Signal Compensation Based Adaptive Cascade Control for Regrinding Processes

Wang, Lanhao; Chai, Tianyou

doi:10.1109/tie.2019.2947804

Cited by 6 publications

(6 citation statements)

References 20 publications

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“…• Step target velocity (using actual test platform) Figs. [23][24][25][26][27][28][29][30][31][32] show the actual test-platform-based evaluation results when a sinusoidal target velocity was applied.…”

Section: B Actual Test-platform-based Evaluationmentioning

confidence: 99%

“…As shown in Figs. 23 and 24, the proposed control algorithm (in the adaptive case) allowed the DC motor to track the step target velocity with a relatively small control error. However, the tracking result in the fixed case showed a relatively derivative control inputs.…”

Section: B Actual Test-platform-based Evaluationmentioning

confidence: 99%

“…The main feature of the proposed control scheme is that it can eliminate the need for process modeling, as well as prerequisite knowledge of system dynamics and control parameter tuning. Wang et al [23] proposed a linear model and an uncertain nonlinear dynamic system that described the unknown time-varying dynamics of nonlinearity and couplings among controlled variables. The authors developed a feed-forward and one-step optimal control algorithms to obtain two signal compensators to eliminate the negative impact of frequent unexpected variations in the uncertainties.…”

Section: Introductionmentioning

confidence: 99%

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Data-Driven Adaptive Steady-State-Integral-Derivative Controller Using Recursive Least Squares With Performance Conditions

Lee¹,

2023

IEEE Access

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This paper presents a data-driven adaptive steady state-integral-derivative (SS-ID) control algorithm that uses gradient descent and recursive least squares (RLS) with a forgetting factor. A simplified first-order differential equation of the control system was designed and its parameters were estimated in real-time using the RLS algorithm. The steady-state control input for target-state tracking was derived based on the estimated parameters and steady-state performance conditions. The gradient of the integrated control error to the gain was estimated based on the least-squares method, using the saved past error and gain data in a finite sliding window to determine the control input. The integral gain was adapted based on the gradient descent method, using the estimated gradient, integrated error, and adaptation rate. Simplified control error dynamics were designed, and their parameter was estimated using the RLS algorithm. The derivative control gain can be adapted in real time using the estimated parameters from the simplified control error dynamics and time constant-based performance conditions. The proposed controller was designed in the MATLAB/Simulink environment. A performance evaluation was conducted under various scenarios using a DC motor simulation model and an actual test platform equipped with an optical encoder.

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Section: B Actual Test-platform-based Evaluationmentioning

confidence: 99%

Section: B Actual Test-platform-based Evaluationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Data-Driven Adaptive Steady-State-Integral-Derivative Controller Using Recursive Least Squares With Performance Conditions

Lee¹,

2023

IEEE Access

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“…The PID controller provides three kinds of control methods, namely: Proportional (P), Integral (I), Differential (D). In their operation, the three control parameters require good tuning, in order to provide a good and fast output response [3,4].…”

Section: Introductionmentioning

confidence: 99%

“…Many methods have been introduced to tune PID control parameters including the Rungge-Kutta method, however, in an industrial process to get good control performance in each industrial process when overshoot or oscillation occurs, often by the controller operators the control parameters are checked manually through the trial-and-error method. Every change in the process characteristics must be accompanied by a re-tuning of the control parameters [3,5]. The job of retuning the controller is a job that takes time and is very disruptive to the running process.…”

Section: Introductionmentioning

confidence: 99%

Prototype Development of Final Year Project Management System to Monitor Student’s Performance

Raznida Isa,

Shuhadah Othman,

Adam Shukry Ali

et al. 2024

ARASET

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The university requires students to complete a comprehensive software development project in their final year of study in all Information Technology (IT) and Computer Science (CS) programmes, run by the Faculty of Computing & Multimedia (FCOM), Universiti Poly-Tech Malaysia. As the number of students increases by semester, manual procedures for managing student projects become a major issue. Ineffective supervision and monitoring of student progress are more likely to occur as the number of students to supervise per lecturer also increase. This circumstance may contribute to an increase in the number of students who will fail to complete project development within the time frame specified. A log book is currently used to document meetings and discussions between supervisors and students. Although a detailed schedule has been proposed, a monitoring process that triggers specific actions to ensure all deadlines are met is required. The objective of this research is to improve project monitoring and supervision by developing a prototype of a web-based Final Year Project Management System (FYPMS). The initial prototype is built on the dashboard approach concept, where students can see their progress and lecturers can use this centralized system to monitor students' performance. This system’s users are divided into three groups: students, lecturers, and coordinators where they can communicate efficiently and effectively by using this system. By using the FYPMS, the faculty can look forward to better project management, improved supervision, fewer project delays, and a more efficient final year project process.

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MIMO modeling and multi-loop control based on neural network for municipal solid waste incineration

Ding

Tang

Qiao

2022

Control Engineering Practice

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Signal Compensation Based Adaptive Cascade Control for Regrinding Processes

Cited by 6 publications

References 20 publications

Data-Driven Adaptive Steady-State-Integral-Derivative Controller Using Recursive Least Squares With Performance Conditions

Data-Driven Adaptive Steady-State-Integral-Derivative Controller Using Recursive Least Squares With Performance Conditions

Prototype Development of Final Year Project Management System to Monitor Student’s Performance

MIMO modeling and multi-loop control based on neural network for municipal solid waste incineration

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