8 results about "Terminal cost" patented technology

The invention provides a cooperative heave-rolling compensation method and system for a double-ship lifting arm system, and the method comprises the steps: building a double-ship lifting arm system dynamic model considering heave-rolling coupling motion, and enabling the model to comprise disturbance terms caused by ship motion; based on the kinetic model, generating a nominal trajectory under input saturation constraint and state constraint, and generating an expected trajectory through prediction control of a tightening constraint nonlinear model; for an actual disturbed system, designing a nonlinear robust model prediction controller, and driving an actual state to track the expected trajectory; and the stability of a closed-loop system is guaranteed through a terminal cost function and a terminal constraint set, and cooperative heaving-rolling compensation of the double-ship lifting arm is achieved. According to the method, through dynamic modeling, nominal track generation, robust control and stability guarantee, the double-ship lifting arm cooperative compensation heaving-rolling coupling motion is achieved, multi-degree-of-freedom oscillation can be effectively restrained under the five-level sea condition, and the operation stability and precision under the complex sea condition are improved.

The invention belongs to the technical field of robot control, and discloses a foot-arm multi-task control method and system based on time-varying terminal cost, and the method comprises the steps: generating a whole-body reference trajectory of a robot offline, and pre-calculating a time-varying terminal cost matrix sequence based on the reference trajectory; based on the time-varying terminal cost matrix sequence, constructing and solving a nonlinear model predictive control optimization problem to obtain an optimization sequence of a system state and control input; and the optimization sequence serves as a reference instruction and is input into a layered whole-body controller for multi-task priority optimization, and a joint control instruction is generated and output to a robot execution mechanism. According to the method, the problem of short vision of a traditional NMPC in a long-time task is effectively relieved, and the operation precision of the robot is remarkably improved.

This invention discloses an autonomous unloading method, device, and medium for a loader, belonging to the field of loader unloading technology. The method includes: determining a feasible solution space for unloading trajectory planning based on the spatial constraints of the unloading target area and the kinematic model of the loader's working device; constructing a stage cost function and a terminal cost function for the unloading process based on a dynamic programming algorithm and the feasible solution space, and determining the globally optimal unloading trajectory; determining a mathematical model of the electro-hydraulic servo system based on the electro-hydraulic drive architecture of the proportional servo valve-controlled hydraulic cylinder of the loader's working device; constructing displacement, velocity, and acceleration tracking errors based on the globally optimal unloading trajectory and hydraulic cylinder extension / retraction data from the state acquisition module, determining a trajectory tracking error model; determining a nonlinear integral sliding surface function based on the trajectory tracking error model; and determining a control instruction set based on the nonlinear integral sliding surface function through equivalent control terms and switching control terms with chatter suppression.

The invention provides a terminal strategy optimization heuristic MPC control method and system considering interference, and relates to the technical field of control, and the method comprises the steps: constructing a discrete time nonlinear kinetic model containing time-varying interference; a disturbance predictor is introduced, and disturbance model parameters are obtained through historical disturbance data fitting; using a disturbance predictor to predict disturbance of a future step; obtaining an optimal parameter of the parameterized terminal cost function through supervised learning, and obtaining an optimal terminal cost function; constructing a terminal model predictive control MPC framework, forming a corresponding terminal model predictive control MPC optimization problem, and embedding the optimal terminal cost function into the terminal MPC framework to replace the traditional terminal cost; and solving a constrained terminal MPC optimization problem, and outputting a control law to complete online control. The method can adapt to a time-varying interference scene, reduces interference and control errors caused by various uncertain factors, and improves the stability, accuracy and reliability of online control of the nonlinear unmanned system.

The present invention provides an iterative optimization and control method for the perching maneuver of an unmanned aerial vehicle. First, a longitudinal nonlinear dynamic model of the fixed-wing unmanned aerial vehicle's perching maneuver is established. Secondly, in order to obtain the initial feasible trajectory of the ILMPC strategy with the terminal state variables meeting the expected requirements, the constrained iterative linear quadratic regulator (CILQR) algorithm is used to optimize the perching maneuver trajectory, and the optimal trajectory is used as the initial feasible trajectory of the ILMPC strategy. Then, the perching maneuver process of the unmanned aerial vehicle is defined as an iterative task, a sampling safety set and a terminal cost function related to the perching maneuver trajectory are designed, and the perching maneuver ILMPC strategy is designed by using the sampling safety set and the terminal cost function. Finally, the numerical simulation results show that as the number of iterations increases, the iterative cost gradually decreases, and the perching maneuver trajectory gradually converges to the optimal trajectory, thus proving the effectiveness of the designed controller.

The present application discloses a control method, device, vehicle and storage medium for a vehicle. The method performs longitudinal control on the vehicle through at least one round of iterative process. Among them, the i-th round of iterative process includes: obtaining the actual state quantity; obtaining the terminal cost, where the terminal cost is determined based on a plurality of predicted state quantities determined in the (i-1)-th round of iterative process; the predicted state quantities include the predicted displacement and predicted speed of the vehicle; determining a planning control model based on the actual state quantity and the terminal cost; and performing longitudinal control on the vehicle based on the planning control model. Since the vehicle considers both the current actual state quantity of the vehicle and the predicted state quantities determined in the previous iterative process when determining the planning control model, the control quantity determined by the vehicle can be closer to the global optimal solution, effectively reducing the control error and overshoot of the control system and improving the longitudinal tracking accuracy of the vehicle.

The invention discloses a large model and terminal cost inspired unmanned equipment control method, device and equipment, and relates to the technical field of unmanned equipment motion control. According to the method, a large language model (namely a control strategy large model) is utilized, control track generation driven by task semantics is realized in an unmanned equipment control scene, terminal cost is subjected to micronized expression, disturbance adaptive optimization solution is carried out, a control correction amount is obtained, and the control correction amount and baseline control are superposed and output, so that the control precision is improved. According to the method, semantic planning and real-time constraint optimization collaborative closed-loop control is realized, so that the global decision intelligence and the environmental adaptation robustness are improved while the safety and the real-time performance are ensured.

The application belongs to the technical field of robot control, and discloses a foot-arm multi-task control method and system based on time-varying terminal cost, which comprises the following steps: generating a whole-body reference trajectory of a robot offline, and precomputing a time-varying terminal cost matrix sequence based on the reference trajectory; constructing and solving a nonlinear model predictive control optimization problem based on the time-varying terminal cost matrix sequence, so as to obtain an optimized sequence of system states and control inputs; inputting the optimized sequence as a reference instruction into a hierarchical whole-body controller to optimize multi-task priorities, generating joint control instructions, and outputting the joint control instructions to a robot actuator. The application effectively alleviates the "short-sighted" problem of traditional NMPC in long-term tasks, and significantly improves the operation accuracy of the robot.