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Computed torque control is a control scheme used in motion control in robotics. It combines feedback linearization via a PID controller of the error with a dynamical model of the controlled robot.^[1][2]

Let the dynamics of the controlled robot be described by

${\displaystyle \mathbf {M} \left({\vec {\theta }}\right){\ddot {\vec {\theta }}}+\mathbf {C} \left({\vec {\theta }},{\dot {\vec {\theta }}}\right){\dot {\vec {\theta }}}+{\vec {\tau }}_{g}\left({\vec {\theta }}\right)={\vec {\tau }}}$ where ${\displaystyle {\vec {\theta }}\in \mathbb {R} ^{N}}$ is the state vector of joint variables that describe the system, ${\displaystyle \mathbf {M} \left({\vec {\theta }}\right)}$ is the inertia matrix, ${\displaystyle \mathbf {C} \left({\vec {\theta }},{\dot {\vec {\theta }}}\right){\dot {\vec {\theta }}}}$ is the vector Coriolis and centrifugal torques, ${\displaystyle {\vec {\tau }}_{g}\left({\vec {\theta }}\right)}$ are the torques caused by gravity and ${\displaystyle {\vec {\tau }}}$ is the vector of joint torque inputs.

Assume that we have an approximate model of the system made up of ${\displaystyle {\tilde {\mathbf {M} }}\left({\vec {\theta }}\right),{\tilde {\mathbf {C} }}\left({\vec {\theta }},{\dot {\vec {\theta }}}\right),{\tilde {\vec {\tau }}}_{g}\left({\vec {\theta }}\right)}$. This model does not need to be perfect, but it should justify the approximations ${\displaystyle \mathbf {M} \left({\vec {\theta }}\right)^{-1}{\tilde {\mathbf {M} }}\left({\vec {\theta }}\right)\approx \mathbf {1} }$ and ${\displaystyle \mathbf {M} ^{-1}\left(\mathbf {C} \left({\vec {\theta }},{\dot {\vec {\theta }}}\right){\dot {\vec {\theta }}}+{\vec {\tau }}_{g}\left({\vec {\theta }}\right)\right)\approx \mathbf {M} ^{-1}\left({\tilde {\mathbf {C} }}\left({\vec {\theta }},{\dot {\vec {\theta }}}\right){\dot {\vec {\theta }}}+{\tilde {\vec {\tau }}}_{g}\left({\vec {\theta }}\right)\right)}$.

Given a desired trajectory ${\displaystyle {\vec {\theta }}_{d}(t)}$ the error relative to the current state ${\displaystyle {\vec {\theta }}(t)}$ is then ${\displaystyle {\vec {\theta }}_{e}(t)={\vec {\theta }}_{d}(t)-{\vec {\theta }}(t)}$.

We can then set the input of the system to be

${\displaystyle {\vec {\tau }}(t)={\tilde {\mathbf {M} }}\left({\vec {\theta }}\right)\left({\ddot {\vec {\theta }}}_{d}(t)+K_{p}{\vec {\theta }}_{e}(t)+K_{i}\int _{0}^{t}{\ddot {\vec {\theta }}}_{e}(t')dt'+K_{d}{\dot {\vec {\theta }}}_{e}(t)\right)+{\tilde {\mathbf {C} }}\left({\vec {\theta }},{\dot {\vec {\theta }}}\right)+{\tilde {\vec {\tau }}}_{g}\left({\vec {\theta }}\right)}$

With this input the dynamics of the entire systems becomes

${\displaystyle {\begin{aligned}\mathbf {M} \left({\vec {\theta }}\right){\ddot {\vec {\theta }}}+\mathbf {C} \left({\vec {\theta }},{\dot {\vec {\theta }}}\right){\dot {\vec {\theta }}}+{\vec {\tau }}_{g}\left({\vec {\theta }}\right)=&{\tilde {\mathbf {M} }}\left({\vec {\theta }}\right)\left({\ddot {\vec {\theta }}}_{d}(t)+K_{p}{\vec {\theta }}_{e}(t)+K_{i}\int _{0}^{t}{\ddot {\vec {\theta }}}_{e}(t')dt'+K_{d}{\dot {\vec {\theta }}}_{e}(t)\right)+{\tilde {\mathbf {C} }}\left({\vec {\theta }},{\dot {\vec {\theta }}}\right)+{\tilde {\vec {\tau }}}_{g}\left({\vec {\theta }}\right)\\{\ddot {\vec {\theta }}}+\mathbf {M} \left({\vec {\theta }}\right)^{-1}\left(\mathbf {C} \left({\vec {\theta }},{\dot {\vec {\theta }}}\right){\dot {\vec {\theta }}}+{\vec {\tau }}_{g}\left({\vec {\theta }}\right)\right)=&\underbrace {\mathbf {M} \left({\vec {\theta }}\right)^{-1}{\tilde {\mathbf {M} }}\left({\vec {\theta }}\right)} _{\approx \mathbf {1} }\left({\ddot {\vec {\theta }}}_{d}(t)+K_{p}{\vec {\theta }}_{e}(t)+K_{i}\int _{0}^{t}{\ddot {\vec {\theta }}}_{e}(t')dt'+K_{d}{\dot {\vec {\theta }}}_{e}(t)\right)+\mathbf {M} \left({\vec {\theta }}\right)^{-1}\left({\tilde {\mathbf {C} }}\left({\vec {\theta }},{\dot {\vec {\theta }}}\right)+{\tilde {\vec {\tau }}}_{g}\left({\vec {\theta }}\right)\right)\\{\ddot {\vec {\theta }}}=&{\ddot {\vec {\theta }}}_{d}(t)+K_{p}{\vec {\theta }}_{e}(t)+K_{i}\int _{0}^{t}{\ddot {\vec {\theta }}}_{e}(t')dt'+K_{d}{\dot {\vec {\theta }}}_{e}(t)\\0=&{\ddot {\vec {\theta }}}_{e}+K_{p}{\vec {\theta }}_{e}(t)+K_{i}\int _{0}^{t}{\ddot {\vec {\theta }}}_{e}(t')dt'+K_{d}{\dot {\vec {\theta }}}_{e}(t)\end{aligned}}}$

and the normal methods for PID controller tuning can be applied. In this way the complicated nonlinear control problem has been reduced to a relatively simple linear control problem.