Anti‐disturbance inverse optimal control for systems with disturbances

Fan, Zhongxin; Adhikary, Avizit Chandra; Li, Shihua; Liu, Rongjie

doi:10.1002/oca.2789

Cited by 9 publications

(10 citation statements)

References 41 publications

(97 reference statements)

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“…In practical environment, helicopters are often subjected to the model uncertainties and matched or mismatched external disturbances [25,26]. To actively reduce the adverse effects of so-called uncertainty and/or disturbance, the disturbance observer-based control (DOBC) method has received extensive attention for helicopters due to its excellent performance to deal with disturbances and uncertainties; for example, previous studies [17,[27][28][29]. However, many parameters of these disturbance observers depend on manual tuning off-line.…”

Section: Introductionmentioning

confidence: 99%

Observer‐based robust optimal control for helicopter with uncertainties and disturbances

Qiu

Liu

et al. 2023

Asian Journal of Control

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Unknown model uncertainties and external disturbances widely exist in helicopter dynamics and bring adverse effects on control performance. Optimal control techniques have been extensively studied for helicopters, but these methods cannot effectively handle flight control problems since they are sensitive to uncertainties and disturbances. This paper proposes an observer‐based robust optimal control scheme that enables a helicopter to fly optimally and reduce the influence of unknown model uncertainties and external disturbances. A control Lyapunov function (CLF) is firstly constructed using the backstepping method, then Sontag's formula is utilized to design an inverse optimal controller to stabilize the nominal system. Furthermore, it is stressed that the radial basis function (RBF) neural network is introduced to establish an observer with adaptive laws, approximating and compensating for the unknown model uncertainties and external disturbances to enhance the robustness of the closed‐loop system. The uniform ultimate boundedness of the closed‐loop system is ensured using the presented control approach via Lyapunov stability analysis. Finally, simulation results are presented to demonstrate the effectiveness of the proposed control strategy.

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Section: Introductionmentioning

confidence: 99%

Observer‐based robust optimal control for helicopter with uncertainties and disturbances

Qiu

Liu

et al. 2023

Asian Journal of Control

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“…By choosing an appropriate disturbance compensation gain, a generalized extended state observer-based control method was proposed for nonintegral-chain systems subject to mismatched uncertainties [34]. By combining PPC with DOB, earlier studies [16,35] investigated the prescribed performance output tracking problem for a class of nonlinear systems with mismatched disturbances, Fan et al [36] proposed a novel antidisturbance inverse optimal controller design for a class of high-dimensional chain structure systems with disturbances. However, the tracking errors in previous studies [16,35] cannot achieve asymptotic convergence.…”

Section: Introductionmentioning

confidence: 99%

“…However, the tracking errors in previous studies [16,35] cannot achieve asymptotic convergence. Specifically, the backstepping method [37,38] was born to provide a systematic methodology in the construction of the desired controller, the works [16,36,39,40] combined backstepping method, and DOBC and obtained some meaningful results for several nonlinear systems with multiple mismatched disturbances.…”

Section: Introductionmentioning

confidence: 99%

Disturbance observer‐based tracking control with prescribed performance specifications for a class of nonlinear systems subject to mismatched disturbances

Zhou

Dai

Yang

et al. 2022

Asian Journal of Control

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This work investigates simultaneous prescribed performance tracking control and mismatched disturbance rejection problems for a class of strict‐feedback nonlinear systems. A novel control scheme combining prescribed performance control, disturbance observer technique, and backstepping method is proposed. The disturbance estimations are introduced into the design of virtual control law design in each step to compensate the mismatched disturbances. To further improve the control performance, a prescribed performance function characterizing the error convergence rate, maximum overshoot, and steady‐state error is used to construct the composite controller. The proposed controller guarantees transient and steady‐state performance specifications of tracking error and provides much better disturbance attenuation ability simultaneously. Rigorous stability analysis for the closed‐loop system is established by direct Lyapunov function method. It is shown that all the states in the resulting closed‐loop system are stable, and the tracking error evolves within the prescribed performance boundaries and asymptotically converges to zero even in the presence of mismatched external disturbances. Finally, theoretical results are illustrated and demonstrated by two simulation examples.

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“…A method that applies event‐triggered mechanism

{\mathrm{H}}_{\infty }

control to continuous‐time nonlinear systems with asymmetric constraints based on dual heuristic dynamic programming structure is proposed 16 . A novel anti‐disturbance inverse optimal controller design method is proposed for a class of high‐dimensional chain structure systems with any disturbances, matched, or mismatched 17 . A data‐driven

{\mathrm{H}}_{\infty }

controller design method is studied for continuous‐time linear periodic systems 18 .…”

mentioning

confidence: 99%

“…11 A novel optimal constraint-following controller is proposed for uncertain mechanical systems. 12 The third group of papers [13][14][15][16][17][18][19] focuses on robustness on data-based optimal learning control. A novel Nash game-theoretical optimal adaptive robust control design approach is proposed to address the constraint-following control problem for the uncertain underactuated mechanical systems with fuzzy evidence theory.…”

mentioning

confidence: 99%

Data‐based learning control for optimization of nonlinear systems

Wei

Song

Zhang

et al. 2023

Optim Control Appl Methods

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With the development of science and technology, practical systems such as the power systems, traffic systems, robot manipulator systems, etc., have become more complex. Therefore, it is difficult to build practical systems by accurate models. Under the lack of accurate process models, using system data to improve system performance and learn optimal decisions becomes very important. Through the recent years, data-based learning control theories and technologies have widely been investigated, including adaptive dynamic programming, reinforcement learning, iterative learning control, and so on. Data-based methods require the system data instead of the accurate knowledge of system dynamics that can be considered as model-free learning control methods. The data-based methods are effective solutions for the optimal control of nonlinear systems, which motivate this special issue.This special issue aims to collect and present original research dealing with data-based learning and their applications for optimization and control problems. The first group of papers [1][2][3][4][5][6][7] focuses on data-based control theory, approaches, and applications. A fuzzy model predictive control approach is proposed for stick-slip type piezoelectric actuator to realize the precise control of the end effector. 1 A systematic online adaptive dynamic programming control framework is proposed for smart buildings control to ensure hard constraints to be satisfied. 2 A multi-verse optimizer tuned PI-type active disturbance rejection generalized predictive control method is described for the motion control problems of ships. 3 The sufficient optimality conditions for the optimal controls are established under some convexity assumptions. 4 A receding-horizon reinforcement learning algorithm is proposed for near-optimal control of continuous-time systems under control constraints. 5 In order to solve the interference compensation control problem of a class of nonlinear systems, a method based on memory data is introduced to suppress interference greatly. 6 A new controller design method is proposed for the trajectory tracking problem of robots with imprecise dynamic properties and interference. 7 The second group of papers 8-12 considers iterative learning identification and iterative learning control. An iterative learning control approach is proposed for linear parabolic distributed parameter systems with multiple actuators and multiple sensors. 8 The quantized data-based iterative learning tracking control problem is studied for nonlinear networked control systems with signals quantization and denial-of-service attacks. 9 The output tracking problem is considered for a class of nonlinear parabolic distributed parameter systems with moving boundaries. 10 A just-in-time learning based dual heuristic programming algorithm is proposed to optimize the control performance of autonomous wheeled mobile robots under faults or disturbances. 11 A novel optimal constraint-following controller is proposed for uncertain mechanical systems. 12 The third...

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Anti‐disturbance inverse optimal control for systems with disturbances

Cited by 9 publications

References 41 publications

Observer‐based robust optimal control for helicopter with uncertainties and disturbances

Observer‐based robust optimal control for helicopter with uncertainties and disturbances

Disturbance observer‐based tracking control with prescribed performance specifications for a class of nonlinear systems subject to mismatched disturbances

Data‐based learning control for optimization of nonlinear systems

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