Parameter space optimization towards integrated mechatronic design for uncertain systems with generalized feedback constraints

Ma, Jun; Chen, Silu; Teo, Chek Sing; Tay, Arthur; Mamun, Abdullah Al; Tan, Kok Kiong

doi:10.1016/j.automatica.2019.03.010

Cited by 35 publications

(14 citation statements)

References 35 publications

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“…For a typical precision motion system such as the maglev planar positioning stages, high-performance tracking of a specific reference profile is an essential objective pursued by motion control researchers and engineers, where stringent tracking performance is required to be ensured in terms of both accuracy and smoothness [5]- [7]. Consequently, various research efforts have been carried out to improve the tracking performance via, say, the model-based approaches [8]- [10] or the data-driven approaches [11]- [13].…”

Section: Introductionmentioning

confidence: 99%

Convex Parameterization and Optimization for Robust Tracking of a Magnetically Levitated Planar Positioning System

Cheng

Zhu

et al. 2021

Preprint

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Magnetic levitation positioning technology has attracted considerable research efforts and dedicated attention due to its extremely attractive features. The technology offers highprecision, contactless, dust/lubricant-free, multi-axis, and largestroke positioning. In this work, we focus on the accurate and smooth tracking problem of a multi-axis magnetically levitated (maglev) planar positioning system for a specific S-curve reference trajectory. The floating characteristics and the multi-axis coupling make accurate identification of the system dynamics difficult, which lead to a challenge to design a high performance control system. Here, the tracking task is achieved by a 2-Degree of Freedom (DoF) controller consisting of a feedforward controller and a robust stabilizing feedback controller with a prescribed sparsity pattern. The approach proposed in this paper utilizes the basis of an H∞ controller formulation and a suitably established convex inner approximation. Particularly, a subset of robust stabilizable controllers with prescribed structural constraints is characterized in the parameter space, and so thus the re-formulated convex optimization problem can be easily solved by several powerful numerical algorithms and solvers. With this approach, the robust stability of the overall system is ensured with a satisfactory system performance despite the presence of parametric uncertainties. Furthermore, experimental results clearly demonstrate the effectiveness of the proposed approach.

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Section: Introductionmentioning

confidence: 99%

Convex Parameterization and Optimization for Robust Tracking of a Magnetically Levitated Planar Positioning System

Cheng

Zhu

et al. 2021

Preprint

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“…For example, refs. [9,10] used the H 2 /H ∞ method to obtain the controller tuning parameters. PID values can also be directly optimized, as shown in [11] for a DC motor.…”

Section: Introductionmentioning

confidence: 99%

Optimal Hardware and Control Co-Design Applied to an Active Car Suspension Setup

et al. 2021

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For complex systems, it is not easy to obtain optimal designs for the hardware architecture and control configurations. Every design aspect influences the final performance, and often the interactions of the different components cannot be clearly determined in advance. In this work, a novel co-design optimization method was applied that allows the optimal placement and selection of actuators and sensors to be performed simultaneously with the determination of the control architecture and associated controller tuning parameters. This novel co-design method was applied to a state-space model of a downscaled active car suspension laboratory setup. This setup mimics a car driving over a specific road surface while active components in the suspension have to increase the driver’s comfort by counteracting unwanted vibrations. The result of this co-design optimization methodology is a Pareto front that graphically represents the trade-off between the maximum performance and the total implementation cost; the co-design results were validated with measurements of the physical active car suspension setup. The obtained controller tuning parameters are compared herein with existing controller tuning methods to demonstrate that the co-design method is able to determine optimal controller tuning parameters.

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“…However, it is pertinent to note that the decoupled model is essentially an approximation, and thus could limit the contouring performance attainable. As alternatives in the literature, the multi-axis coupling can also be modeled with certain assumptions and then a decentralized control scheme is developed for the gantry system [13], [14]. However, the system identification for such multi-axis systems is inevitably inaccurate [15], [16].…”

Section: Introductionmentioning

confidence: 99%

Global Iterative Sliding Mode Control of an Industrial Biaxial Gantry System for Contouring Motion Tasks

Wang,

Ma,

Cheng

et al. 2021

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This paper proposes a global iterative sliding mode control approach for high-precision contouring tasks of a flexurelinked biaxial gantry system. For such high-precision contouring tasks, it is the typical situation that the involved multi-axis cooperation is one of the most challenging problems. As also would be inevitably encountered, various factors render the multi-axis cooperation rather difficult; such as the strong coupling (which naturally brings nonlinearity) between different axes due to its mechanical structure, the backlash and deadzone caused by the friction, and the difficulties in system identification, etc. To overcome the above-mentioned issues, this work investigates an intelligent model-free contouring control method for such a multiaxis motion stage. Essentially in the methodology developed here, it is firstly ensured that all the coupling, friction, nonlinearity, and disturbance (regarded as uncertain dynamics in each axis) are suitably posed as 'uncertainties'. Then, a varying-gain sliding mode control method is proposed to adaptively compensate for the matched unknown dynamics in the time domain, while an iterative learning law is applied to suppress the undesirable effects (arising from the repetitive matched and unmatched uncertainties in the iteration domain). With this approach, the chattering that typically results from the overestimated control gains in the sliding mode control is thus suppressed during the iterations. To analyze the contouring performance and show the improved outcomes, rigorous proof is furnished on both the stability in the time domain and the convergence in the iteration domain; and the real-time experiments also illustrate that the requirements of precision motion control towards highspeed and complex-curvature references can be satisfied using the proposed method, without prior knowledge of the boundary to the unknown dynamics.

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Parameter space optimization towards integrated mechatronic design for uncertain systems with generalized feedback constraints

Cited by 35 publications

References 35 publications

Convex Parameterization and Optimization for Robust Tracking of a Magnetically Levitated Planar Positioning System

Convex Parameterization and Optimization for Robust Tracking of a Magnetically Levitated Planar Positioning System

Optimal Hardware and Control Co-Design Applied to an Active Car Suspension Setup

Global Iterative Sliding Mode Control of an Industrial Biaxial Gantry System for Contouring Motion Tasks

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