4 results about "Affine nonlinear system" patented technology

The invention discloses a unified design method for a suboptimal controller of an aircraft system under multiple constraints, and the method comprises the steps: carrying out the modeling of an aircraft system and various constraint conditions in a control process; secondly, converting the aircraft system model into a form of an affine nonlinear system, and representing various constraints as state inequality constraints, input amplitude inequality constraints or input time delay constraints; then, respectively converting the state inequality constraint and the input amplitude inequality constraint into state equality constraints; meanwhile, a system containing input time delay is converted into a system without input time delay; then, establishing an unconstrained augmentation system comprising a no-input time-delay system and state equality constraints; and finally, designing a sub-optimal controller based on reinforcement learning for the unconstrained augmentation system by adopting an execution-evaluation network framework. By means of the multi-constraint control method, various constraints can be processed at the same time, and the multi-constraint control method is high in universality and can be used for diversified actual application scenes.

The application relates to the technical field of automatic control, in particular to a high-order iterative learning control method for saturation disturbance of a variable-length nonlinear system, which comprises the following steps: for a discrete-time affine nonlinear system simultaneously affected by input saturation, variable iteration length, random initial state offset and external disturbance, Bernoulli random variables are introduced to describe the variable iteration length, an extended control input sequence uniform time scale is constructed, and a modified tracking error is defined; a high-order feedforward and instantaneous feedback compound control law with dynamic time-varying characteristics is constructed: the high-order feedforward synthesizes the saturation extended control input and the modified tracking error of multiple historical iterations, and a saturation residual memory mechanism is introduced for adaptive anti-integral saturation compensation; the feedback term utilizes the current iteration error to suppress local fluctuations; a cooperative convergence condition of a time-varying learning gain matrix is given, the mathematical expectation of a tracking error is proved to converge to a bounded region related to the disturbance amplitude and the initial state offset; and under ideal conditions, the tracking error asymptotically converges to zero.

The application discloses a robot contact force constraint control method based on a control barrier function, and an affine nonlinear system model containing contact force is constructed based on a kinematics model, a dynamics model and a contact force model of the robot, which has better dynamic performance compared with a single contact force model, the control barrier function is applied to robot contact force safety constraint control, compared with existing position safety constraint control methods, the application of the control barrier function in the robot field is widened, the control barrier function can be combined with various existing force-position control methods, has good contact force constraint effect, high flexibility, good real-time performance, strong robustness, simple application, and effectively improves the problem that traditional force-position control is difficult to constrain contact force.

The invention belongs to the technical field of energy management and intelligent decision, and provides a self-adaptive dynamic programming hydrogen energy supply chain optimization decision method and system, which abstracts a hydrogen energy supply chain into a discrete-time affine nonlinear system, introduces a utility function, constructs a performance index function considering asymmetric constraint, and improves the performance of the hydrogen energy supply chain. The hydrogen storage state value is maintained at an expected level, and a control problem is converted into a double-person zero-sum game problem; a self-adaptive dynamic programming method based on a neural network is adopted to approximately solve the double-person zero-sum game problem, according to data collected along an actual running track of a system, the weight of each network is optimized and updated by taking error function minimization of each network in the neural network as a target, and a decision result after solving is obtained. The method does not depend on an accurate model, can adapt to environment changes online, effectively processes asymmetric constraints and can suppress demand disturbance.