D2.1 Requirements and Specification - CORBYS

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D2.1 Requirements and Specification non exhaustive overview is aimed at listing some of the different ways of implementing the assistance-asneeded concept on the control level. Variability in assistance is generally achieved by means of one or a combination of the following concepts: � task/function specific assistance � adaptivity of the assistance level � adaptivity of timing � adaptivity in space 16.3.2.1 Task/function specific assistance Task/function specific assistance implies that the assistance is tailored to the gait function(s), joint(s) or limb(s) that need(s) it and in the meantime assistance is reduced where it is useless or adverse. Hybrid forceposition control has been used to promote free motion during swing (force control) while using position control during stance (Bernhardt et al., 2005b, Lokomat). A similar approach has been conceived for training of the hemiparetic: position control (Bernhardt et al., 2005b, Lokomat) or impedance control (Vallery et al., 2009, LOPES) of the impaired side and force control of the unaffected side. Another example of task-specific assistance is the use of Virtual Model Control (Ekkelenkamp et al., 2007, LOPES). A virtual model simulates a specific action that needs to be performed on the patient by means of the exoskeleton (e.g., foot lift during swing, body weight support, ...). The virtual model control is implemented by means of impedance control (in joint space or task space) defining the interaction between the actual joint/limb motion and a moving or fixed target position. For several reasons different research groups have used force control to apply the zero assistance mode (patientin-charge mode) also to the entire device: to record unassisted gait for use as target trajectories in a position/impedance control scheme (Aoyagi et al., 2007, PAM, van Asseldonk et al., 2007, LOPES) and as a reference or baseline for the assisted mode (van Asseldonk et al., 2008, LOPES). In order to approximate the ideal zero assistance level the robot's dynamics are modelled and partly compensated. In that case, instead of controlling the actuator output towards zero force/torque, the interaction forces/torques between the robot and the human are minimised. 16.3.2.2 Adaptivity of the assistance level Adaptivity of the assistance level is often accomplished by using a measure of the patient's effort or a measure of how well the patient performs a task either directly as a feedback control signal or indirectly as a means to scale one or more control parameter(s). Patient-driven motion reinforcement (Bernhardt et al., 2005b, Lokomat) belongs to the first category, since a support torque is calculated as the product of a scale factor and the (modelled) active torque exerted by the patient. In Duschau-Wicke et al., 2008 (Lokomat) the support torque, proportional to the error between the actual and target trajectory, is recalculated at every gait cycle by an iterative learning controller. The second category groups several variations on the parameter scaling approach depending on the underlying control scheme. A forgetting factor is used for instance on the PD gains of a position control scheme (Emken et al., 2008, ARTHuR) and on an error-based learning controller (Emken et al., 2005, ARTHuR) reducing the assistance over time and ensuring the patient is sufficiently challenged. In Riener et al., 2005 (Lokomat) the impedance control parameters are scaled with the patient's effort, such that a larger contribution of the patient allows for larger trajectory deviations. 172
D2.1 Requirements and Specification 16.3.2.3 Adaptivity of timing Adaptivity of timing is always related to adaptivity in space and vice versa, since a gait pattern is defined both in space and time. Moreover, for position based control (e.g. position control, impedance control) the timing and the amplitude of the target trajectory affect the assistance level as well. Imposing a target trajectory without imposing a related timing has been done by using a set point control or path control. In Banala et al., 2007 (ALEX) PD set point control is used in which the target position is only switched to the next set point if the actual position is close enough to the current set point. In Aoyagi et al., 2007 (PAM) the target trajectory of the PD controller is determined on the basis of the actual state of the robot and the target trajectory of the robot defined in state-space. A moving window limits candidate target trajectory points to points close to the actual state. The difference in timing between the two points is fed to a synchronisation algorithm that selects the appropriate target trajectory point. The approach in Duschau-Wicke et al., 2008 (Lokomat) combines impedance control with the aforementioned set point generation method and the use of a moving window. In addition, an automatic treadmill speed adaptation algorithm changes the treadmill speed according to an admittance control scheme, using the measured interaction force between the human and an external fixed reference as an input. Instead of altering time dependent trajectories, some methods generate a position dependent force field or velocity field. The force field controller proposed in Banala et al., 2009 (ALEX) displays a force tangential to a required foot trajectory inside a “virtual tunnel”, a normal force towards the target trajectory outside that tunnel and a damping force to limit foot velocity. In Cai et al., 2006 two velocity field controllers are proposed. One has a virtual tunnel with tangential velocity inside and inward spiralling velocity outside, whereas the other has a small moving window with tangential velocity inside and a radial velocity field outside pointed towards the windows centre. 16.3.2.4 Adaptivity in space Adaptivity in space is either achieved by altering the target trajectory of a position-based controller or it is intrinsic to a non-position-based controller (egg. force controller). The aforementioned force field and velocity field controllers are examples of the latter: the actual position can vary almost freely within the boundaries of the virtual tunnel. The force controllers discussed in the paragraph about task/function specific assistance also allow for patient-induced gait pattern adaptation. The same goes for the impedance controllers used in Lokomat (Riener et al., 2005), LOPES (Veneman et al., 2007) and WalkTrainer (Stauffer et al., 2009) and for the position controllers implemented in devices with intrinsically compliant or back driveable actuators (ARTHuR, PAM, POGO (Reinkensmeyer et al., 2006), KNEXO (Beyl et al., 2009). The trajectory tracking controller implemented in KNEXO provides a tunable limitation of the assistive torque allowing for large deviations from the target trajectory. Different target trajectory adaptation algorithms for position-based control have been proposed in Jezernik et al., 2004. These algorithms calculate a target gait pattern adaptation that minimises the active patient torque. In Vallery et al., 2009 the target trajectory of the impedance controller for the impaired leg is based on the recorded motion of the unimpaired leg fed to a so called “complementary limb motion estimation” algorithm. This allows for a patient- induced adaptation of gait both in space and in timing. The use of surface EMG sensors in a so called proportional myoelectric control should be mentioned as well (Gordon and Ferris, 2007; Lee and Sankai, 2002). Providing an output torque proportional to processed EMG signals indirectly puts the human in control of the timing and the level of assistance. EMG-based torque control thus fits in the previous categories as well. 173
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CORBYS Cognitive Control Framework
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D2.1 Requirements and Specification
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