A data-driven approach for on-line auto-tuning of minimum variance PID controller

Zhu, Nianyong; Wei, Zimin; Huang, Chunqing

doi:10.1016/j.isatra.2022.04.001

Cited by 12 publications

(6 citation statements)

References 45 publications

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“…The proposed control algorithm has the advantage of not needing any system parameters and dynamics for target value tracking. Related previous works are based on model-free adaptive control [3], [4], [5], [6], [7], [8], [9], [10], [11], data-driven control, and artificial intelligence control [16], [17], [18], [19], [20], [21]. In the previous related works, generally, the conventional proportional-integral-derivative control scheme was adopted, and the gain adaptation law was proposed using various methods.…”

Section: Concept Of Data-driven Adaptive Steady-state-integral-deriva...mentioning

confidence: 99%

“…• Sinusoidal target velocity (simulation) Figs. [14][15][16][17][18][19][20][21][22] show the simulation-based performance evaluation results when a sinusoidal target velocity was applied as the target state.…”

Section: A Simulation-based Evaluationmentioning

confidence: 99%

“…Figs. [20][21][22] show the proportional, integral, and derivative control inputs. The magnitude of the integral control input in the adaptive case was gradually reduced by the designed adaptation rule.…”

Section: A Simulation-based Evaluationmentioning

confidence: 99%

“…Li and Yu [19] developed a novel deep-reinforcement-learning algorithm called the two-stage training strategy large-scale twin delayed deep determination policy gradient. Zhu et al [20] VOLUME 11,2023 proposed a data-driven approach for the online tuning of minimum-variance PID controllers for a linear system subject to stochastic conditions. Rahmani and Redkar [21] developed a fractional-order PID controller to control a dynamic model linearized by the Koopman operator based on dynamic mode decomposition.…”

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: Concept Of Data-driven Adaptive Steady-state-integral-deriva...mentioning

confidence: 99%

Section: A Simulation-based Evaluationmentioning

confidence: 99%

Section: A Simulation-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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“…Both the deterministic and stochastic frameworks have been proposed for the tuning of PID controllers [15]. The most commonly used conventional methods for tuning the PID controller are Trial and Error Method, Relay Tuning method, pole placement [34] and minimum variance techniques [35], Ziegler-Nichols step response method, Ziegler-Nichols frequency response method [36] and Cohen-Coon method (1953) [4]. The model of the system is not necessarily required to determine the gains of PID controller in such conventional methods which makes them advantageous over others [5].…”

Section: Introductionmentioning

confidence: 99%

Three‐time scale multi‐objective optimal PID control with filter design

Aafaque¹,

Boker

Sardahi

2023

Asian Journal of Control

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This paper presents the design of a multi-objective PID (proportional, integral, and derivative) controller in three-time scales for a system with load disturbances and sensor noise. The key to this design method is to divide the problem into three-time scales by following a singular perturbation approach, which allows for the optimization of fewer parameters in each time scale instead of all the three PID gains at once, hence less computation and design effort. The optimization objectives are the minimization of the overshoot and peak time, and the maximization of the closed-loop system's ability to reject noise and load disturbances. The impact of the integral action and filter on the optimal results is investigated by tuning the singular perturbation parameters. The obtained results show that the performance of the closed-loop system recovers the optimal solution, which is based on the fast subsystem, as the singular perturbation parameters get sufficiently small.

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Adaptive trajectory tracking control of robotic manipulators based on integral sliding mode

Qi,

Han,

Guo

et al. 2024

Asian Journal of Control

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This paper proposes an adaptive control scheme with finite‐time convergent property based on integral sliding mode to achieve trajectory tracking control of rigid robotic manipulators. The novel adaptive gain can be adjusted automatically with the system disturbance, as long as the disturbance and its derivative are bounded without requiring more information. Therefore, the control chattering effect can be mitigated obviously. The global robustness of system is guaranteed by Lyapunov stability analysis. Finally, the simulation of a two‐link manipulator is given to show that the desired trajectory tracking performance is obtained with the proposed adaptive sliding mode approach.

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A data-driven approach for on-line auto-tuning of minimum variance PID controller

Cited by 12 publications

References 45 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

Three‐time scale multi‐objective optimal PID control with filter design

Adaptive trajectory tracking control of robotic manipulators based on integral sliding mode

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