Page 104 - Handbook of Biomechatronics

100 Naser Mehrabi and John McPhee

However, during conscious voluntary movements, the CNS has to con-
tinuously update the motor commands to correct for errors (Todorov,
2004). For example, previous studies on pointing and reaching (Sarlegna
and Pratik, 2015) have shown that the CNS constantly updates the hand tra-
jectory based on sensory (feedback) information. This sensory information
can be received from vision, proprioception, audition, the vestibular system,
and internal models that can predict the motion (Desmurget and Grafton,
2000). A few studies have used feedback controllers to coordinate the move-
ments of a musculoskeletal model. The linear quadratic regulator (LQR) and
linear quadratic Gaussian (LQG) optimal feedback control methods have
been applied to a linear arm model to describe the hand trajectory (Harris
and Wolpert, 1998; Todorov and Jordan, 2002; Liu and Todorov, 2007).
Later, to control the nonlinear dynamics of the neuromuscular system, an
iterative LQG (iLQG) controller has been developed, in which the
nonlinear model is iteratively linearized (Todorov and Li, 2005). Recently,
a nonlinear model predictive control (NMPC) has been used to mimic the
CNS during reaching tasks (Mehrabi et al., 2017). This near-optimal con-
troller uses a nonlinear model to predict the reaching dynamics over a finite
horizon ahead of the current time, and uses the sensory information as feed-
back to correct the prediction errors. Depending on the application, the
CNS can be modeled as either a feedforward or feedback controller, or as
a combination of both. A control system with both feedforward and feed-
back components is preferred because it performs better and is more robust
to external disturbances.

1.3 Integrated Biomechatronic Models
Having a clear understanding of the dynamical system is crucial in designing
a controller, since not only does it strengthen our knowledge about the sys-
tem but also it reduces development time and cost. A predictive simulation
of an integrated model of the biomechatronic device and its user for the task
under study allows replicating the user-device interaction in silico
(Ghannadi et al., 2017; Mehrabi et al., 2015a). This platform can be used
to improve the device and controller design without going through the con-
ventional and cumbersome trial and error design methods. Now that we
introduced different approaches to develop and simulate biomechanical
models, we will describe the benefits and deficiencies of different model-
based control techniques that can be used to operate various biomechatronic
devices.