An improved nonlinear proportional-integral-differential controller combined with fractional operator and symbolic adaptation algorithmShi, Lezhen; Miao, Xiaodong; Wang, Hua
doi: 10.1177/0142331219879332pmid: N/A
Parameter adjustment is usually applied for designing the proportional-integral-differential (PID) controllers. However, the ability to improve control performance by adjusting parameters is limited. Hence, with the goal to achieve ideal closed-loop response, this paper takes advantage of a structural optimization method for modifying the controller model. A symbolic adaptation algorithm for fractional order PID (FOPID) controller is employed to obtain precise nonlinear controller model. Firstly, a modeling comparison for nonlinear duffing system is carried out to highlight the efficiency of the symbolic adaptation algorithm. The case study indicates the proposed algorithm can establish compact dynamic models by amending the shortcomings of symbolic regression. Secondly, the proposed controller is restructured with the linear FOPID controller and its nonlinearity is increased by adjusting controllers’ components in symbolic form. The proposed controllers are simulated in an unstable second-order system, a time-delay system and a nonlinear VanderPol system. Compared with the IOPID and the FOPID controller, the symbolic adaptation algorithm improves the structural flexibility of these linear controllers. Meanwhile, the system response can better approximate the desired response and the structural integrity of the nonlinear controller model is guaranteed simultaneously. Finally, the nonlinear FOPD controllers for trajectory tracking experiments are carried out on a rotary inverted pendulum control system.
Tracking control of multiple unmanned aerial vehicles incorporating disturbance observer and model predictive approachZhang, Boyang; Sun, Xiuxia; Liu, Shuguang; Deng, Xiongfeng
doi: 10.1177/0142331219879858pmid: N/A
This paper studies the disturbance observer-based model predictive control approach to deal with the unmanned aerial vehicle formation flight with unknown disturbances. The distributed control problem for a class of multiple unmanned aerial vehicle systems with reference trajectory tracking and disturbance rejection is formulated. Firstly, a local distributed controller is designed by using the model predictive control method to achieve stable tracking, where the local optimization problem is solved by an adaptive differential evolution algorithm. Then, a feedforward compensation controller is introduced by using a disturbance observer to estimate and compensate disturbances, and improve the ability of anti-interference. Besides, the stability of the proposed composite controller is analyzed as well. Finally, the simulation examples are provided to illustrate the validity of proposed control structure.
Fully distributed guidance laws for unmanned aerial vehicles formation flightWei, Xiaoqian; Yang, Jianying; Fan, Xiangru
doi: 10.1177/0142331219880048pmid: N/A
In this paper, three fully distributed guidance laws are designed for unmanned aerial vehicles formation flight, which have the following advantages. Adaptive technology in novel guidance laws can adapt to various graphs that only need one spanning tree. Cooperative formation does not need to set the virtual structure of formation in advance, but only needs to adjust the formation parameters in the guidance law to achieve the desired time-varying formation. This paper uses a guidance law perpendicular to the line of sight to make the flight trajectory more straight; hence, enhancing its applicability in real-world scenarios. These new guidance laws also enable group formation transformation and can optimize the unmanned aerial vehicles’ formation without global information to obtain the optimum performance of the formation. The simulation results show the practicability and effectiveness of the new method.
Adaptive iterative learning control for unknown linear time-varying continuous systemsAfsharnia, Fateme; Madady, Ali; Menhaj, Mohammad Bagher
doi: 10.1177/0142331219880110pmid: N/A
This paper presents a novel model reference adaptive iterative learning control (ILC) for unknown continuous-time linear time-varying systems. The unknown time-varying parameters of the system are neither required to vary slowly nor to have known bounds. The system is not required to be minimum-phase, stable, controllable or observable. The input of the system is determined by a differentiator-free control law. The used reference model is time-invariant and first order and thus choosing its parameters is easily possible, even though, the system under control is high order and time variant. Almost all of the components of the system initial condition can be iteration variant. By introducing a novel kind of Lyapunov function the convergence of the proposed adaptive ILC (AILC) and achieving asymptotic tracking are proved. Also, by rigorous mathematical analysis and with the help of some mathematical key techniques such as Bellman-Gronwall lemma, it is shown that all signals and quantities in the closed-loop system are bounded in the sense of at least one norm. Finally, the effectiveness of the proposed method is verified by two simulation examples.
An adaptive second-order sliding-mode observer for permanent magnet synchronous motor with an improved phase-locked loop structure considering speed reverseZhan, Yuan; Guan, Jifu; Zhao, Yufeng
doi: 10.1177/0142331219880712pmid: N/A
An adaptive second-order sliding-mode observer based on Super-Twisting Algorithm (STA-SMO) to estimate rotor position and speed of permanent magnet synchronous motor (PMSM) is proposed in this paper. The advantages of the proposed observer algorithm are reflected in small chattering, high tracking accuracy, good robustness to parameters change and external disturbance in wide positive and reversal speed range. The Lyapunov stability of the system is proved. A new perturbation term form is employed and according to the stable condition of STA-SMO, the adaptive sliding-mode coefficients related to absolute value of estimated speed are deduced to guarantee the performance mentioned above in wide positive and negative speed range. In order to extract rotor position and speed in both speed range from the adaptive STA-SMO, an improved conventional phase-locked loop (IPLL), which is more accurate, is studied and its non-linear dynamics are analyzed in detail to prove the effectiveness of the IPLL theoretically. In the end, the effectiveness of the adopted adaptive second-order sliding-mode observer with IPLL structure is verified through simulations. Simulation results show that the position error and chattering of the proposed adaptive observer are decreased more than 25% and 50% compared with conventional SMO and STA-SMO in wide positive and negative speed range. Meanwhile, the proposed system still has good estimation performance and strong robustness with torque and parameters variation.
Phase division and transition modeling based on the dominant phase identification for multiphase batch process quality predictionTang, Xiaochu; Li, Yuan
doi: 10.1177/0142331219881343pmid: N/A
Batch processes are carried out from one steady phase to another one, which may have multiphase and transitions. Modeling in transitions besides in the steady phases should also be taken into consideration for quality prediction. In this paper, a quality prediction strategy is proposed for multiphase batch processes. First, a new repeatability factor is introduced to divide batch process into different steady phases and transitions. Then, the different local cumulative models that considered the cumulative effect of process variables on quality are established for steady phases and transitions. Compared with the reported modeling methods in transitions, a novel just-in-time model can be established based on the dominant phase identification. The proposed method can not only consider the dynamic characteristic in the transition but also improve the accuracy and the efficiency of transitional models. Finally, online quality prediction is performed by accumulating the prediction results from different phases and transitions. The effectiveness of the proposed method is demonstrated by penicillin fermentation process.
Modeling and implementation of 1-D inverted magnetic needle system using a robust sliding mode controllerSuresh Kumar, Pakala; Priyadarshan, Hari; Harsha Simha, MS
doi: 10.1177/0142331219881538pmid: N/A
In this paper, we propose a novel problem in control systems area involving the control of a magnetic needle in the presence of an external magnetic field. A magnetic needle when restricted to rotate about a single axis in an external magnetic field, by pivoting its center will produce a stable and unstable equilibrium. Here, we present the detailed mathematical modeling of the 1-D inverted magnetic needle system and its control in the unstable equilibrium point. We use sliding mode controller (SMC) to achieve the control objectives. The simulation results are validated with the experimental results. For achieving a close match, we consider sensor and actuator nonlinearities. Further, its robust performance is compared with proportional-derivative (PD), proportional-integral-derivative (PID) controllers in the presence of system parameter uncertainty, disturbance, and sensor delay. We also study the effect of change in SMC parameters, proportional and derivative gains on the system performance. It is to be noted that the proposed experimental setup can be extended to a much more general and complex system, both in modeling as well as control design leading to a new benchmark problem in the control system.
Stabilization of nonlinear vibrations of carbon nanotubes using observer-based terminal sliding mode controlYousefpour, Amin; Vahidi-Moghaddam, Amin; Rajaei, Arman; Ayati, Moosa
doi: 10.1177/0142331219881547pmid: N/A
This article is concerned with suppression of nonlinear forced vibration of a single-wall carbon nanotube conveying fluid based on the nonlocal elasticity theory and Euler–Bernoulli beam theory. Electrostatic actuation is considered as the control force for the suppression of carbon nanotube. Based on Galerkin approach, the governing nonlinear partial differential equation is reduced to an ordinary one. Since the sliding mode controller (SMC) does not assures finite time system stabilization and also causes chattering in the control input and consequently vibration in the system, terminal sliding mode controller (TSMC) is developed for the stabilization of carbon nanotube based on a disturbance observer. TSMC and disturbance observer suppress the vibrations of nanotube in the presence of external disturbances caused by the internal flow. Numerical simulation results are presented to illustrate the effectiveness and performance of the proposed control scheme in comparison to similar approaches. Simulation results show that the proposed control method successfully stabilizes the uncertain system in a finite time.