NUST Institutions Library Catalogue Search for 'kw,wrdl: (su-rl:"Machine Design")'

Engineering circuit analysis (EBOOK)

By Hayt, William Hart,. New York : McGraw-Hill, 2012 . xxi, 852 p. : 26 cm.. 0071317066 (international ed. : pbk.) | 0073529575 | 9780071317061 (international ed. : pbk.) | 9780073529578

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Understanding advanced statistical methods /

By Westfall, Peter H.,. . xxv, 543 pages : 26 cm.. 9781466512108 (hardback) | 1466512105 (hardback)

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Fundamentals of logic design /

By Roth, Charles H.. . xxiii, 791 pages : 24 cm +. 9781133628477 | 1133628478

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The art of memory forensics : detecting malware and threats in Windows, Linux, and Mac memory /

By Ligh, Michael Hale.. . xxiii, 886 pages : 24 cm. 9781118825099 (pbk.) | 1118825098 (pbk.) | 9781118825044 (ebk.) | 1118825047 (ebk.) | 9781118824993 (ebk.) | 1118824997 (ebk.)

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Artificial intelligence safety and security /

. xxix, 443 pages : 25 cm.. 9780815369820 (paperback : acidfree paper) | 9781138320840 (hardback : acidfree paper)

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Traffic Signal Control using Reinforcement Learning /

By Umer Jamil, Qazi . . 90p. ; 30cm..

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RL based Differential Drive Primitive Policy for Transfer Learning /

By Shahid, Mahrukh . . 52p. , To ensure the steady navigation for robot stable controls are the basic unit and control values selection is highly environment dependent. Adding Generalization to system is the key to reusability of control parameters to ensure adaptability in robots to perform with sophistication, in the environments about which they have no prior knowledge, for this Reinforcement Leaning (RL) based control systems are promising. However, tuning appropriate parameters to train RL algorithm is a challenge. Therefore, we designed a continuous reward function to minimizing the sparsity and stabilizes the policy convergence, to attain control generalization for differential drive robot. We Implemented Twin Delayed Deep Deterministic Policy Gradient-TD3 on Open-AI Gym Race Car. System was trained to achieve smart primitive control policy, moving forward in the direction of goal by maintaining an appropriate distance from walls to avoid collisions. Resulting policy was tested on unseen environments and observed precisely performing results. Upon comparative analysis of TD3 with DDPG, TD3 policy outperformed the DDPG policy in both training and testing phase, proving TD3 to be resource efficient and stable. 30cm.

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Control of Flywheel Inverted Pendulum Using Reinforcement Learning /

By Ahmad, Shakeel . . 67p. , Balancing an inverted pendulum is a classic control problem that traditionally requires precise system modeling for effective controller design. Reinforcement Learning (RL) offers a model-free alternative but requires extensive training, which is impractical and risky when performed directly on physical hardware. Existing methods typically rely on simulation environments built on accurate models, which are often difficult to obtain. In this work, we use RL to balance flywheel inverted pendulum by constructing an approximate model of the system through parameter estimation. Despite its inaccuracies, the model proved sufficient for training RL agents in simulation. We developed a simulation environment based on the estimated model and trained agents using Deep Q-Network (DQN), Proximal Policy Optimization (PPO), and Discrete Soft Actor-Critic (SAC) algorithms. The trained policies were deployed on real hardware without any additional fine-tuning. All agents achieved successful swing-up and stabilization, with SAC achieving the fastest swing-up time (1.65 s) and lowest steady-state error (0.0220 rad), demonstrating that RL can tolerate model imperfections and still perform effectively on real systems. 30cm.

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Addressing the Problem of Sloshing in a Liquid Carrying Mobile Robot through Artificial Intelligence /

By Khan, Khubaib Haider . . 90p. , Liquid sloshing presents a critical challenge for mobile robots tasked with transporting partially filled containers, as it destabilizes motion, reduces accuracy, and increases the risk of spillage. Traditional passive and active control methods—such as baffles, PID, and LQR—are limited in adaptability and computational efficiency when faced with nonlinear, time-varying fluid–structure interactions. This thesis addresses these challenges by formulating slosh suppression as a reinforcement learning problem, leveraging the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm integrated with a surrogate ball-in-container analogy in the Webots simulator. The surrogate transforms complex liquid dynamics into a tractable cart-pole inspired system, enabling efficient training of robust control policies without reliance on high-fidelity but computationally expensive CFD models. A custom simulation–learning pipeline was developed, coupling Webots with a PyTorchbased TD3 agent via JSON-based communication. State and action spaces were defined using the ball’s displacement ratio and robot velocity, while a multi-component reward function balanced slosh minimization, forward progress, and smooth energy-efficient motion. Training results demonstrated that the TD3 agent learned to achieve stable, sloshfree navigation over a 10 m trajectory, outperforming traditional controllers and earlier RL variants in stability and adaptability. Results shows that the Model Learns well after 600 Episodes attaining optimal velocity of 1.25 – 0.5 m/s keeping ball within 2 mm limit, maximizing the reward to around 1180 at 1700 Episodes, eventually reducing sloshing by 65 % during Training phase. While, during Test phase robot is tested for 0 to 4 m/s velocity with control and no control scenarios, showing the results RL algorithm suppress the ball displacement (liquid slosh) by approximately 45 %. The study validates reinforcement learning as a viable paradigm for real-time liquid slosh suppression in robotics, offering superior robustness and scalability. Contributions include an AI-driven control framework for slosh suppression, integrationxvii of surrogate modeling with DRL for real-time feasibility, and a reward design framework encoding domain-specific stability objectives. For Further Future working it is recommended to include extending to real liquid models and hardware validation, adopting symmetric action spaces and advanced RL methods, and integrating slosh control with autonomous navigation in dynamic environments. 30cm.

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