creep and hysteresis phenomena. Furthermore, for underwater applications, they are highly exposed to parametric uncertainties and external disturbances due to the inherent characteristics and working environment. Those factors significantly affect the positioning accuracy and reliability of IPMC actuators. Hence, feedback control techniques are vital in the control of IPMC actuators for suppressing the system uncertainty and external disturbance. In this paper, for the first time an adaptive full-order recursive terminal sliding-mode (AFORTSM) controller is proposed for the IPMC actuator to enhance the positioning accuracy and robustness against parametric uncertainties and external disturbances. The proposed controller incorporates an adaptive algorithm with terminal sliding mode method to release the need for any prerequisite bound of the disturbance. In addition, stability analysis proves that it can guarantee the tracking error to converge to zero in finite time in the presence of uncertainty and disturbance. Experiments are carried out on the IPMC actuator to verify the practical effectiveness of the AFORTSM controller in comparison with a conventional nonsingular terminal sliding mode (NTSM) controller in terms of smaller tracking error and faster disturbance rejection.

scholarly journals Aircraft Maintenance Check Scheduling Using Reinforcement Learning

Aerospace ◽  
2021 ◽  
Vol 8 (4) ◽  
pp. 113
Author(s):  
Pedro Andrade ◽  
Catarina Silva ◽  
Bernardete Ribeiro ◽  
Bruno F. Santos
Keyword(s):  
Reinforcement Learning ◽  
Time Horizon ◽  
Learning Algorithm ◽  
Initial Conditions ◽  
Q Learning ◽  
Scheduling Policy ◽  
Real Scenario ◽  
Maintenance Plan ◽  
Small Disturbances

This paper presents a Reinforcement Learning (RL) approach to optimize the long-term scheduling of maintenance for an aircraft fleet. The problem considers fleet status, maintenance capacity, and other maintenance constraints to schedule hangar checks for a specified time horizon. The checks are scheduled within an interval, and the goal is to, schedule them as close as possible to their due date. In doing so, the number of checks is reduced, and the fleet availability increases. A Deep Q-learning algorithm is used to optimize the scheduling policy. The model is validated in a real scenario using maintenance data from 45 aircraft. The maintenance plan that is generated with our approach is compared with a previous study, which presented a Dynamic Programming (DP) based approach and airline estimations for the same period. The results show a reduction in the number of checks scheduled, which indicates the potential of RL in solving this problem. The adaptability of RL is also tested by introducing small disturbances in the initial conditions. After training the model with these simulated scenarios, the results show the robustness of the RL approach and its ability to generate efficient maintenance plans in only a few seconds.

The Road to Chaos by Time-Asymmetric Hebbian Learning in Recurrent Neural Networks

2007 ◽  
Vol 19 (1) ◽  
pp. 80-110 ◽  
Author(s):  
Colin Molter ◽  
Utku Salihoglu ◽  
Hugues Bersini
Keyword(s):  
Supervised Learning ◽  
Hebbian Learning ◽  
Learning Algorithm ◽  
Initial Conditions ◽  
A Priori ◽  
Computational Cost ◽  
Chaotic Regime ◽  
Dynamical Regime ◽  
New Form ◽  
The Impact

This letter aims at studying the impact of iterative Hebbian learning algorithms on the recurrent neural network's underlying dynamics. First, an iterative supervised learning algorithm is discussed. An essential improvement of this algorithm consists of indexing the attractor information items by means of external stimuli rather than by using only initial conditions, as Hopfield originally proposed. Modifying the stimuli mainly results in a change of the entire internal dynamics, leading to an enlargement of the set of attractors and potential memory bags. The impact of the learning on the network's dynamics is the following: the more information to be stored as limit cycle attractors of the neural network, the more chaos prevails as the background dynamical regime of the network. In fact, the background chaos spreads widely and adopts a very unstructured shape similar to white noise. Next, we introduce a new form of supervised learning that is more plausible from a biological point of view: the network has to learn to react to an external stimulus by cycling through a sequence that is no longer specified a priori. Based on its spontaneous dynamics, the network decides “on its own” the dynamical patterns to be associated with the stimuli. Compared with classical supervised learning, huge enhancements in storing capacity and computational cost have been observed. Moreover, this new form of supervised learning, by being more “respectful” of the network intrinsic dynamics, maintains much more structure in the obtained chaos. It is still possible to observe the traces of the learned attractors in the chaotic regime. This complex but still very informative regime is referred to as “frustrated chaos.”

Studies on Bypass Transition of a Boundary Layer Subjected to Localized Periodic External Disturbances

2004 ◽  
Author(s):  
K. Funazaki ◽  
Y. Wakita ◽  
T. Otsuki
Keyword(s):  
Boundary Layer ◽  
Flat Plate ◽  
External Disturbance ◽  
Transition Process ◽  
Bypass Transition ◽  
Hot Wire ◽  
Turbulent Spot ◽  
External Disturbances ◽  
Small Sphere ◽  
Probe Sensor

This study aims at clarification of wake-induced bypass transition process of a boundary layer on a flat plate with no pressure gradient. Special attention is paid to inception as well as growth of a turbulent spot created by the incoming wake as an external disturbance. To meet this goal a unique wake generator is invented to create an isolated turbulent spot. A multi-probe sensor with seven single-hot-wire probes is used to measure wake-affected boundary layer. The wake generator consists of a disk, pillars and a very thin wire with a small sphere on it. The sphere on the wire generates periodic wakes behind it when it passes across the main flow in front of the test flat plate. These sphere wakes impinge the flat plate in a spatially and timewisely localized manner so that the wakes periodically leave narrow affected zones inside the boundary layer. The observations confirm that an isolated turbulence spot emerges from each of those wake-affected zones. It is also found that the turbulent spot observed in this study bears a close resemblance to the conventional turbulent spot that takes a shape of arrowhead pointing downstream.

Diagonal recurrent neural network observer-based adaptive control for unknown nonlinear systems

2020 ◽  
Vol 42 (15) ◽  
pp. 2833-2856
Author(s):  
Ahmed Elkenawy ◽  
Ahmad M El-Nagar ◽  
Mohammad El-Bardini ◽  
Nabila M El-Rabaie
Keyword(s):  
Neural Network ◽  
Optimal Control ◽  
Adaptive Control ◽  
Nonlinear Systems ◽  
Recurrent Neural Network ◽  
Learning Algorithm ◽  
Lyapunov Stability Theory ◽  
Adaptive Dynamic Programming ◽  
External Disturbances ◽  
System States

This paper proposes an observer-based adaptive control for unknown nonlinear systems using an adaptive dynamic programming (ADP) algorithm. First, a diagonal recurrent neural network (DRNN) observer is proposed to estimate the unknown dynamics of the nonlinear system states. The proposed neural network offers a simpler structure with deeper memory and guarantees the faster convergence. Second, a neural controller is constructed via ADP method using the observed states to get the optimal control. The optimal control law is determined based on the new structure of the critic network, which is performed using the DRNN. The learning algorithm for the proposed DRNN observer-based adaptive control is developed based on the Lyapunov stability theory. Simulation results and hardware-in-the-loop results indicate the robustness of the proposed ADP to respond the system uncertainties and external disturbances compared with other existing schemes.

Analytical Investigation of Hopf Bifurcations Occurring in a 1DOF Sliding-Friction Oscillator With Application to Disc-Brake Vibrations

2005 ◽  
Author(s):  
Hartmut Hetzler ◽  
Wolfgang Seemann ◽  
Daniel Schwarzer
Keyword(s):  
Fixed Point ◽  
Steady State ◽  
Limit Cycle ◽  
Relative Velocity ◽  
Sliding Friction ◽  
Analytical Investigation ◽  
Stable Region ◽  
Stable Steady State ◽  
Disc Brake ◽  
Stick Slip

This article deals with analytical investigations on stability and bifurcations due to declining dry friction characteristics in the sliding domain of a simple disc-brake model, which is commonly referred to as “mass-on-a-belt”-oscillator. Sliding friction is described in the sense of Coulomb as proportional to the normal force, but with a friction coefficient μS which depends on the relative velocity. For many common friction models this latter dependence on the relative velocity can be described by exponential functions. For such a characteristic the stability and bifurcation behavior is discussed. It is shown, that the system can undergo a subcritical Hopf-bifurcation from an unstable steady-state fixed point to an unstable limit cycle, which separates the basins of the stable steady-state fixed point and the self sustained stick-slip limit cycle. Therefore, only a local examination of the eigenvalues at the steady-state, as is the classical ansatz when investigating conditions for the onset of friction-induced vibrations, may not give the whole picture, since the stable region around the steady state fixed point may be rather small. The analytical results are verified by numerical simulations. Parameter values are chosen for a model which corresponds to a conventional disc-brake.

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