adaptive learning systems have gained significant attention in recent years for their ability to cater to diverse learner needs and improve educational outcomes. This paper proposes a novel adaptive learning system th...
详细信息
Safety assurance is critical in the planning and control of robotic systems. For robots operating in the real world, the safety-critical design often needs to explicitly address uncertainties and the pre-computed guar...
详细信息
ISBN:
(纸本)9798350382662;9798350382655
Safety assurance is critical in the planning and control of robotic systems. For robots operating in the real world, the safety-critical design often needs to explicitly address uncertainties and the pre-computed guarantees often rely on the assumption of the particular distribution of the uncertainty. However, it is difficult to characterize the actual uncertainty distribution beforehand and thus the established safety guarantee may be violated due to possible distribution mismatch. In this paper, we propose a novel safe control framework that provides a high-probability safety guarantee for stochastic dynamical systems following unknown distributions of motion noise. Specifically, this framework adopts adaptive conformal prediction to dynamically quantify the prediction uncertainty from online observations and combines that with the probabilistic extension of the control barrier functions (CBFs) to characterize the uncertainty-aware control constraints. By integrating the constraints in the model predictive control scheme, it allows robots to adaptively capture the true prediction uncertainty online in a distribution-free setting and enjoys formally provable high-probability safety assurance. Simulation results on multi-robot systems with stochastic single-integrator dynamics and unicycle dynamics are provided to demonstrate the effectiveness of our framework.
Previously, we reported on a revamping of an existing design course to shift from a focus on sewage treatment plant design to a focus on the (re)design of local food systems. To introduce engineering students to quali...
详细信息
This paper considered implementing an adaptive multi-channel filter system on a Field-Programmable Gate Array (FPGA). The proposed system addresses the challenge of interference suppression in complex disturbance envi...
详细信息
This research proposes an innovative optimal trajectory tracking scheme for uncertain linear discrete-time (DT) systems, leveraging trajectory-dependent Q-learning. Unlike conventional optimal tracking control approac...
详细信息
ISBN:
(纸本)9798350382662;9798350382655
This research proposes an innovative optimal trajectory tracking scheme for uncertain linear discrete-time (DT) systems, leveraging trajectory-dependent Q-learning. Unlike conventional optimal tracking control approaches, the proposed method eliminates the need for a desired trajectory generator function, typically modeled as the dynamics of an autonomous system. Instead, we tackle the tracking problem by learning a Q-function that depends on a horizon of reference trajectory points in the future, which enables the computation of optimal feedback gains and time-varying feedforward control inputs without prior knowledge of system parameters or access to the complete reference trajectory. To enhance the effectiveness of the controller in multitask scenarios, we use the Efficient Lifelong Learning Algorithm (ELLA) to generate a shared knowledge base and use online adaptive control methods to directly learn parameters for each task, enabling information transfer between tasks. Simulation results using a power system demonstrate the efficacy of our approach.
Over-approximating the reachable sets of dynamical systems is a fundamental problem in safety verification and robust control synthesis. The representation of these sets is a key factor that affects the computational ...
详细信息
Over-approximating the reachable sets of dynamical systems is a fundamental problem in safety verification and robust control synthesis. The representation of these sets is a key factor that affects the computational complexity and the approximation error. In this paper, we develop a new approach for over-approximating the reachable sets of neural network dynamical systems using adaptive template polytopes. We use the singular value decomposition of linear layers along with the shape of the activation functions to adapt the geometry of the polytopes at each time step to the geometry of the true reachable sets. We then propose a branch-and-bound method to compute accurate over-approximations of the reachable sets by the inferred templates. We illustrate the utility of the proposed approach in the reachability analysis of linear systems driven by neural network controllers.
At present, adaptive cruise systems have become a research hotspot at home and abroad, and have made certain research progress. However, most of them are based on straight road conditions. There is still a need for in...
详细信息
Traditional retrieval systems are hardly adequate for Legal research, mainly because only returning the documents related to a given query is usually insufficient. Legal documents are extensive, and we posit that gene...
详细信息
ISBN:
(纸本)9798400704314
Traditional retrieval systems are hardly adequate for Legal research, mainly because only returning the documents related to a given query is usually insufficient. Legal documents are extensive, and we posit that generating questions about them and detecting the answers provided by these documents help the Legal research journey. This paper presents a pipeline that relates Legal Questions with documents answering them. We align features generated by Large Language Models with traditional clustering methods to find convergent and divergent answers to the same legal matter. We performed a case study with 50 legal documents on the Brazilian judiciary system. Our pipeline found convergent and divergent answers to 23 major legal questions regarding the case law for daily fines in Civil Procedural Law. The pipeline manual evaluation shows it managed to group diverse similar answers to the same question with an average precision of 0.85. It also managed to detect two divergent legal matters with an average F1 Score of 0.94.
In the wake of escalating tensions between Russia and Ukraine, Quadrotor Unmanned Aerial Vehicles (UAVs) are emerging as game-changers. These ensemble UAVs, with their variable fuel efficiencies and flight times, requ...
详细信息
ISBN:
(纸本)9798350304626
In the wake of escalating tensions between Russia and Ukraine, Quadrotor Unmanned Aerial Vehicles (UAVs) are emerging as game-changers. These ensemble UAVs, with their variable fuel efficiencies and flight times, require efficient routing from origin to destination. However, factors like speed, angle, accuracy, timing, and situational control introduce uncertainty in trajectory prediction, further complicated by the Internet of Things (IoT) platform integration. This study presents an innovative adaptive fuzzy sliding mode controller optimized for UAV routing in an IoT environment. Using swarm intelligence algorithms in combination, the proposed model leverages the Dragonfly-Firefly algorithm to facilitate deviation-free path scheduling and target tracking. Through a MATLAB environment simulation, this research expects to yield comprehensive evaluation results, thereby demonstrating the potential efficacy of this approach. The simulation's graphical display enables a visual understanding of real-time path scheduling based on routing and target trajectory. This groundbreaking research paves the way for more efficient and intelligent preparation for potential conflicts, capitalizing on the increasing significance of Quadrotor UAVs. The proposed model could revolutionize the way we approach UAV routing within IoT platforms, marking a significant advancement in warfare technology.
In this paper, dynamic surface control (DSC) with radial basis function neural networks (RBFNNs) is addressed for the novel nonlinear mixed multiagent systems with constraints. Each agent can be a nonstrict state or o...
详细信息
In this paper, dynamic surface control (DSC) with radial basis function neural networks (RBFNNs) is addressed for the novel nonlinear mixed multiagent systems with constraints. Each agent can be a nonstrict state or output feedback system. By using the nonlinear transformation rules (NTRs), the states or output constraints can be handled. By using the compensating signals, the filtering errors can be eliminated. The stability analysis demonstrates that all the signals are bounded.
暂无评论