Implementation of a Perceptual Controller for an Inverted Pendulum Robot

Johnson, Thomas; Zhou, Siteng; Cheah, Wei; Mansell, Warren; Young, Rupert; Watson, Simon

doi:10.1007/s10846-020-01158-4

Cited by 29 publications

(25 citation statements)

References 18 publications

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“…The hierarchical model was superior in fit to a non-hierarchical proximity control model when the target moved in a predictable (sinusoid) pattern, but similar in fit with a less predictable (pseudorandom) pattern. Most recently, we have confirmed that the PCT hierarchical model of the inverted pendulum within a robotic device shows superior balance in the presence of disturbances compared to two other popularly used (non-hierarchical) controllers for the same robot (63).…”

Section: Separate Computational Models Of Hierarchies Conflict and mentioning

confidence: 55%

“…If, in turn, the gain of A was increased, the two agents oscillated widely and vigorously, potentially illustrating the loss of control that can come from increasing one's efforts. The remaining studies of conflict have not interpreted their findings in the context of psychological distress, but either in terms of social group behavior (63,64), or to illustrate more universal principles (54).…”

Section: Separate Computational Models Of Hierarchies Conflict and mentioning

confidence: 99%

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Why Do We Need Computational Models of Psychological Change and Recovery, and How Should They Be Designed and Tested?

Mansell

Huddy

2020

Front. Psychiatry

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Traditional research methodologies typically assume that humans operate on the basis of an "open loop" stimulus-process-response rather than the "closed loop" control of internal state. They also average behavioral data across repeated measures rather than assess it continuously, and they draw inferences about the working of an individual from statistical group effects. As such, we propose that they are limited in their capacity to accurately identify and test for the mechanisms of change within psychological therapies. As a solution, we explain the advantages of using a closed loop functional architecture, based on an extended homeostatic model of the brain, to construct working computational models of individual clients that can be tested against real-world data. Specifically, we describe tests of a perceptual control theory (PCT) account of psychological change that combines the components of negative feedback control, hierarchies, conflict, reorganization, and awareness into a working model of psychological function, and dysfunction. In brief, psychopathology is proposed to be the loss of control experienced due to chronic, unresolved conflict between important personal goals. The mechanism of change across disorders and different psychological therapies is proposed to be the capacity for the therapist to help the client shift and sustain their awareness on the higher level goals that are driving goal conflict, for sufficiently long enough to permit a trial-and-error learning process, known as reorganization, to "stumble" upon a solution that regains control. We report on data from studies that have modeled these components both separately and in combination, and we describe the parallels with human data, such as the pattern of early gains and sudden gains within psychological therapy. We conclude with a description of our current research program that involves the following stages: (1) construct a model of the conflicting goals that are held by people with specific phobias; (2) optimize a model for each individual using their dynamic movement data from a virtual reality exposure task (VRET); (3) construct and optimize a learning parameter (reorganization) within each model using a subsequent VRET; (3) validate the model of each individual against a third VRET. The application of this methodology to robotics, attachment dynamics in childhood, and neuroimaging is discussed.

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Section: Separate Computational Models Of Hierarchies Conflict and mentioning

confidence: 55%

Section: Separate Computational Models Of Hierarchies Conflict and mentioning

confidence: 99%

Why Do We Need Computational Models of Psychological Change and Recovery, and How Should They Be Designed and Tested?

Mansell

Huddy

2020

Front. Psychiatry

Self Cite

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“…The simulation is shown in Figure 10, using K our and the LQR controller. For our controller, the parameters used were K our = [2, 4,6,10] The reactions of the link 2 angle obtained by, respectively, utilising our strategy and the LQR method are presented in Figure 11, from which, one can infer that our swing-up technique was quicker than that utilising the LQR control scheme. The explanation was that, in the LQR control technique, link 2 needed to follow a to-and-fro movement until it arrived at a little neighbourhood of the vertical position [1].…”

Section: Simulation Resultsmentioning

confidence: 99%

“…There is another idea that its components map onto the adverse feedback system within the existing models of behaviour for artificial control systems [5]. The movement control of a two-legged robot is based on the inverse pendulum theory [6]. Its good example is an RIP system for nonlinear underactuated mechanical systems to practice the control theory and with special emphasis on robotics for applications [7].…”

Section: Introductionmentioning

confidence: 99%

Control Theory Application for Swing Up and Stabilisation of Rotating Inverted Pendulum

et al. 2021

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This paper introduces a new scheme for sliding mode control using symmetry principles for a rotating inverted pendulum, with the possibility of extension of this control scheme to other dynamic systems. This was proven for swing up and stabilisation control problems via the new sliding mode control scheme using both simulations and experiments of rotary inverted pendulum (RIP) underactuated systems. According to the Lyapunov theory, a section of the pendulum was compensated with a scale error in the upright position, as the desired trajectory was followed by the pendulum arm section. As the RIP’s dynamic equations were nonlinearly complex and coupled, the complex internal dynamics made the task of controller design difficult. The system control for the pathway of the reference model of the rotational actuator with the application of the sliding mode technique for moving back and forth up the inverted pendulum’s structure, till the arm to reach the linear range round the vertical upright position, was created and tested in an existent device. The stabilisation scheme was switched on in the sliding mode as soon as the arm reached the linear range. A comparison of the stabilisation performance for the same rotating inverted pendulum as discussed by other authors revealed that the proposed controller was more flexible and reliable in terms of the swing up and stabilisation time.

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“…Inverted pendulum systems present high dynamic complexity and represent a challenge in the automatic control field due to their nonlinearity and unstable equilibrium point when the pendulum rod is at the vertical position (Ogata [1]). Several approaches and configurations of inverted pendulums are studied in the literature, such as the Furuta pendulum (Furuta et al [2]; Antonio-Cruz et al [3]; Zabihifar et al [4]), the cart pendulum (Blondin and Pardalos [5]; Roose, Yahya and Al-Rizzo [6]), the robot pendulum (Grasser et al [7]; Ye et al [8]; Johnson et al [9]), the single propeller pendulum (Job and Jose [10]; Habib et al [11]), among others. The aeropendulum system with two propellers has been also analyzed, which presents a dynamic behavior similar to the traditional helicopters and can be used as a didact tool for engineering studies (Saleem et al [12]).…”

Section: Introductionmentioning

confidence: 99%

Loop-Shaping ℋ<sub>∞</sub> Control of an Aeropendulum Model

Breganon

Alves

Almeida

et al. 2021

International Journal of Applied Mechanics and Engineering

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This work presents a mathematical model of an aeropendulum system with two sets of motors with propellers and the design and simulation of a loop-shaping ℋ∞ control for this system. In this plant, the objective is to control the angular position of the pendulum rod through the torque generated by the thrust of the motorized propellers at the end of the rod’s axis. The control design is obtained by first using feedback linearization and then designing the ℋ∞ controller using the resulting linear system. For the control strategy validation, simulations were conducted in the Matlab/Simulink® environment, and the weighting functions for the ℋ∞ controller were adjusted to obtain the desired performance and stability of the closed-loop system. The simulation results show the efficiency of the applied methodology.

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Implementation of a Perceptual Controller for an Inverted Pendulum Robot

Cited by 29 publications

References 18 publications

Why Do We Need Computational Models of Psychological Change and Recovery, and How Should They Be Designed and Tested?

Why Do We Need Computational Models of Psychological Change and Recovery, and How Should They Be Designed and Tested?

Control Theory Application for Swing Up and Stabilisation of Rotating Inverted Pendulum

Loop-Shaping ℋ<sub>∞</sub> Control of an Aeropendulum Model

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