Prescribed Performance Adaptive Control for Multi-Input Multi-Output Affine in the Control Nonlinear Systems

Bechlioulis, Charalampos P.; Rovithakis, George A.

doi:10.1109/tac.2010.2042508

Cited by 438 publications

(271 citation statements)

References 12 publications

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“…This section introduces preliminary knowledge on the prescribed performance concepts reported in [20,21]. According to [20,21], the prescribed performance is achieved by ensuring that tracking error 1 ( ) = − evolves strictly within predefined decaying bounds as follows:…”

Section: Performance Function and Errormentioning

confidence: 99%

“…It is worth pointing out that a design solution called prescribed performance control has been proposed in [18] for a class of feedback linearization nonlinear systems and was extended to the class of nonlinear systems in [19][20][21]. Its main idea is to introduce predefined performance bounds of the tracking errors and it is able to adjust control performance indices.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Adaptive Fuzzy Fault-Tolerant Output Feedback Tracking Control of Uncertain Stochastic Nonlinear Systems with Unknown Time-Delay and Tracking Error Constrained

Sui

Tong

2014

Journal of Applied Mathematics

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The problem of tracking error constrained adaptive fuzzy output feedback control is investigated for a class of single-input and single-output (SISO) stochastic nonlinear systems with actuator faults, unknown time-delay, and unmeasured states. The considered faults are modeled as both loss of effectiveness and lock-in-place. The fuzzy logic systems are used to approximate the unknown nonlinear functions, and a fuzzy adaptive observer is designed for estimating the unmeasured states. By transforming the tracking errors into new virtual error variables and based on backstepping recursive design technique, a new fuzzy adaptive output feedback control method is developed. It is shown that all the signals of the resulting closed-loop system are bounded in probability and the tracking error remains an adjustable neighborhood of the origin within the prescribed bounds. The simulation results are provided to show the effectiveness of the proposed approach.

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Section: Performance Function and Errormentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Adaptive Fuzzy Fault-Tolerant Output Feedback Tracking Control of Uncertain Stochastic Nonlinear Systems with Unknown Time-Delay and Tracking Error Constrained

Sui

Tong

2014

Journal of Applied Mathematics

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“…In [14], a constrainedinput system is tackled in combination with the optimal control to ensure a good tradeoff between control performance and energy consumption. For output constraints, artificial potential field [15], prescribed performance control [16,17], model predictive control [18], and reference governor [19] are some of the existing strategies to handle this problem. In [20,21], Barrier Lyapunov Function (BLF) is introduced which needs less initial conditions and does not require explicit system solution.…”

Section: Introductionmentioning

confidence: 99%

Neural Network Based Central Heating System Load Prediction and Constrained Control

Wang

Tu³

et al. 2018

Mathematical Problems in Engineering

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A neural network (NN) based heating system load prediction and control scheme are proposed. Different from traditional physical principle based load calculation method, a multilayer NN is incorporated with selected input features and trained to predict the heating load as well as the desired supply water temperature in heating supply loop. In this manner, a complicated load calculation model can be replaced by simple but efficient data-driven scheme and the response time to outdoor temperature variation can be enhanced. Moreover, in order to handle the input and output constraints in valve opening degree control task to achieve desired supply water temperature, Barrier Lyapunov candidate function and axillary system technique are involved. An additional NN is employed to approximate the system transfer function with reliable accuracy. The stability of the system is guaranteed through rigorous mathematical analysis. The excellent performance of the novelly proposed control over traditional PID is demonstrated via extensive simulation study. A quantitative case study is also conducted to verify the flexibility and validity of proposed load prediction strategy.

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“…Therefore, it implies that the state constraints have to be symmetric. On the other hand, in [36], [37], prescribed performance adaptive neural network control is developed to constrain the error signals within a prescribed region, which is shaped by a performance function. This method may be extended to handle the problem of motion constraints, but it is not straightforward to define the corresponding performance function which is related to the tracking error and is required to be exponentially decaying.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Control of Robotic Manipulators With Unified Motion Constraints

et al. 2017

IEEE Trans. Syst. Man Cybern, Syst.

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Abstract-In this paper, we present an adaptive control of robotic manipulators with parametric uncertainties and motion constraints. Position and velocity constraints are considered and they are unified and converted into the constraint of the nominal input. An adaptive neural network control is developed to achieve trajectory tracking, while the problems of motion constraints are addressed by considering the saturation effect of the nominal input. The uniform boundedness of all closed-loop signals is verified through Lyapunov analysis. Simulation and experiment results on a 2 DOF robotic manipulator demonstrate the effectiveness of the proposed method.

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Prescribed Performance Adaptive Control for Multi-Input Multi-Output Affine in the Control Nonlinear Systems

Cited by 438 publications

References 12 publications

Adaptive Fuzzy Fault-Tolerant Output Feedback Tracking Control of Uncertain Stochastic Nonlinear Systems with Unknown Time-Delay and Tracking Error Constrained

Adaptive Fuzzy Fault-Tolerant Output Feedback Tracking Control of Uncertain Stochastic Nonlinear Systems with Unknown Time-Delay and Tracking Error Constrained

Neural Network Based Central Heating System Load Prediction and Constrained Control

Adaptive Control of Robotic Manipulators With Unified Motion Constraints

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