[go: up one dir, main page]

Lennon et al., 2002 - Google Patents

Intelligent control for brake systems

Lennon et al., 2002

View PDF
Document ID
5772445801936011718
Author
Lennon W
Passino K
Publication year
Publication venue
IEEE Transactions on control systems technology

External Links

Snippet

There exist several problems in the control of brake systems including the development of control logic for antilock braking systems (ABS) and" base-braking." Here, we study the base- braking control problem where we seek to develop a controller that can ensure that the …
Continue reading at www.academia.edu (PDF) (other versions)

Classifications

    • GPHYSICS
    • G05CONTROLLING; REGULATING
    • G05BCONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B13/00Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
    • G05B13/02Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
    • G05B13/04Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators
    • GPHYSICS
    • G05CONTROLLING; REGULATING
    • G05BCONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B13/00Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
    • G05B13/02Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
    • G05B13/0265Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric the criterion being a learning criterion
    • G05B13/027Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric the criterion being a learning criterion using neural networks only
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00Computer systems based on biological models
    • G06N3/02Computer systems based on biological models using neural network models
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N7/00Computer systems based on specific mathematical models
    • G06N7/02Computer systems based on specific mathematical models using fuzzy logic
    • GPHYSICS
    • G05CONTROLLING; REGULATING
    • G05BCONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B2219/00Program-control systems
    • G05B2219/30Nc systems

Similar Documents

Publication Publication Date Title
Lennon et al. Intelligent control for brake systems
Li et al. Robust model predictive shielding for safe reinforcement learning with stochastic dynamics
US20220187793A1 (en) Stochastic Model-Predictive Control of Uncertain System
Rubies-Royo et al. A classification-based approach for approximate reachability
CN114872730B (en) Vehicle driving trajectory prediction method, device, vehicle and storage medium
WO1995004958A1 (en) Extended horizon adaptive block predictive controller with an efficient prediction system
Kapoor et al. Model-based reinforcement learning from signal temporal logic specifications
JP7529145B2 (en) Learning device, learning method, and learning program
Yousfi Allagui et al. Artificial Fuzzy‐PID Gain Scheduling Algorithm Design for Motion Control in Differential Drive Mobile Robotic Platforms
Chen et al. RHONN-modeling-based predictive safety assessment and torque vectoring for holistic stabilization of electrified vehicles
Ait Abbas A new adaptive deep neural network controller based on sparse auto‐encoder for the antilock bracking system systems subject to high constraints
Hsu et al. Fuzzy-identification-based adaptive controller design via backstepping approach
Lennon et al. Genetic adaptive identification and control
Dombrovskii et al. A linear quadratic control for discrete systems with random parameters and multiplicative noise and its application to investment portfolio optimization
Ober‐Blöbaum et al. Explicit multiobjective model predictive control for nonlinear systems with symmetries
Chen et al. Fuzzy predictive control of uncertain chaotic systems using time series
CN118885967B (en) A vehicle trajectory fusion prediction method based on deep learning and kinematic model
Taguchi et al. Method for solving nonlinear goal programming with interval coefficients using genetic algorithm
Nolan Computational aspects of stable distributions
Mori et al. A simplified fuzzy inference method with tabu search for short-term load forecasting in power systems
Jou Supervised learning in fuzzy systems: algorithms and computational capabilities
Cordón et al. Evolutionary design of TSK fuzzy rule-based systems using (/spl mu/,/spl lambda/)-evolution strategies
Zarei et al. Optimal control of linear fuzzy time-variant controlled systems
Chopra et al. A Neurofuzzy Learning and its Application to Control system
Leffler et al. Efficient Exploration With Latent Structure.