首页|Robust tube-based MPC with smooth computation for dexterous robot manipulation
Robust tube-based MPC with smooth computation for dexterous robot manipulation
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Dexterous robot manipulation has shone in complex industrial scenarios,where multiple manip-ulators,or fingers,cooperate to grasp and manipulate objects.When encountering multi-objective optimiza-tion with system constraints in such scenarios,model predictive control(MPC)has demonstrated exceptional performance in complex multi-robot manipulation tasks involving multi-objective optimization with system constraints.However,in such scenarios,the substantial computational load required to solve the optimal control problem(OCP)at each triggering instant can lead to significant delays between state sampling and control application,hindering real-time performance.To address these challenges,this paper introduces a novel robust tube-based smooth MPC approach for two fundamental manipulation tasks:reaching a given target and tracking a reference trajectory.By predicting the successor state as the initial condition for immi-nent OCP solving,we can solve the forthcoming OCP ahead of time,alleviating delay effects.Additionally,we establish an upper bound for linearizing the original nonlinear system,reducing OCP complexity and enhancing response speed.Grounded in tube-based MPC theory,the recursive feasibility and closed-loop stability amidst constraints and disturbances are ensured.Empirical validation is provided through two nu-merical simulations and two real-world dexterous robot manipulation tasks,which shows that the seamless control input by our methods can effectively enhance the solving efficiency and control performance when compared to conventional time-triggered MPC strategies.
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