Techniques for the Robust Control of Rigid Robots

Abdallah, Chaouki T.; Dawson, D.M.; Dorato, P.; Jamshidi, Mo

doi:10.1016/b978-0-12-012753-5.50015-6

Cited by 43 publications

(75 citation statements)

References 47 publications

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“…Much work (26)(27)(28) has thus been dedicated to automatically producing robust robot behaviors; however, in all cases more evaluations had to be performed during training: Robustness arises in robots that must maintain a given behavior in different task environments. Here, robust controllers were obtained using fewer evaluations, compared to behaviors obtained from robots that experience no morphological change.…”

Section: Resultsmentioning

confidence: 99%

Morphological change in machines accelerates the evolution of robust behavior

Bongard

2011

Proc. Natl. Acad. Sci. U.S.A.

152

115

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Most animals exhibit significant neurological and morphological change throughout their lifetime. No robots to date, however, grow new morphological structure while behaving. This is due to technological limitations but also because it is unclear that morphological change provides a benefit to the acquisition of robust behavior in machines. Here I show that in evolving populations of simulated robots, if robots grow from anguilliform into legged robots during their lifetime in the early stages of evolution, and the anguilliform body plan is gradually lost during later stages of evolution, gaits are evolved for the final, legged form of the robot more rapidly-and the evolved gaits are more robust-compared to evolving populations of legged robots that do not transition through the anguilliform body plan. This suggests that morphological change, as well as the evolution of development, are two important processes that improve the automatic generation of robust behaviors for machines. It also provides an experimental platform for investigating the relationship between the evolution of development and robust behavior in biological organisms.evolutionary computation | robotics | evolutionary robotics | biomechanics | locomotion U sing robots to study adaptive behavior is of interest as a basic intellectual pursuit, but it may also lead to machines that assist or replace humans in unstructured or outdoor environments. To date, however, limited success has been achieved in realizing machines that continually perform simple yet robust behaviors in unstructured environments. It is contended here that this is due to overemphasis on the proximate mechanisms (1) of adaptive behavior-copying specific morphological and neuromorphological detail from organisms of interest into robots (2) in the hopes of replicating their behavior-and too little emphasis on the ultimate mechanisms of behavior-replicating the ontogenetic processes and selection pressures that gave rise to the behavior initially.To demonstrate the value of incorporating the evolution of development (3) into robotics, here I show that the automated discovery of robot behaviors can be accelerated if their body plans progress from an infant to an adult form over their lifetime (Fig. 1B)-and this ontogenetic change itself changes over successive generations of robots ( Fig. 1 D and F) such that later robots exhibit only the adult form during their lifetime (Fig. 1H)-than if robots maintain the adult form throughout behavior optimization. Moreover, robots evolved with this evolution-of-development method were found to be more robust to environmental perturbation than robots evolved without it because the former robots' ancestors assumed a range of body plans and thus had to maintain the behavior over a wider range of sensor-motor relationships.

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Section: Resultsmentioning

confidence: 99%

Morphological change in machines accelerates the evolution of robust behavior

Bongard

2011

Proc. Natl. Acad. Sci. U.S.A.

152

115

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“…Additionally, the controller's matrices � � and � � require bounded elements to guarantee the Hurwitz property in matrix � � ; hence, it is always possible to obtain the following relation (see [9]):…”

Section: Resultsmentioning

confidence: 99%

“…This could be particularly relevant with a nominal linear time invariant system with a nonlinear perturbation such as the following: (9) In this case, the equilibrium point is exponentially stable if is a Hurwitz matrix and the perturbation satisfies the following conditions: (10) where and are the minimum and maximum eigenvalues of the matrices and respectively. These matrices are positive definite and satisfy the Lyapunov equation: (11) In this case, the Lyapunov function is defined as and it is clear that it satisfies the conditions (6)- (8).…”

Section: Mathematical Preliminariesmentioning

confidence: 99%

“…A compendium of papers related to robust control of robot manipulators can be found in [9]. Robust control techniques applied to robot manipulators have recently been discussed in various publications; for example, in [22] an output feedback sliding mode robust control method was proposed, using the Lyapunov method to establish semi-global stability conditions.…”

Section: Introductionmentioning

confidence: 99%

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New Method for Tuning Robust Controllers Applied to Robot Manipulators

Romero

Alcorta

Lara

et al. 2012

International Journal of Advanced Robotic Systems

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This paper presents a methodology to select the parameters of a nonlinear controller using Linear Matrix Inequalities (LMI). The controller is applied to a robotic manipulator to improve its robustness. This type of dynamic system enables the robust control law to be applied because it largely depends on the mathematical model of the system; however, in most cases it is impossible to be completely precise. The discrepancy between the dynamic behaviour of the robot and its mathematical model is taken into account by including a nonlinear term that represents the model´s uncertainty. The controller´s parameters are selected with two purposes: to guarantee the asymptotic stability of the closed-loop system while taking into account the uncertainty, and to increase its robustness margin. The results are validated with numerical simulations for a particular case study; these are then compared with previously published results to prove a better controller performance.

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“…Several papers and wide researches in optimizing performance of the robot manipulator show the importance of this issue. Different adaptive and robust procedures are proposed for robot control that all of them are so complicated in analysis and design [8][9][10][11] . Approaches that have been proposed for robot control are categorized in non-linear methods that are more difficult than linear methods in analysis and implementation [12][13][14] .…”

mentioning

confidence: 99%

Two-link Robot Manipulator using Fractional Order PID Controllers Optimized by Evolutionary Algorithms

Fani¹,

Shahraki²

2016

Biosci., Biotech. Res. Asia

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Fractional order (FO) controllers are highly considered with regard to higher performance and robustness of these controllers in FO systems. According to advantages of PID controllers such as suitable performance, low price and simplicity of design, they are widely used in industry. A FOPID controller is used for two-link robot control in this paper. Considering vast use of evolutionary algorithms and numerical optimization, coefficients of the FO controller are optimized using evolutionary algorithms in this paper. An individual FOPID controller is applied in order to control each link. Three evolutionary optimization algorithms including particle swarm optimization (PSO), genetic algorithm and estimation of distribution algorithm, are compared from optimal coefficients determination point of view. Experimental results indicate that FOPID controller is more applicable according to use of actual model for robot and suitable performance of the PSO algorithm. Key words: Evolutionary algorithms, fractional order controller, PID controller, two-link robotAlong with introduction of fractional order (FO) controllers and their higher performance and robustness on FO systems by Podlubny in 1994, FO systems and their control process are significantly considered 1 . Although PID controllers are introduced long time ago, they are widely used in industry because of their advantages such as low price, design simplicity and suitable performance 2, 3 . While three parameters of design including proportional (K p ), integral (K i ), and derivative (K d ) are available in PID controllers, two more parameters exist in FOPID controllers for adjustment. These parameters are integral fractional order and derivative fractional order 4 . In comparison with PID controllers, FOPID controllers have more flexible design that result in more precise adjustment of closed-loop system 5 . FOPID controllers are defined by FO differential equations. It is possible to tune frequency response of the control system by expanding integral and derivative terms of the PID controller to fractional order case. This characteristic result in a more robust design of control system, but it is not easily possible 6 .According to non-linearity, uncertainty, and confusion behaviors of robot arms, they are highly recommended for experimenting designs of control systems. Despite non-linear behavior of robot arm, it is demonstrable that a linear proportional derivative controller can stabilize the system using Lyapanov 7 . But, classic PD controller itself cannot control robot to reach suitable condition. Several papers and wide researches in optimizing performance of the robot manipulator show the importance of this issue. Different adaptive and robust procedures are proposed for robot control that all of them are so complicated in analysis and design [8][9][10][11] . Approaches that have been proposed for robot control are categorized in non-

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Techniques for the Robust Control of Rigid Robots

Cited by 43 publications

References 47 publications

Morphological change in machines accelerates the evolution of robust behavior

Morphological change in machines accelerates the evolution of robust behavior

New Method for Tuning Robust Controllers Applied to Robot Manipulators

Two-link Robot Manipulator using Fractional Order PID Controllers Optimized by Evolutionary Algorithms

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