640 research outputs found
Lifelong Deep Learning-based Control Of Robot Manipulators
This study proposes a lifelong deep learning control scheme for robotic manipulators with bounded disturbances. This scheme involves the use of an online tunable deep neural network (DNN) to approximate the unknown nonlinear dynamics of the robot. The control scheme is developed by using a singular value decomposition-based direct tracking error-driven approach, which is utilized to derive the weight update laws for the DNN. To avoid catastrophic forgetting in multi-task scenarios and to ensure lifelong learning (LL), a novel online LL scheme based on elastic weight consolidation is included in the DNN weight-tuning laws. Our results demonstrate that the resulting closed-loop system is uniformly ultimately bounded while the forgetting is reduced. To demonstrate the effectiveness of our approach, we provide simulation results comparing it with the conventional single-layer NN approach and confirm its theoretical claims. The cumulative effect of the error and control input in the multitasking system shows a 43% improvement in performance by using the proposed LL-based DNN control over recent literature
Lifelong Learning-Based Multilayer Neural Network Control of Nonlinear Continuous-Time Strict-Feedback Systems
In This Paper, We Investigate Lifelong Learning (LL)-Based Tracking Control for Partially Uncertain Strict Feedback Nonlinear Systems with State Constraints, employing a Singular Value Decomposition (SVD) of the Multilayer Neural Networks (MNNs) Activation Function based Weight Tuning Scheme. the Novel SVD-Based Approach Extends the MNN Weight Tuning to (Formula Presented.) Layers. a Unique Online LL Method, based on Tracking Error, is Integrated into the MNN Weight Update Laws to Counteract Catastrophic Forgetting. to Adeptly Address Constraints for Safety Assurances, Taking into Account the Effects Caused by Disturbances, We Utilize a Time-Varying Barrier Lyapunov Function (TBLF) that Ensures a Uniformly Ultimately Bounded Closed-Loop System. the Effectiveness of the Proposed Safe LL MNN Approach is Demonstrated through a Leader-Follower Formation Scenario Involving Unknown Kinematics and Dynamics. Supporting Simulation Results of Mobile Robot Formation Control Are Provided, Confirming the Theoretical Findings
Real-Time Progressive Learning: Mutually Reinforcing Learning and Control with Neural-Network-Based Selective Memory
Memory, as the basis of learning, determines the storage, update and
forgetting of the knowledge and further determines the efficiency of learning.
Featured with a mechanism of memory, a radial basis function neural network
(RBFNN) based learning control scheme named real-time progressive learning
(RTPL) is proposed to learn the unknown dynamics of the system with guaranteed
stability and closed-loop performance. Instead of the stochastic gradient
descent (SGD) update law in adaptive neural control (ANC), RTPL adopts the
selective memory recursive least squares (SMRLS) algorithm to update the
weights of the RBFNN. Through SMRLS, the approximation capabilities of the
RBFNN are uniformly distributed over the feature space and thus the passive
knowledge forgetting phenomenon of SGD method is suppressed. Subsequently, RTPL
achieves the following merits over the classical ANC: 1) guaranteed learning
capability under low-level persistent excitation (PE), 2) improved learning
performance (learning speed, accuracy and generalization capability), and 3)
low gain requirement ensuring robustness of RTPL in practical applications.
Moreover, the RTPL based learning and control will gradually reinforce each
other during the task execution, making it appropriate for long-term learning
control tasks. As an example, RTPL is used to address the tracking control
problem of a class of nonlinear systems with RBFNN being an adaptive
feedforward controller. Corresponding theoretical analysis and simulation
studies demonstrate the effectiveness of RTPL.Comment: 16 pages, 15 figure
Lifelong Learning Control of Nonlinear Systems with Constraints using Multilayer Neural Networks with Application to Mobile Robot Tracking
This Paper Presents a Novel Lifelong Multilayer Neural Network (MNN) Tracking Approach for an Uncertain Nonlinear Continuous-Time Strict Feedback System that is Subject to Time-Varying State Constraints. the Proposed Method Uses a Time-Varying Barrier Function to Accommodate the Constraints Leading to the Development of an Efficient Control Scheme. the Unknown Dynamics Are Approximated using a MNN, with Weights Tuned using a Singular Value Decomposition (SVD)-Based Technique. an Online Lifelong Learning (LL) based Elastic Weight Consolidation (EWC) Scheme is Also Incorporated to Alleviate the Issue of Catastrophic Forgetting. the Stability of the overall Closed-Loop System is Analyzed using Lyapunov Analysis. the Effectiveness of the Proposed Method is Demonstrated by using a Quadratic Cost Function through a Numerical Example of Mobile Robot Control Which Demonstrates a 38% Total Cost Reduction When Compared to the Recent Literature and 6% Cost Reduction is Observed When the Proposed Method with LL is Compared to the Proposed Method Without LL
Scheduling Dimension Reduction of LPV Models -- A Deep Neural Network Approach
In this paper, the existing Scheduling Dimension Reduction (SDR) methods for
Linear Parameter-Varying (LPV) models are reviewed and a Deep Neural Network
(DNN) approach is developed that achieves higher model accuracy under
scheduling dimension reduction. The proposed DNN method and existing SDR
methods are compared on a two-link robotic manipulator, both in terms of model
accuracy and performance of controllers synthesized with the reduced models.
The methods compared include SDR for state-space models using Principal
Component Analysis (PCA), Kernel PCA (KPCA) and Autoencoders (AE). On the
robotic manipulator example, the DNN method achieves improved representation of
the matrix variations of the original LPV model in terms of the Frobenius norm
compared to the current methods. Moreover, when the resulting model is used to
accommodate synthesis, improved closed-loop performance is obtained compared to
the current methods.Comment: Accepted to American Control Conference (ACC) 2020, Denve
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