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Figure 5 – uploaded by Frank Cardullo

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This model is preferred because it allows flexibility when dealing with the parameters of the system, permitting us to account for variations among different subjects.

Figure 5 This model is preferred because it allows flexibility when dealing with the parameters of the system, permitting us to account for variations among different subjects.

Related Figures (12)

The early concept of a mathematical model of the human pilot was introduced by McRuer (McRuer and Jex. 1967) in the form of the crossover model, the main idea behind it is the combined open loop human-vehicle input-output behavior will act as “K/jo-like” (as an integration) with a processing delay in the crossover frequency region. This model includes beside the human the controlled element dynamics (aircraft, aircraft controls, dis play dynamics, and manipulator dynamics) and the human is assumed to be a part of the closed-loop system where his actions evolved to a stable relationship with the controlled element due to sufficient practice. McRuer developed t his model based on experiments, where the pilot performed a compensatory tracking task of random or pseudo-random input with the tracking error displayed on a CRT display in a fixed-base flight simulator (McRuer and Krendel, 1974). The parameters of the model (Crossover frequency (wc) and effective time delay (te)) are selected for and represent how the human pilot adjust his behavior to fulfill stable closed loop characteristics. particular situations
The early concept of a mathematical model of the human pilot was introduced by McRuer (McRuer and Jex. 1967) in the form of the crossover model, the main idea behind it is the combined open loop human-vehicle input-output behavior will act as “K/jo-like” (as an integration) with a processing delay in the crossover frequency region. This model includes beside the human the controlled element dynamics (aircraft, aircraft controls, dis play dynamics, and manipulator dynamics) and the human is assumed to be a part of the closed-loop system where his actions evolved to a stable relationship with the controlled element due to sufficient practice. McRuer developed t his model based on experiments, where the pilot performed a compensatory tracking task of random or pseudo-random input with the tracking error displayed on a CRT display in a fixed-base flight simulator (McRuer and Krendel, 1974). The parameters of the model (Crossover frequency (wc) and effective time delay (te)) are selected for and represent how the human pilot adjust his behavior to fulfill stable closed loop characteristics. particular situations
Figure 4 shows a structural model of the human-simulator system, initially developed by Ronald Hess (Hess, 1985) which has been commonly used by previous researchers to get a general idea about the performance of the used motion system. This model contains linear models blocks that represent the human visual system, neuromuscular system, processing delay, and aircraft dynamics. Blocks that represent the flight simulator motion system in the vestibula1 system feedback path were added later by George and Cardullo (Cardullo, George, & Latham, 2006). The selection of these systems parameters is constrained, so the overall input-output relationship complies with the McRuet crossover model. The disadvantages of this structure that it is limited to a single axis motion and assumes a fully trained pilot, which makes it unfeasible for determining training effectiveness. Therefore, a new architecture was developed by building upon the structure of the McRuer and Hess structural models.
Figure 4 shows a structural model of the human-simulator system, initially developed by Ronald Hess (Hess, 1985) which has been commonly used by previous researchers to get a general idea about the performance of the used motion system. This model contains linear models blocks that represent the human visual system, neuromuscular system, processing delay, and aircraft dynamics. Blocks that represent the flight simulator motion system in the vestibula1 system feedback path were added later by George and Cardullo (Cardullo, George, & Latham, 2006). The selection of these systems parameters is constrained, so the overall input-output relationship complies with the McRuet crossover model. The disadvantages of this structure that it is limited to a single axis motion and assumes a fully trained pilot, which makes it unfeasible for determining training effectiveness. Therefore, a new architecture was developed by building upon the structure of the McRuer and Hess structural models.
Magdaleno and MCRuer investigated the earliest model that discussed the neuromuscular system. They did a series of subjective and objective tests to obtain a transfer function that describes the combined dynamics of the muscle and the manipulator, and transfer functions of the spindle and joint sensors feedback. The values of the system parameters vary with task and manipulator type. (Stanco, Cardullo, Houck, Grube and Kelly, 2013) Hess in his structural model, used a second-order transfer function to describe the dynamics of the neuromuscular system as a spring-damper system with a muscle spindle and Golgi tendon feedback. He chose the model parameters so that the open loop pilot-vehicle dynamics comply with the crossover model around 2 rad/sec. (Stanco, Cardullo, Houck, Grube and Kelly, 2013)
Magdaleno and MCRuer investigated the earliest model that discussed the neuromuscular system. They did a series of subjective and objective tests to obtain a transfer function that describes the combined dynamics of the muscle and the manipulator, and transfer functions of the spindle and joint sensors feedback. The values of the system parameters vary with task and manipulator type. (Stanco, Cardullo, Houck, Grube and Kelly, 2013) Hess in his structural model, used a second-order transfer function to describe the dynamics of the neuromuscular system as a spring-damper system with a muscle spindle and Golgi tendon feedback. He chose the model parameters so that the open loop pilot-vehicle dynamics comply with the crossover model around 2 rad/sec. (Stanco, Cardullo, Houck, Grube and Kelly, 2013)
Schouten [Hosman, Cardullo, and Abbink 2010] developed a model that describes the dynamics of a one degree of freedom joint with antagonist flexor and extensor muscles assumed as one musculoskeletal system, and explain its interaction with external forces. Figure 7 shows the components of the model. (Stanco, Cardullo, Houck, Grube and Kelly, 2013) The literature presents a number of neuromuscular models based on the Hill-model approach which were considered for inclusion in the structural model (Winters and Stark, 1985) (Van Paasen, Van Der Vaart and Mulder 2004) (Van der Helm and Rozendaal 2000). The Tahara model (Tahara et al., 2005) (Tahara et al., 2009) for arm reaching movement in the horizontal plane can be used to represent the human arm dynamics controlling the aircraft motion inceptor, and thereby accounting for the nonlinear behavior of muscles combined with the dynamics of the skeletal system. It also describes the sensory-motor control problem based on the theory of stability that does not rely on inverse kinematics or optimization theory in a way that imitates the human response.
Schouten [Hosman, Cardullo, and Abbink 2010] developed a model that describes the dynamics of a one degree of freedom joint with antagonist flexor and extensor muscles assumed as one musculoskeletal system, and explain its interaction with external forces. Figure 7 shows the components of the model. (Stanco, Cardullo, Houck, Grube and Kelly, 2013) The literature presents a number of neuromuscular models based on the Hill-model approach which were considered for inclusion in the structural model (Winters and Stark, 1985) (Van Paasen, Van Der Vaart and Mulder 2004) (Van der Helm and Rozendaal 2000). The Tahara model (Tahara et al., 2005) (Tahara et al., 2009) for arm reaching movement in the horizontal plane can be used to represent the human arm dynamics controlling the aircraft motion inceptor, and thereby accounting for the nonlinear behavior of muscles combined with the dynamics of the skeletal system. It also describes the sensory-motor control problem based on the theory of stability that does not rely on inverse kinematics or optimization theory in a way that imitates the human response.
Figure 8. Detailed illustration of the model components (Tahara et al., 2005)
Figure 8. Detailed illustration of the model components (Tahara et al., 2005)
This model consists of two parts: the dynamic equations of the skeletal system (upper arm and forearm) where the shoulder and elbow joints are assumed to work as revolute joints, and the neuromuscular system presented by the six muscles shown in figure 8 (1): shoulder flexor, l2: shoulder extensor, 13: elbow flexor, 14: elbow extensor, Is double joint flexor, lg: double-joint extensor). The forces produced by the muscles are calculated using the Hill model and is
This model consists of two parts: the dynamic equations of the skeletal system (upper arm and forearm) where the shoulder and elbow joints are assumed to work as revolute joints, and the neuromuscular system presented by the six muscles shown in figure 8 (1): shoulder flexor, l2: shoulder extensor, 13: elbow flexor, 14: elbow extensor, Is double joint flexor, lg: double-joint extensor). The forces produced by the muscles are calculated using the Hill model and is
Figure 10 below shows the basic concept of a new approach using Kalman filter as a sensory fusion algorithm where the neuromuscular output along with the internal model of the aircraft is used to predict the aircraft state, and this value is corrected using the information coming from different sensory systems. While we can use Kalman filter as sensory fusion algorithm, it was designed as a state estimation method, so some modifications to its structure (sequential or parallel) are required to allow for that.
Figure 10 below shows the basic concept of a new approach using Kalman filter as a sensory fusion algorithm where the neuromuscular output along with the internal model of the aircraft is used to predict the aircraft state, and this value is corrected using the information coming from different sensory systems. While we can use Kalman filter as sensory fusion algorithm, it was designed as a state estimation method, so some modifications to its structure (sequential or parallel) are required to allow for that.
flight simulator to training in aircraft. These values represent how well, and how fast the pilot can train in the flight simulator, and modifying the parameters of the motion system allow us to minimize these values. The second evaluation method mimics the performance studies. The initial pilot model is trained in parallel within the aircraft and flight simulator environment until they both reach their training asymptote. The area between the two curves (Figure 14) represents the performance of the motion system of the flight simulator which increases as the are< value decrease.
flight simulator to training in aircraft. These values represent how well, and how fast the pilot can train in the flight simulator, and modifying the parameters of the motion system allow us to minimize these values. The second evaluation method mimics the performance studies. The initial pilot model is trained in parallel within the aircraft and flight simulator environment until they both reach their training asymptote. The area between the two curves (Figure 14) represents the performance of the motion system of the flight simulator which increases as the are< value decrease.
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