Application of Linear Model Predictive Control and Input-Output Linearization to Constrained Control of 3D Cable Robots

Ghasemi, Ali

doi:10.4236/mme.2011.12009

Cited by 7 publications

(7 citation statements)

References 13 publications

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“…The mapping problem, at each sampling time k, can be defined as deriving the constraints of the following form: This transformation process should be solved at each sampling time, as the mapping is output dependent. That is, for finding the constraints in (17) at each sampling time, mapping is performed by solving an optimization problem defined in (18) and output vector at current sampling time, y k . The resulting optimization problem is defined as the following:…”

Section: B Mapping Input Constraintsmentioning

confidence: 99%

“…On the other hand, it is difficult to compute the constraints for future inputs over the prediction window [v k+1|k , v k+2|k , ..., v k+W −1|k ]. Note that in order to solve the optimization problem formulated in (18) to obtain the mapped constraints in (17), the estimates of the future values of input and output variables are needed. However, these estimates are not available until the LMPC problem is solved that in turn requires the mapped input constraints in (17) over the entire prediction window [17].…”

Section: B Mapping Input Constraintsmentioning

confidence: 99%

“…However, these estimates are not available until the LMPC problem is solved that in turn requires the mapped input constraints in (17) over the entire prediction window [17]. To address this problem, we use a similar but slightly modified approach illustrated in [18] described as follows.…”

Section: B Mapping Input Constraintsmentioning

confidence: 99%

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Linear Model-Predictive Controller (LMPC) for Building’s Heating Ventilation and Air Conditioning (HVAC) System

Ostadijafari

Dubey

2019

2019 IEEE Conference on Control Technology and Applications (CCTA)

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Model predictive control (MPC) is a widely used technique for temperature set-point tracking and energy optimization of Heating Ventilation and Air Conditioning (HVAC) systems in buildings. Unfortunately, a nonlinear thermal building model leads to a computationally expensive nonlinear MPC problem that is not suitable for real-time control and optimization. This paper presents a novel approximate linearized model for building's thermal dynamics and the HVAC system power consumption that leads to a computationally efficient linear model predictive controller (LMPC) for the buildings' HVAC systems. We employ feedback linearization technique to obtain an equivalent linearized model for the nonlinear thermal building dynamics and use constraint mapping approach to obtain a linearized formulation for new control variables. Next, using piecewise linearization, we obtain a linearized analytical model for the HVAC system power consumption. The proposed LMPC technique is validated using multiple simulation case studies. We demonstrate that the proposed LMPC technique is not only computationally efficient but also accurately approximates the nonlinear optimal control decisions.

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Section: B Mapping Input Constraintsmentioning

confidence: 99%

Section: B Mapping Input Constraintsmentioning

confidence: 99%

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Linear Model-Predictive Controller (LMPC) for Building’s Heating Ventilation and Air Conditioning (HVAC) System

Ostadijafari

Dubey

2019

2019 IEEE Conference on Control Technology and Applications (CCTA)

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“…Linear or linearized models are the simplest but most effective to implement feedforward control in robot trajectories [30]. …”

Section: Theorymentioning

confidence: 99%

“…Feedforward control can improve the accuracy of the control by as much as an order of magnitude due to the anticipation of the plant changes and faster response: it mainly depends on the accuracy of the model used to predict the following outputs of the plant. Linear or linearized models are the simplest but most effective to implement feedforward control in robot trajectories [ 30 ].…”

Section: Theorymentioning

confidence: 99%

A Feedfordward Adaptive Controller to Reduce the Imaging Time of Large-Sized Biological Samples with a SPM-Based Multiprobe Station

Otero

Guerrero

González

et al. 2012

Sensors

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The time required to image large samples is an important limiting factor in SPM-based systems. In multiprobe setups, especially when working with biological samples, this drawback can make impossible to conduct certain experiments. In this work, we present a feedfordward controller based on bang-bang and adaptive controls. The controls are based in the difference between the maximum speeds that can be used for imaging depending on the flatness of the sample zone. Topographic images of Escherichia coli bacteria samples were acquired using the implemented controllers. Results show that to go faster in the flat zones, rather than using a constant scanning speed for the whole image, speeds up the imaging process of large samples by up to a 4× factor.

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Position control of a planar cable-driven parallel robot using reinforcement learning

2022

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This study proposes a method based on reinforcement learning (RL) for point-to-point and dynamic reference position tracking control of a planar cable-driven parallel robots, which is a multi-input multi-output system (MIMO). The method eliminates the use of a tension distribution algorithm in controlling the system’s dynamics and inherently optimizes the cable tensions based on the reward function during the learning process. The deep deterministic policy gradient algorithm is utilized for training the RL agents in point-to-point and dynamic reference tracking tasks. The performances of the two agents are tested on their specifically trained tasks. Moreover, we also implement the agent trained for point-to-point tasks on the dynamic reference tracking and vice versa. The performances of the RL agents are compared with a classical PD controller. The results show that RL can perform quite well without the requirement of designing different controllers for each task if the system’s dynamics is learned well.

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Application of Linear Model Predictive Control and Input-Output Linearization to Constrained Control of 3D Cable Robots

Cited by 7 publications

References 13 publications

Linear Model-Predictive Controller (LMPC) for Building’s Heating Ventilation and Air Conditioning (HVAC) System

Linear Model-Predictive Controller (LMPC) for Building’s Heating Ventilation and Air Conditioning (HVAC) System

A Feedfordward Adaptive Controller to Reduce the Imaging Time of Large-Sized Biological Samples with a SPM-Based Multiprobe Station

Position control of a planar cable-driven parallel robot using reinforcement learning

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