Output-Constrained Robust Adaptive Control for Uncertain Nonlinear MIMO Systems With Unknown Control Directions

Sachan, Kapil; Padhi, Radhakant

doi:10.1109/lcsys.2019.2919814

Cited by 26 publications

(8 citation statements)

References 20 publications

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“…The effectiveness of the proposed controller is demonstrated by controlling a two-link robot manipulator in a constrained environment. Note that the proposed controller is significantly different from our previous work [31,32], where a nominal controller is formulated in [31] for known system dynamics, and in [32], only output constraints are considered in the adaptive control formulation.…”

Section: Introductionmentioning

confidence: 93%

“…Assumption 2 is also standard in constrained control design literature, which is required to avoid conflict between tracking and constraint satisfaction [10-12, 18-22, 24]. Assumption 3 is necessary to maintain the controllability of the system for all time [32].…”

Section: Actual Systemmentioning

confidence: 99%

“…where v i (0) is the initial value of the Lyapunov function (20) and e represents exponential. By analysing (32), the upper bound on…”

Section: Approximation Of Modelling Uncertaintiesmentioning

confidence: 99%

“…(i) Select the desired state constraints κ i , κ i , ζ i and ζ i , and the ultimate tracking error bounds ±ρ i and ±σ i , ∀i. (ii) Using the relations in (31), (32), (35) and (36), Compute the tracking error constraints ζ e˙i , ζ e˙i , κ e i and κ e i .…”

Section: Implementation Stepsmentioning

confidence: 99%

See 3 more Smart Citations

Safety‐constrained robust adaptive control for a class of MIMO non‐linear systems

Sachan¹,

Padhi²

2020

IET Control Theory & Applications

Self Cite

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Section: Introductionmentioning

confidence: 93%

Section: Actual Systemmentioning

confidence: 99%

“…where v i (0) is the initial value of the Lyapunov function (20) and e represents exponential. By analysing (32), the upper bound on…”

Section: Approximation Of Modelling Uncertaintiesmentioning

confidence: 99%

Section: Implementation Stepsmentioning

confidence: 99%

See 2 more Smart Citations

Safety‐constrained robust adaptive control for a class of MIMO non‐linear systems

Sachan¹,

Padhi²

2020

IET Control Theory & Applications

Self Cite

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“…Based on the adaptive laws and finite-time stability theory, a nonsingular terminal sliding mode control is designed in literature [14]. Literature [15] proposes a new output-constrained robust adaptive controller for a class of uncertain multi-input multi-output (MIMO) nonlinear systems. These methods which are simple to implement, do not require the controlled object model.…”

Section: Introductionmentioning

confidence: 99%

Data-Driven Nonlinear Near-Optimal Regulation Based on Multi-Dimensional Taylor Network Dynamic Programming

et al. 2020

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Using the data-driven control formulation, an iterative dynamic programming approach which is based on a multi-dimensional Taylor network is established to design the near optimal regulation of discrete-time nonlinear systems. For discrete-time general nonlinear systems, the iterative adaptive dynamic programming algorithm is developed and proved to guarantee the property of convergence and optimality. Three networks are constructed, namely, the identification network, critic network and action network. Moreover, a globalized dual heuristic programming technique with detailed implementation is developed. The cost function and its derivative can be approximated by this novel architecture. Besides, without the consideration of the system dynamics, this technique can learn the near-optimal control law simultaneously and adaptively. In addition, this technique greatly improves the existing results of the iterative adaptive dynamic programming algorithm in terms of reducing the requirement of the control matrix. Furthermore, because of the approach that is based on the multi-dimensional Taylor network, the amount of calculation needed is also greatly reduced. The simulation experiment is described to illustrate the effectiveness of the data-driven optimal regulation method proposed in this paper. INDEX TERMS Adaptive dynamic programming, data-driven control, multi-dimensional Taylor network, nonlinear control.

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Motion Control with Hard Constraints – Adaptive Controller with Nonlinear Integration

Kabziński

Mosiołek

2020

Advances in Intelligent Systems and Computing

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Output-Constrained Robust Adaptive Control for Uncertain Nonlinear MIMO Systems With Unknown Control Directions

Cited by 26 publications

References 20 publications

Safety‐constrained robust adaptive control for a class of MIMO non‐linear systems

Safety‐constrained robust adaptive control for a class of MIMO non‐linear systems

Data-Driven Nonlinear Near-Optimal Regulation Based on Multi-Dimensional Taylor Network Dynamic Programming

Motion Control with Hard Constraints – Adaptive Controller with Nonlinear Integration

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