Predictor-Based Control of a Class of Time-Delay Systems and Its Application to Quadrotors

Sanz, Ricardo; Garcia, Pedro Castillo; Zhong, Qing-Chang; Albertos, Pedro

doi:10.1109/tie.2016.2609378

Cited by 114 publications

(43 citation statements)

References 37 publications

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“…3. This is mainly due to the fact that PID controllers are relatively easy to implement and program, fine-tune, and are conceptually straightforward.…”

Section: Motivationmentioning

confidence: 99%

“…Moreover, a zero convergence proof of the control errors was included and it was validated through numerical simulations. In addition, other control approaches have been used for multicopters navigation such as predictive control, 3 sliding-mode control approach, 4 and NN trained using reinforcement learning techniques, 5 inverse dynamic, 6 and inverse kinematics considering energy consumption. 7 Therefore, the great challenge of how to effectively control a UAV to precisely track a desired trajectory is still subject to active research in UAV control.…”

Section: Motivationmentioning

confidence: 99%

“…The exact model of the system (in this case, the UAV) does not need to be known. 3. The design of a MIMO AN-PID controller is straightforward and rooted in the topology of a dynamic NN, which is adaptable.…”

Section: Motivationmentioning

confidence: 99%

See 2 more Smart Citations

Identification and adaptive PID Control of a hexacopter UAV based on neural networks

Rosales

Soria

Rossomando

2018

Adaptive Control & Signal

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In this paper, a novel adaptive PID controller for trajectory-tracking tasks is proposed. It is implemented in discrete time over a hexacopter, and it takes into consideration the unmanned aerial vehicles (UAVs) nonlinear model. The PID controller is developed following an adaptive neural technique, and its stability is verified by the Lyapunov discrete theory. Besides, the neural identification of the dynamic model of the UAV is presented to backpropagate output errors to adjust PID gains with the purpose of reducing the control errors. The validation of the proposed algorithm is performed through experimental results with a hexacopter. KEYWORDSadaptive PID, discrete stability analysis, hexacopter, identification, neural networks 74 ;33:74-91. ROSALES ET AL. 75steady-state errors, and the derivative gain is used to modify any system overshoot. However, since quadrotor is a nonlinear underactuated systems, 8 it is not always possible to use PID control directly for the quadrotor system. Consequently, some authors proposed controllers designed based only on static PID for the UAV control such as the controllers presented in other works. [9][10][11] Other studies proposed the UAV control based on PID controllers with artificial intelligence and dynamic properties, such as the controllers presented in the work of Yang et al, 12 where authors defined a single-neuron PID, where the adjusting of the weights is a function of the control errors. Besides, simulations results were displayed to validate the proposal but the stability analysis is omitted. In addition, Xu and Zhou 13 use self-tuning PID controller based on adaptive pole placement to a quadrotor by using a linearized version of the dynamic model. Another class of PID controllers had the capabilities to backpropagate control errors for a better adjust of the controller gains, some of those are described in this work. Tsakalis and Dash 14 proposed an algorithm for  ∞ for online tuning of PID gains. The estimator is very complex to implement, although is robust with respect to the excitation spectrum. The algorithm is validated by using simulations results for different transfer functions. In the work of Liu et al, 15 the design of a Gaussian potential function network PID (GPFN-PID) controller based on GPFN NN was described. The proposed controller had the functions of online training, self-training, and self-adjusting, which made control of the UAV longitudinal channel more effective. However, the results obtained were based on simulations. Babu et al 16 proposed a gradient descent-based methodology for adjusting online the gains of a PID controller based on a cost function defined to penalize errors. The controller is designed only for positioning task and a commercial quadrotor is used to experiment the controller in position task. In the work of Wang et al, 17 an intelligent PID controller based on radial basis functions (RBFs) NN was designed for longitudinal attitude control of a small UAV using a nonlinear model. An RBF NN was utilized for online updatin...

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“…3. This is mainly due to the fact that PID controllers are relatively easy to implement and program, fine-tune, and are conceptually straightforward.…”

Section: Motivationmentioning

confidence: 99%

Section: Motivationmentioning

confidence: 99%

See 1 more Smart Citation

Identification and adaptive PID Control of a hexacopter UAV based on neural networks

Rosales

Soria

Rossomando

2018

Adaptive Control & Signal

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show abstract

“…In [28], a subset of DOB named Uncertainty and Disturbance Estimator (UDE) was utilized to solve the problem of inverter output voltage quality. UDE was initially proposed in [30], elaborated in [31] - [38] and applied to a variety of control tasks [39] - [48]. Its functionality is based on the assumption that appropriate filtering can approximate any continuous uncertainty and disturbance and then compensate it by opposite phase injection.…”

Section: Introductionmentioning

confidence: 99%

Uncertainty and Disturbance Estimator-Based Controller Equipped With a Time-Delayed Filter to Improve the Voltage Quality of Inverters

Gadelovits

Zhong

Kadirkamanathan

et al. 2019

IEEE Trans. Ind. Electron.

Self Cite

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“…So called time-delay systems (Zhong 2006;Wu et al 2010) include chemical processes, engines, manufacturing, micro-aerial vehicles andtelecommunication systems, etc. (Liu 2014;Sanz et al 2017). Timedelay is generally regarded as a main source of instability and poor performance in control systems (Li and deSouza 1997).…”

Section: Introductionmentioning

confidence: 99%

An Enhanced Smith Predictor Based Control System Using Feedback-feedforward Structure for Time-delay Processes

Mohammadzaheri

Tafreshi

2017

Jou. Eng. Res.

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This paper presents enhanced Smith predictor based control systems (SPBCSs) for processes with a time-varying or fixed time delay. This work focuses on improving the arrangement and asynchrony of SPBCS components rather than the design of the predictor and the feedback controller, which have been well discussed in the literature. The proposed control system advances SPBCS through implementation of two design recommendations: (i) replacing the classical feedback controller by a feedback-feedforward control system, and (ii) synchronizing the reference signal and the predicted output. As a result, common shortcomings of SPBCSs or control systems based on Pade approximation, i.e. the trade-off between performance and steady-state error, and instability associated with non-minimum-phase systems do not exist in the proposed SPBCS. The superior performance of the proposed control system is demonstrated with two examples: temperature control of an infrared dryer (a system with fixed time-delay) and air-fuel ratio of a lean-burn spark-ignition engine (a system with time-varying delay and lag). The proposed control system is shown to clearly outperform the conventional SPBCS and Internal Model Control (IMC) PID based on Pade approximation for aforementioned examples and performs satisfactorily in the presence of noises, actuator saturations, and severe model inaccuracies.

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Predictor-Based Control of a Class of Time-Delay Systems and Its Application to Quadrotors

Cited by 114 publications

References 37 publications

Identification and adaptive PID Control of a hexacopter UAV based on neural networks

Identification and adaptive PID Control of a hexacopter UAV based on neural networks

Uncertainty and Disturbance Estimator-Based Controller Equipped With a Time-Delayed Filter to Improve the Voltage Quality of Inverters

An Enhanced Smith Predictor Based Control System Using Feedback-feedforward Structure for Time-delay Processes

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