Bio-Inspired Structure and Behavior of Self-Recovery Quadruped Robot with a Limited Number of Functional Legs

In this study, the authors focus on the structural design of and recovery methods for a damaged quadruped robot with a limited number of functional legs. Because the pre-designed controller cannot be executed when the robot is damaged, a control strategy to avoid task failures in such a scenario should be developed. Not only the control method but also the shape and structure of the robot itself are significant for the robot to be able to move again after damage. We present a caterpillar-inspired quadruped robot (CIQR) and a self-learning mudskipper inspired crawling (SLMIC) algorithm in this research. The CIQR is realized by imitating the prolegs of caterpillars and by using a numerical optimization technique. A reinforcement learning method called Q-learning is employed to improve the adaptability of locomotion based on the crawling behavior of mudskipper. The results show that the proposed robotic platform and recovery method can improve the moving ability of the damaged quadruped robot with a few active legs in both simulations and experiments. Moreover, we obtained satisfactory results showing that a damaged multi-legged robot with at least one leg could travel properly along the required direction. Furthermore, the presented algorithm can successfully be employed in a damaged quadruped robot with fewer than four legs.

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Efficient Structural Design Using a Numerical Optimization Technique

IABSE Congress, New York, New York 2019: The Evolving Metropolis ◽

10.2749/newyork.2019.0499 ◽

2019 ◽

Author(s):

Qian Wang ◽

Lucas Schmotzer ◽

Yongwook Kim

Keyword(s):

Structural Design ◽

Numerical Optimization ◽

Optimization Technique ◽

Optimization Method ◽

Optimization Techniques ◽

Convergence Criteria ◽

Efficient Design ◽

Concrete Building ◽

Gradient Based ◽

Structural Responses

<p>Structural designs of complex buildings and infrastructures have long been based on engineering experience and a trial-and-error approach. The structural performance is checked each time when a design is determined. An alternative strategy based on numerical optimization techniques can provide engineers an effective and efficient design approach. To achieve an optimal design, a finite element (FE) program is employed to calculate structural responses including forces and deformations. A gradient-based or gradient-free optimization method can be integrated with the FE program to guide the design iterations, until certain convergence criteria are met. Due to the iterative nature of the numerical optimization, a user programming is required to repeatedly access and modify input data and to collect output data of the FE program. In this study, an approximation method was developed so that the structural responses could be expressed as approximate functions, and that the accuracy of the functions could be adaptively improved. In the method, the FE program was not required to be directly looped in the optimization iterations. As a practical illustrative example, a 3D reinforced concrete building structure was optimized. The proposed method worked very well and optimal designs were found to reduce the torsional responses of the building.</p>

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Structural Design and Crawling Pattern Generator of a Planar Quadruped Robot for High-Payload Locomotion

Sensors ◽

10.3390/s20226543 ◽

2020 ◽

Vol 20 (22) ◽

pp. 6543

Author(s):

Ru Kang ◽

Fei Meng ◽

Xuechao Chen ◽

Zhangguo Yu ◽

Xuxiao Fan ◽

...

Keyword(s):

Structural Design ◽

Control Method ◽

Design Method ◽

Load Capacity ◽

Center Of Mass ◽

Integrated Design ◽

Pattern Generator ◽

Quadruped Robot ◽

Point Of View ◽

High Payload

Load capacity is an important index to reflect the practicability of legged robots. Existing research into quadruped robots has not analyzed their load performance in terms of their structural design and control method from a systematic point of view. This paper proposes a structural design method and crawling pattern generator for a planar quadruped robot that can realize high-payload locomotion. First, the functions required to evaluate the leg’s load capacity are established, and quantitative comparative analyses of the candidates are performed to select the leg structure with the best load capacity. We also propose a highly integrated design method for a driver module to improve the robot’s load capacity. Second, in order to realize stable load locomotion, a novel crawling pattern generator based on trunk swaying is proposed which can realize lateral center of mass (CoM) movement by adjusting the leg lengths on both sides to change the CoM projection in the trunk width direction. Finally, loaded crawling simulations and experiments performed with our self-developed quadruped robot show that stable crawling with load ratios exceeding 66% can be realized, thus verifying the effectiveness and superiority of the proposed method.

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Boltzmann Distributed Replicator Dynamics: Population Games in a Microgrid Context

Games ◽

10.3390/g12010008 ◽

2021 ◽

Vol 12 (1) ◽

pp. 8

Author(s):

Gustavo Chica-Pedraza ◽

Eduardo Mojica-Nava ◽

Ernesto Cadena-Muñoz

Keyword(s):

Control Method ◽

Optimization Problems ◽

Replicator Dynamics ◽

Boltzmann Distribution ◽

Full Information ◽

Multi Agent Systems ◽

Q Learning ◽

Population Games ◽

Distributed Approach ◽

Multi Agent

Multi-Agent Systems (MAS) have been used to solve several optimization problems in control systems. MAS allow understanding the interactions between agents and the complexity of the system, thus generating functional models that are closer to reality. However, these approaches assume that information between agents is always available, which means the employment of a full-information model. Some tendencies have been growing in importance to tackle scenarios where information constraints are relevant issues. In this sense, game theory approaches appear as a useful technique that use a strategy concept to analyze the interactions of the agents and achieve the maximization of agent outcomes. In this paper, we propose a distributed control method of learning that allows analyzing the effect of the exploration concept in MAS. The dynamics obtained use Q-learning from reinforcement learning as a way to include the concept of exploration into the classic exploration-less Replicator Dynamics equation. Then, the Boltzmann distribution is used to introduce the Boltzmann-Based Distributed Replicator Dynamics as a tool for controlling agents behaviors. This distributed approach can be used in several engineering applications, where communications constraints between agents are considered. The behavior of the proposed method is analyzed using a smart grid application for validation purposes. Results show that despite the lack of full information of the system, by controlling some parameters of the method, it has similar behavior to the traditional centralized approaches.

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An Optimal Tracking Control Method with Q-learning for Discrete-time Linear Switched System

2020 39th Chinese Control Conference (CCC) ◽

10.23919/ccc50068.2020.9188922 ◽

2020 ◽

Author(s):

Shangwei Zhao ◽

Jingcheng Wang ◽

Hongyuan Wang ◽

Haotian Xu

Keyword(s):

Discrete Time ◽

Tracking Control ◽

Control Method ◽

Switched System ◽

Q Learning ◽

Optimal Tracking ◽

Optimal Tracking Control ◽

Time Linear

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Optimization Technique Inspired Load Frequency Controller for Multi Area Power System with Renewable Energy Source

IOP Conference Series Earth and Environmental Science ◽

10.1088/1755-1315/850/1/012017 ◽

2021 ◽

Vol 850 (1) ◽

pp. 012017

Author(s):

J Shri Saranyaa ◽

A Peer Fathima ◽

Asutosh Mishra ◽

Rushali Ghosh ◽

Shalmali Das

Keyword(s):

Renewable Energy ◽

Power Systems ◽

Control Method ◽

Frequency Control ◽

Renewable Energy Sources ◽

Optimization Technique ◽

Optimization Method ◽

Load Frequency Control ◽

Load Frequency ◽

The Individual

Abstract Modern day scenario has an increasing power demand due to the growing development which indeed increases the load on the generation which might cause turbulence in the system and may bounce out of stability. The governor itself can’t handle such frequent load changes and adjust the generation amount to keep the frequency between the margins. This paper proposes an approach towards such predicament to incorporate an optimization method in order to ensure stability of the system despite the drastic changes in demand. Load frequency control is a control method for maintaining the frequency of the system during the change in demand. Use of controllers has proven to be effective in controlling the frequency deviations in the power systems and the response of the controller is further improved using optimization technique for better stability. The PID controller tuned by Particle Swarm Optimization is employed in multi-area system which reduces the time response by a considerable amount and the deviation settles much quicker despite the rapid load changes. The proposed controller is executed further for renewable energy sources connected to the individual areas and demonstration proves that the optimized controller is efficient enough in handling the frequency deviations when wind and solar with sunlight penetration is incorporated.

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A Numerical Optimization Technique Applied to the Design of Two-Dimensional Cavitating Hydrofoil Sections

Journal of Ship Research ◽

10.5957/jsr.1996.40.1.28 ◽

1996 ◽

Vol 40 (01) ◽

pp. 28-38

Author(s):

Shigenori Mishima ◽

Spyros A. Kinnas

Keyword(s):

Numerical Optimization ◽

Cavity Length ◽

Optimization Technique ◽

Cavitation Number ◽

Quadratic Functions ◽

Two Dimensional ◽

Systematic Design ◽

Hydrodynamic Analysis ◽

Total Drag ◽

Cavitating Hydrofoil

A numerical nonlinear optimization technique is applied to the systematic design of two-dimensional partially or supercavitating hydrofoil sections. The design objective is to minimize the hydrofoil drag for given lift and cavitation number. The hydrodynamic analysis of the cavitating hydrofoil is performed in nonlinear theory, via a low-order potential-based panel method. The effects of viscosity are taken into account via a uniform friction coefficient applied on the wetted foil surface. The total drag, lift, cavitation number, and other quantities involved in the imposed constraints, are expressed in terms of quadratic functions of the main parameters of the hydrofoil geometry, angle of attack, and the cavity length. The optimization is based on the method of multipliers by coupling the Lagrange multiplier terms and the penalty function terms. The robustness and convergence of the method are extensively investigated, and the results are compared with those from applying other design methods.

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Numerical Optimization Technique for Wheel Profile Considering the Normal Gap of the Wheel and Rail

Journal of Mechanical Engineering ◽

10.3901/jme.2009.12.205 ◽

2009 ◽

Vol 45 (12) ◽

pp. 205 ◽

Cited By ~ 2

Author(s):

Dabin CUI

Keyword(s):

Numerical Optimization ◽

Optimization Technique ◽

Wheel Profile

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Novel Intelligent and Sensorless Proportional Valve Control with Self-Learning Ability

Journal of Sensors ◽

10.1155/2016/8141720 ◽

2016 ◽

Vol 2016 ◽

pp. 1-6

Author(s):

Bayram Akdemir

Keyword(s):

Pulse Width ◽

Pulse Width Modulation ◽

Control Method ◽

Low Voltage ◽

Control Unit ◽

Control Signal ◽

Learning Ability ◽

Learning Abilities ◽

Valve Control ◽

Self Learning

Linear control is widely used for any fluid or air flows in many automobile, robotics, and hydraulics applications. According to signal level, valve can be controlled linearly. But, for many valves, hydraulics or air is not easy to control proportionally because of flows dynamics. As a conventional solution, electronic driver has up and down limits. After manually settling up and down limits, control unit has proportional blind behavior between two points. This study offers a novel valve control method merging pulse width and amplitude modulation in the same structure. Proposed method uses low voltage AC signal to understand the valve position and uses pulse width modulation for power transfer to coil. DC level leads to controlling the valve and AC signal gives feedback related to core moving. Any amplitude demodulator gives core position as voltage. Control unit makes reconstruction using start and end points to obtain linearization at zero control signal and maximum control signal matched to minimum demodulated amplitude level. Proposed method includes self-learning abilities to keep controlling in hard environmental conditions such as dust, temperature, and corrosion. Thus, self-learning helps to provide precision control for hard conditions.

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A self-learning propotional–integral–derivative control of grouting pressure using the back-propagation model

Proceedings of the Institution of Mechanical Engineers Part I Journal of Systems and Control Engineering ◽

10.1177/0959651818774485 ◽

2018 ◽

Vol 232 (8) ◽

pp. 1090-1099

Author(s):

Fengling Li ◽

Zhixiang Hou ◽

Juan Chen

Keyword(s):

Control Method ◽

Tracking Error ◽

Back Propagation ◽

Propagation Model ◽

Test Method ◽

Iteration Algorithm ◽

Grouting Pressure ◽

Geological Conditions ◽

Self Learning ◽

Derivative Control

For the security of grouting process of dam foundation, grouting pressure control is one of the most important problems. In order to avoid dangerous grouting pressure fluctuation and improve the control precision, a feedback propotional–integral–derivative control method was presented for the whole grouting system. Because the grouting pressure is affected by many factors such as grouting flow, grouts density, and geological conditions, the parameters of propotional–integral–derivative must be tuned. In this article, the adaptive tuning method is presented. The back-propagation artificial neural networks model was proposed to simulate the grouting control process, and sensitivity analysis algorithm based on orthogonal test method was adopted for the selection of input variables. To obtain the optimal propotional–integral–derivative parameters, an iteration algorithm was used in each sampling interval time and the discrete Lyapunov function of the tracking error. The simulation results showed that self-learning propotional–integral–derivative tuning was robust and effective for the realization of the automatic control device in the grouting process.

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