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公开(公告)号：US20190243320A1
公开(公告)日：2019-08-08
申请号：US15888143
申请日：2018-02-05
CPC分类号： G05B13/048 , B25J9/1607 , G06F16/23 , G06F17/11 , G06F17/16
摘要： A predictive controller for controlling a system subject to constraints including equality and inequality constraints on state and control variables of the system, includes an estimator to estimate a current state of the system using measurements of outputs of the system and a controller to solve, at each control step, a matrix equation of necessary optimality conditions to produce a control solution and to control the system using the control solution to change a state of the system. The matrix equation includes a block-structured matrix having a constraint Jacobian matrix of the equality constraints of the system. The controller determines the control solution iteratively using two levels of iterations including a first level of iterations that selects active inequality constraints for each point of time within a control horizon, updates the constraint Jacobian matrix, with a low-rank update for a change in the set of active inequality constraints, to include the equality constraints and the active inequality constraints, and updates a preconditioning matrix, with a low-rank factorization update, in response to the low-rank update of the constraint Jacobian matrix. The second level of iterations solves the matrix equation with the updated constraint Jacobian matrix using the updated preconditioning matrix to produce the control solution.
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公开(公告)号：US12061474B2
公开(公告)日：2024-08-13
申请号：US17407437
申请日：2021-08-20
发明人： Stefano Di Cairano , Ankush Chakrabarty , Rien Quirynen , Mohit Srinivasan , Nobuyuki Yoshikawa , Toshisada Mariyama
IPC分类号： G05D1/00 , G06F18/214 , G06N3/08
CPC分类号： G05D1/0055 , G05D1/0088 , G05D1/02 , G06F18/214 , G06N3/08
摘要： A controller for controlling a motion of at least one device subject to constraints on the motion, is disclosed. The controller comprises a processor and a memory, where the controller inputs parameters of the task including the state of the at least one device to a neural network trained to output an estimated motion trajectory for performing the task. Further, the controller extracts at least some of the integer values of a solution to a mixed-integer optimization problem for planning an execution of the task that results in the estimated motion trajectory. Further, the controller solves the mixed-integer optimization problem for the parameters of the task with corresponding integer values fixed to the extracted integer values to produce an optimized motion trajectory subject to the constraint and changes the state of the at least one device to track the optimized motion trajectory.
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公开(公告)号：US20230330853A1
公开(公告)日：2023-10-19
申请号：US17659246
申请日：2022-04-14
发明人： Chungwei Lin , Yebin Wang , Rien Quirynen , Devesh Jha , Bingnan Wang , William Vetterling , Siddarth Jain , Scott Bortoff
CPC分类号： B25J9/1664 , B25J9/1653 , B25J5/007 , B62D15/0285
摘要： The present disclosure provides a system and a method for controlling a motion of a robot from a starting point to a target point within a bounded space with a floorplan including one or multiple obstacles. The method includes solving for an electric potential in a bounded virtual space formed by scaling the floorplan of the bounded space with the one or multiple obstacles and applying charge to at least one bound of the bounded virtual space while treating the scaled obstacles as metallic surfaces with a constant potential value, wherein the electric potential provides multiple equipotential curves within the bounded virtual space. The method further includes selecting an equipotential curve with a potential value different from a potential value of an obstacle equipotential curve, determining a motion path based on the selected equipotential curve, and controlling the motion of the robot based on the determined motion path.
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公开(公告)号：US11698625B2
公开(公告)日：2023-07-11
申请号：US17117159
申请日：2020-12-10
发明人： Karl Berntorp , Rien Quirynen , Sean Vaskov
IPC分类号： G05B19/4155 , G05D1/00 , G06N20/00
CPC分类号： G05B19/4155 , G05D1/0088 , G05B2219/42061 , G05D2201/0213 , G06N20/00
摘要： A stochastic model predictive controller (SMPC) estimates a current state of the system and a probability distribution of uncertainty of a parameter of dynamics of the system based on measurements of outputs of the system, and updates a control model of the system including a function of dynamics of the system modeling the uncertainty of the parameter with first and second order moments of the estimated probability distribution of uncertainty of the parameter. The SMPC determines a control input to control the system by optimizing the updated control model of the system at the current state over a prediction horizon and controls the system based on the control input to change the state of the system.
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公开(公告)号：US11643982B1
公开(公告)日：2023-05-09
申请号：US17455472
申请日：2021-11-18
发明人： Rien Quirynen , Stefano Di Cairano
CPC分类号： F02D13/0203 , B60W50/0097 , F02D28/00 , B60W2050/0008
摘要： To control a hybrid dynamical system, a predictive feedback controller formulates a mixed-integer nonlinear programming (MINLP) problem including nonlinear functions of continuous optimization variables representing the continuous elements of the operation of the hybrid dynamical system and/or one or multiple linear functions of integer optimization variables representing the discrete elements of the operation of the hybrid dynamical system. The MINLP problem is formulated into a separable format ensuring that the discrete elements of the operation are present only in the linear functions of the MINLP problem. The MINLP problem is solved over multiple iterations using a partial convexification of a portion of a space of the solution including a current solution guess. The partial convexification produces a convex approximation of the nonlinear functions of the MINLP without approximating the linear functions of the MINLP to produce a partially convexified MINLP.
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公开(公告)号：US11327449B2
公开(公告)日：2022-05-10
申请号：US16886825
申请日：2020-05-29
发明人： Rien Quirynen , Xuhui Feng , Stefano Di Cairano
摘要： A predictive controller controls a system under uncertainty subject to constraints on state and control variables of the system. At each control step, the predictive controller solves an inequality constrained nonlinear dynamic optimization problem including probabilistic chance constraints representing the uncertainty to produce a control command, and controls an operation of the system using the control command. The predictive controller solves the dynamic optimization problem based on a two-level optimization that alternates, until a termination condition is met, propagation of covariance matrices of the probabilistic chance constraints within the prediction horizon for fixed values of the state and control variables with optimization of the state and control variables within the prediction horizon for fixed values of the covariance matrices.
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公开(公告)号：US11163273B2
公开(公告)日：2021-11-02
申请号：US16806701
申请日：2020-03-02
发明人： Rien Quirynen , Jonathan Frey , Stefano Di Cairano
摘要： A control system for controlling an operation of a machine subject to constraints including equality and inequality constraints on state and control variables of the system iteratively solves an optimal control structured optimization problem (OCP), such that each iteration outputs primal variables and dual variables with respect to the equality constraints and dual variables and slack variables with respect to the inequality constraints. For a current iteration, the system classifies each of the inequality constraints as an active, an inactive or an undecided constraint based on a ratio of a slack variable to a dual variable of the corresponding inequality constraint determined by a previous iteration, finds an approximate solution to the set of relaxed optimality conditions subject to the equality constraints and the active and undecided inequality constraints, and update the primal, dual, and slack variables for each of the equality and inequality constraint.
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公开(公告)号：US11106189B2
公开(公告)日：2021-08-31
申请号：US16293818
申请日：2019-03-06
发明人： Ankush Chakrabarty , Rien Quirynen , Claus Danielson , Weinan Gao
IPC分类号： G05B19/045 , G06F17/11 , G06F17/16 , G08G1/16
摘要： A machine subject to state and control input constraints is control, while the control policy is learned from data collected during an operation of the machine. To ensure satisfaction of the constraints, the state of machine is maintained within a constraint admissible invariant set (CAIS) satisfying the constraints and the machine is controlled with corresponding control policy mapping a state of the system within the CAIS to a control input satisfying the control input constraints. The machine is controlled using a constrained policy iteration, in which a constrained policy evaluation updates CAIS and value function and a constrained policy improvement updates control policy that improves the cost function of operation according to the updated CAIS and the corresponding updated value function.
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公开(公告)号：US10996639B2
公开(公告)日：2021-05-04
申请号：US16297870
申请日：2019-03-11
摘要： A controller for controlling a system with continuous and discrete elements of operation accepts measurements of a current state of the system, solves a mixed-integer model predictive control (MI-MPC) problem subject to state constraints on the state of the system to produce control inputs to the system, and submits the control inputs to the system thereby changing the state of the system. To solve the MI-MPC, the controller transforms the state constraints into state-invariant control constraints on the control inputs to the system, such that any combination of values for the control inputs, resulting in a sequence of values for the state variables that satisfy the state constraints, also satisfy the state-invariant control constraints, and solve the MI-MPC problem subject to the state constraints and the state-invariant control constraints.
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公开(公告)号：US10409233B2
公开(公告)日：2019-09-10
申请号：US15895393
申请日：2018-02-13
发明人： Rien Quirynen , Pedro Hespanhol
摘要： A control system for controlling an operation of a system with continuous-time nonlinear dynamics subject to constraints including equality and inequality constraints on state and control variables of the system, including an estimator to estimate a current state of the system using measurements of the operation of the system and a controller to iteratively solve, at each control time step, an approximation of a constrained nonlinear optimization problem to produce a control solution, wherein the approximation includes a linearization of the nonlinear dynamics of the system discretized by time intervals in the control horizon and represented using an approximation of the constraint Jacobian matrix for each time interval of the control horizon. The iterative solution procedure is based on a block-wise update formula for the approximation of the constraint Jacobian matrix and the intermediate condensing matrices using an evaluation of one or combination of the discretized dynamics of the system and at least one directional derivative of the discretized dynamics of the system. Each block in the constraint Jacobian matrix and in the intermediate condensing matrices represents one time interval in the prediction horizon and can be updated independently, based on a block-wise rank-one update formula without any iterative solution procedure and without any matrix-matrix multiplications or matrix factorizations.
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