46 results about "Discrete time system" patented technology

A family of novel multi-layer discrete-time neural net controllers is presented for the control of an multi-input multi-output (MIMO) dynamical system. No learning phase is needed. The structure of the neural net (NN) controller is derived using a filtered error/passivity approach. For guaranteed stability, the upper bound on the constant learning rate parameter for the delta rule employed in standard back propagation is shown to decrease with the number of hidden-layer neurons so that learning must slow down. This major drawback is shown to be easily overcome by using a projection algorithm in each layer. The notion of persistency of excitation for multilayer NN is defined and explored. New on-line improved tuning algorithms for discrete-time systems are derived, which are similar to e-modification for the case of continuous-time systems, that include a modification to the learning rate parameter plus a correction term. These algorithms guarantee tracking as well as bounded NN weights. An extension of these novel weight tuning updates to NN with an arbitrary number of hidden layers is discussed. The notions of discrete-time passive NN, dissipative NN, and robust NN are introduced.

The invention discloses a double-layer Kalman filter-based intermittent failure diagnosis and active fault-tolerant control method. The method includes the following steps that: a linear discrete timesystem model on which an intermittent failure occurs is established; double-layer Kalman filter-based intermittent failure diagnosis is performed; and fault-tolerance control for the intermittent failure is performed. The invention provides the above method for one type of linear discrete time systems on which intermittent failures occur. With the method adopted, the occurrence time and disappearance time of the intermittent failure can be accurately and effectively detected under the condition of noise disturbance, and the solving problem of the intermittent failure is converted into the optimization problem of minimum variance unbiased estimation under an intermittent failure gain constraint, so that an optimal estimation value can be obtained accurately; and a failure diagnosis processis combined with the design process of a fault-tolerant controller, so that the real-time performance and rapidity of fault-tolerant control can be ensured. The active fault-tolerant controller is simple in structure and is easy to implement, and can ensure the stable operation of the system even when the intermittent failure occurs.

A novel and useful method and apparatus for suppressing aliasing interferers in decimating and sub-sampling discrete time systems. The present invention is operative to reduce the requirements for or completely eliminate the need for the anti-aliasing filter by dynamically modifying the sub-sampling rate (or decimation ratio). Rather than maintain a constant sampling rate (or decimation ratio), the sampling rate (or decimation ratio) is randomized such that its average remains at the nominal value and the effective jitter is low enough for the low rate (or low decimation ratio) system to tolerate. This smears or spreads interfering signals across the spectrum resulting in a noise floor at a significantly reduced level much lower than that of the original interferer signal. The interfering signals are reduced to background noise wherein the level of the resulting noise floor is not nearly as strong as the original interfering signal. Aliasing interferers are effectively suppressed by spreading the energy of the interfering signals out over a wider spectrum.

A simple and robust discrete-time induction motor control technique employs a control algorithm that estimates load torque and rotor resistance from the measured rotor velocity of the motor to be controlled. The estimates of load torque and rotor resistance are employed to generate periodic estimates of parameters that are employed to control first and second harmonic signal generators. A switch controls which of the signal generators supplies a control signal to the motor at any given moment. The two signal generators work in turn such that while one produces the motor control signal, the other one is being readjusted to the new set of parameters by the parameter controller. The control method can be considered as a generalization and modification of Field Oriented Control. In a "non-adaptive" modification, it ensures global exponential stability of a closed-loop system, if at least local stabilization of the system by static control is possible, for given parameter estimates. In an adaptive version, it provides identification and global exponential stabilization of the system.

A plant controller controls a plant modeled in a discrete-time system. The controller uses a frequency-shaping response-designating control algorithm having a filtering function to cause a difference between output of the plant and a target value to converge. Thus, the plant is robustly controlled without causing undesired frequency components. When the plant is an engine, the engine is modeled using a ignition timing corrective quantity as input and a rotational speed as output. The controller performs the frequency-shaping response-designating control algorithm to determine the ignition timing corrective quantity. The frequency-shaping response-designating control may be frequency-shaping sliding-mode control. In one embodiment, the control input into the plant may includes a corrective term for stabilizing an internal variable of the filter, a corrective term for stabilizing the difference between the output of the plant and the target value and a corrective term that varies depending on a switching function.

A method of building discrete system simulations, comprising a sequential set of questions asked of the author of the system, the inclusion of said questions being dependent upon answers to preceding questions and thus context-sensitive, comprising steps of implementing said sequence in software as an interactive process and skipping any steps that are irrelevant in view of earlier input. The method further comprises steps of providing an engine for a simplified, directed construction in logical top-down order of a model for simulation of a discrete-time system providing an engine for the conversion of a model created by a model-building apparatus to human-readable textual output that uniquely specifies the model.

A method for approximation of optimal control for a nonlinear discrete time system in which the state variables are first obtained from a system model. Control sequences are then iteratively generated for the network to optimize control variables for the network and in which the value for each control variable is independent of the other control variables. Following optimization of the control variables, the control variables are then mapped onto a recurrent neural network utilizing conventional training methods.

A control apparatus capable of improving the control accuracy and stability when controlling a controlled object with a predetermined restraint condition between a plurality of model parameters, or a controlled object having a lag characteristic, using a control target model of a discrete-time system. The control apparatus has an ECU which arranges a control target model including two model parameters such that terms not multiplied by the model parameters and terms multiplied by the same are on different sides of the model, respectively. Assuming the different sides represent a combined signal value and an estimated combined signal value, respectively, the ECU calculates onboard identified values of the model parameters such that an identification error between the signal values is minimized, and calculates an air-fuel ratio correction coefficient using the identified values and a control algorithm derived from the control target model.

The invention provides a centralized state estimation and data fusion method for multi-rate observation data. By applying the lift technique, a multi-rate estimation system is modeled into a single-rate discrete time system with multiple random parameters; upon an obtained system model, a centralized fusion estimation algorithm, a sequential fusion estimation algorithm and a distributed fusion estimation algorithm are respectively proposed.

The invention relates to a KFSTRCF-based target tracking architecture, which comprises a discrete time Kalman estimator DKE and an STRCF, and the DKE comprises a discrete time system measurement subsystem and a discrete time system copy subsystem; the target tracking result output of the STRCF serves as measured value input of an observation model in discrete time system measurement, a DKE model is updated through a discrete time system copy subsystem, and a state estimation observation updating equation is obtained. According to the method, the Kalman filter and the STRCF are combined to realize visual tracking, so that the problem of instability caused by large-scale application change is solved; and moreover, a step length control method is also introduced to limit the maximum amplitudeof the output state of the proposed framework, so that the problem of target loss caused by sudden acceleration and steering is solved, the KFSTRCF-based target tracking architecture is superior to STRCF in most cases, and particularly, in sports events, the method shows better performance and stronger robustness than competitors.

The invention provides a participatory perception and motivation method based on information quality. The participatory sensing system is modeled as a discrete time system, and the sensing activities are divided into multiple rounds according to time, and the platform starts the incentive mechanism for the task according to the "round", and the rewards are distributed in advance according to the perceived contribution of the participants in each round After the data collection results are uploaded to the platform, update the contribution and evaluate the current round of information quality, and formulate the next round of remuneration discount or compensation strategy according to the current remuneration and information quality, and realize the dynamic pricing of perception tasks based on information quality to motivate Participants complete perception activities continuously.

A discretization processing method for transforming a transfer function in continuous time systems to a transfer function in discrete time systems is disclosed. To obtain the new transfer function, angular frequency ωa of the original transfer function in continuous time systems is transformed to angular frequency ωc using the bilinear z-transform of the inverse characteristic. In the discretion processing result, the equivalent characteristics of the original transfer function in continuous time systems can be obtained by performing the bilinear z-transform against the new transfer function in continuous time systems having been obtained by the angular frequency transformation of inverse characteristic.

A method and apparatus for estimating a state parameter in a nonlinear discrete time system are provided. The method for estimating a state parameter has the steps of (a) predicting a state parameter at a current time using an estimated state parameter at a previous time and a system dynamics; and (b) estimating an optimal state parameter at the current time from the state parameter predicted in the step (a) and a system output parameter measured at the current time, using a geometric data fusion method. Since the method and apparatus for estimating a state parameter have an excellent estimation performance particularly when nonlinearity is great or the error in an estimated initial value is big, the method and apparatus solve many problems, which cannot be solved by the conventional extended Kalman filter, and more improve the performance of estimating a state parameter, by analyzing the system characteristic and then appropriately utilizing constraints such as the operation range of the state parameter.

The invention discloses a design method for an optimal controller of a linear discrete time system with control time delay. The method converts the linear discrete time system with control time delayinto an equivalent linear discrete time system with no control time delay through a multi-step transformation of a memorized memory; then obtains a controller of the linear discrete time system with control time delay by designing a finite time optimal controller of the equivalent linear discrete time system without control time delay; and obtains the controller of the linear discrete time systemwith control time delay by designing an infinite time optimal controller of the equivalent linear discrete time system without control time delay. According to the design method for optimal controllerof linear discrete time system with control time delay, an optimal control for multi-step transformation of a linear discrete time system with control time delay is provided on the basis of memoryless transformation, , and the dimension of the system will not increase and the obtained optimal controller can stabilize a closed-loop system index when using such transformation.

There is provided a plant and internal combustion engine control device capable of improving controllability and control accuracy when controlling a plurality of control amounts while eliminating interference between control inputs and control amounts. A plant (90) has interference between TH_cmd, Liftin_cmd as control input and PB, Gcyl as control amounts. The control device (1) of the plant (90) uses a predetermined control algorithm which combines a predetermined response-specifying type control algorithm (expressions (2) to (8)) and a predetermined non-interference control algorithm expression (9) based on a plant model (expression (20)) in which the plant (90) is modeled as a discrete time system model. The two control inputs TH_cmd and Liftin_cmd are calculated as two non-interference inputs eliminating interference so that PB and Gcyl follow the target values PB_cmd and Gcyl_cmd.

The invention discloses a heterogeneous distributed cluster system formation control algorithm based on a discrete system, and the algorithm comprises the steps: constructing a communication topological relation of a multi-agent system which comprises a plurality of second-order agents and first-order agents; respectively constructing a kinetic model and a formation function of a second-order intelligent agent and a first-order intelligent agent; each agent obtains a state vector and a formation function of an adjacent agent; obtaining control instruction state quantities of each second-order intelligent agent and each first-order intelligent agent; and based on the dynamic model, the control instruction state quantity and the state vector of each agent at the k moment, obtaining a position vector of each agent at the k + 1 moment, and according to the position vector of each agent at the k + 1 moment, obtaining the state vector of each agent at the k + 1 moment and a new control instruction state quantity so as to maintain formation control. According to the method, the discrete time system is utilized, the communication traffic among the multi-agent systems is effectively reduced, the two types of agents are controlled, and the multi-agent system formation algorithm better conforms to the actual system.

The invention discloses an online learning control method of a nonlinear discrete time system. The method comprises a behavior strategy selection step, an optimal Q-function definition step, an evaluation network and execution network introduction step, an estimation error calculation step and a final optimal weight calculation step, wherein after the weights of an evaluation network and an execution network are converged, the output of the execution network is the approximate value of an optimal controller. According to the invention, repeated iteration between strategy evaluation and strategy improvement is not needed, and real-time online learning of the optimal controller can be realized; a deorbital strategy learning mechanism is adopted, so that the problem that a direct heuristic dynamic planning method is insufficient in state-strategy space exploration is effectively solved, and an execution network and an evaluation network can use activation functions in any form; and online learning of an optimal controller can be achieved, a system model is not needed, and only state data generated by the behavior strategy is needed.