Neural Network Control of a Robotic Manipulator With Input Deadzone and Output Constraint

He, Wei; Amoateng, David Ofosu; Zhao, Yin; Sun, Changyin

doi:10.1109/tsmc.2015.2466194

Cited by 339 publications

(185 citation statements)

References 70 publications

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“…Lemma 7 (see [43]). The Gaussian function described in (9) witĥ= − being the input vector, where is a bounded vector and is a positive, is given as…”

Section: Assumption 6 There Are Ideal Constant Weights Such That | | ≤mentioning

confidence: 99%

Constrained Adaptive Neural Control of Nonlinear Strict‐Feedback Systems with Input Dead‐Zone

Shi

2017

Mathematical Problems in Engineering

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This paper focuses on a single neural network tracking control for a class of nonlinear strict-feedback systems with input dead-zone and time-varying output constraint via prescribed performance method. To release the limit condition on previous performance function that the initial tracking error needs to be known, a new modified performance function is first constructed. Further, to reduce the computational burden of traditional neural back-stepping control approaches which require all the virtual controllers to be necessarily carried out in each step, the nonlinear items are transmitted to the last step such that only one neural network is required in this design. By regarding the input-coefficients of the dead-zone slopes as a system uncertainty and introducing a new concise system transformation technique, a composite adaptive neural state-feedback control approach is developed. The most prominent feature of this scheme is that it not only owes low-computational property but also releases the previous limitations on performance function and is capable of guaranteeing the output confined within the new form of prescribed bound. Moreover, the closed-loop stability is proved using Lyapunov function. Comparative simulation is induced to verify the effectiveness.

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“…Lemma 7 (see [43]). The Gaussian function described in (9) witĥ= − being the input vector, where is a bounded vector and is a positive, is given as…”

Section: Assumption 6 There Are Ideal Constant Weights Such That | | ≤mentioning

confidence: 99%

Constrained Adaptive Neural Control of Nonlinear Strict‐Feedback Systems with Input Dead‐Zone

Shi

2017

Mathematical Problems in Engineering

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“…[9][10][11][12][13][14][15][16][17][18][19][20] In the work of El-Farra et al, 16 a state feedback control law and an output feedback control law were developed to guarantee the stabilization of the closed-loop system for parabolic PDEs systems in the presence of input saturation, respectively. Considering external disturbance, two different control methods were proposed to investigate the problem of stabilization for a one-dimensional wave, which was represented by a set of PDEs in the work of Guo and Jin.…”

Section: Introductionmentioning

confidence: 99%

Robust adaptive constrained boundary control for a suspension cable system of a helicopter

Ren

Shi

2017

Adaptive Control & Signal

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SummaryIn this article, the problems of modeling and controlling are investigated for a suspension cable system of a helicopter with input saturation, system parameter uncertainties, and external disturbances by using the boundary control method.In accordance with the Hamilton's principle, the model of the suspension cable system of a helicopter is established by using a set of partial differential and ordinary differential equations. Considering nonsymmetric saturation constraint, the auxiliary systems are designed to handle with the effect of input saturation. Considering Lyapunov's direct method and the designed auxiliary systems, two robust adaptive boundary controllers are provided by the actuators at the helicopter and the box. Under the proposed controllers, the error between the bottom payload and the target location and the vibration range are uniformly ultimately bounded. Moreover, they will converge to a small neighborhood of zero by selecting the suitable parameters. Meanwhile, to guarantee the validity of the proposed adaptive boundary control laws, some sufficient conditions are raised. Simulation results are provided to verify that the effectiveness of the designed controllers in this paper.

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“…In practice, there are many different kinds of constraints in most of physical systems, such as output or state constraints, tracking performance constraints. The violation of the constraints may cause severe performance degradation, safety problem, or system damage [35]. Therefore, it is of great importance for solving the control and learning problem of constrained systems.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, it is of great importance for solving the control and learning problem of constrained systems. Based on Lyapunov theorem, a barrier Lyapunov function (BLF) method has been presented to solve output constraints for strict-feedback nonlinear systems [36], output feedback nonlinear systems [37], flexible systems [38], and robotic manipulator [35,39]. The BLF-based methods were also extended to solve state constraints [40][41][42].…”

Section: Introductionmentioning

confidence: 99%

“…To solve partial tracking error constraints, a fuzzy dynamic surface control design was developed in [49,50] for a class of strict-feedback nonlinear systems by transforming the state tracking errors into new virtual variables. However, the existing control schemes, such as [35][36][37][38][39][40][41][42][43][44][45][46][47][48][49][50][51], can only guarantee the stability of closed-loop systems with different constraints, which are not capable of achieving the learning of unknown system dynamics. The main reason is that the derived closed-loop error system is extremely complex, such that its exponential convergence is difficult to be verified using the existing stability analysis tools.…”

Section: Introductionmentioning

confidence: 99%

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Dynamic Learning from Adaptive Neural Control of Uncertain Robots with Guaranteed Full-State Tracking Precision

Wang

Zhang

2017

Complexity

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A dynamic learning method is developed for an uncertain -link robot with unknown system dynamics, achieving predefined performance attributes on the link angular position and velocity tracking errors. For a known nonsingular initial robotic condition, performance functions and unconstrained transformation errors are employed to prevent the violation of the full-state tracking error constraints. By combining two independent Lyapunov functions and radial basis function (RBF) neural network (NN) approximator, a novel and simple adaptive neural control scheme is proposed for the dynamics of the unconstrained transformation errors, which guarantees uniformly ultimate boundedness of all the signals in the closed-loop system. In the steadystate control process, RBF NNs are verified to satisfy the partial persistent excitation (PE) condition. Subsequently, an appropriate state transformation is adopted to achieve the accurate convergence of neural weight estimates. The corresponding experienced knowledge on unknown robotic dynamics is stored in NNs with constant neural weight values. Using the stored knowledge, a static neural learning controller is developed to improve the full-state tracking performance. A comparative simulation study on a 2-link robot illustrates the effectiveness of the proposed scheme.

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Neural Network Control of a Robotic Manipulator With Input Deadzone and Output Constraint

Cited by 339 publications

References 70 publications

Constrained Adaptive Neural Control of Nonlinear Strict‐Feedback Systems with Input Dead‐Zone

Constrained Adaptive Neural Control of Nonlinear Strict‐Feedback Systems with Input Dead‐Zone

Robust adaptive constrained boundary control for a suspension cable system of a helicopter

Dynamic Learning from Adaptive Neural Control of Uncertain Robots with Guaranteed Full-State Tracking Precision

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