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Switching control of a long-stroke precision stage based on model prediction
Published in Artde D.K.T. Lam, Stephen D. Prior, Siu-Tsen Shen, Sheng-Joue Young, Liang-Wen Ji, Engineering Innovation and Design, 2019
First, advanced control schemes were frequently applied to solve the problems caused by PZT nonlinearities. For example, Bouc-Wen model was applied to compensate the hysteresis nonlinearity in piezoelectric actuators with an inverse multiplicative scheme. (Rakotondrabe, 2011) Because it is difficult to use nonlinear models for control design, we regard the PZT stage as linear model with nonlinear properties as system uncertainties. Then robust control can be applied to guarantee system stability and performance. Furthermore, because a single controller cannot satisfy all performance requirement at all conditions, controller switching algorithms were proposed to adjust the controllers according to the system situations. For example, a mode-switching control with initial value compensation was presented to improve the transient responses in terms of settling time and overshoot. (Yamaguchi, et al., 1996) Gain-scheduling integral control was applied to a PZT stage for nano-positioning with a travel range of 100 μm × 100 μm. (Wang, et al., 2017) This paper extends these ideas by proposing an automatic control switching mechanism based on model prediction. (Clarke, et al., 1987) Two robust controllers were designed for the PZT stage: one gave fast but oscillatory responses while the other one gave smooth but slow responses. The penalty/cost by different controllers in limited horizons was estimated, so that at each step the controller was switched to the best controller that minimized the penalty/cost.
Actuator Optimization and Fuzzy Control in Mechatronics
Published in C.W. de Silva, Mechatronic Systems, 2007
Standard control schemes and the use of compensators can achieve the desired performance. However, in the presence of uncertainties, a robust control scheme is needed to assure system performance. Classical control design techniques may achieve reliable performance to a certain degree; however, there are a number of robust control design methods such as root locus, frequency response, and PID control that can provide desirable performance in the absence of large disturbances and errors. Among these methods, the PID control scheme provides a popular robust control design approach that has functional simplicity. In designing a PID controller, three control parameters—proportional gain Kp, integral gain KI, and derivative gain KD have to be determined, because the PID control law in the time domain is given by f(t)=Kp.e(t)+kI∫t0te(t).dt+kD.TD.ddte(t)
Advanced Control Systems
Published in Arthur G.O. Mutambara, Design and Analysis of Control Systems, 2017
Robust control is an approach to handling model uncertainties associated with time varying systems, both linear and nonlinear. This strategy happens to be a special case of adaptive control, the robust. The goal of robust systems design is to retain assurance of system performance in spite of model inaccuracies and changes. A system is robust when it has acceptable changes in performance due to model changes or inaccuracies. A robust control system exhibits the desired performance despite the presence of significant plant (process) uncertainty.
Undiscounted reinforcement learning for infinite-time optimal output tracking and disturbance rejection of discrete-time LTI systems with unknown dynamics
Published in International Journal of Systems Science, 2023
Ali Amirparast, S. Kamal Hosseini Sani
In this paper, an undiscounted optimal control structure is proposed to solve the infinite-time linear quadratic tracking problem. The proposed optimal control scheme guarantees the damping of oscillation in the transient state and tracking of the reference in the steady-state in presence of external process disturbance. The stability of the proposed closed-loop system is analysed via the Lyapunov approach. As a novelty in model-based RL algorithms, a model predictive RL algorithm is proposed to reduce the number of iterations in the learning phase. The solution of the infinite time LQT problem for the closed-loop system with unknown dynamics is achieved via model-free RL algorithm. The proposed model-free optimal controller uses least square technique to obtain optimal control for tracking the reference. In addition, the effect of initialisation of model-free algorithm on the transient response of the system is investigated. The linear discrete-time model of F16 aircraft and a grid-connected inverter are considered as the case studies and the simulation results reveal the advantages of the proposed controller in dealing with parameter changes and disturbance rejection. An interesting avenue for future research in this field is extending the proposed control method to solve the robust control problem without requiring any knowledge about system dynamics.
Robust control of isopropyl benzene production process using H ∞ loop shaping control scheme
Published in Journal of Control and Decision, 2022
Vinila Mundakkal Lakshmanan, Aparna Kallingal, Sreepriya Sreekumar
An optimum design of the concentration regulation loop of the reactor in the cumene production process is proposed in this article using robust control approach (Vinila et al., 2019). To obtain the trade-off between robustness and stability, this approach is based on the application of control with loop shaping method (McFarlane & Glover, 1992). The final controller is then formulated to PID form (Doyle et al., 1988). This strategy retains all the advantages of the existing control strategy and loop shaping included in the model proposes a new tuning parameter which directly influences the response of the system. This design parameter can be used to obtain the PID form, where it directly influences the stability and robustness of the system (Figueroa et al., 1993). The time domain performance of the feedback system is also affected by the design parameter. This methodology is interpreted as optimal control based on PID tuning. Robust control is one of the most important aspects of modern control design (Doyle et al., 1989; Zames & Francis, 1983). The major role of robust control is to keep the feedback control loop stable and to achieve required control performance in the presence of uncertainty in the system (Vasičkanmová et al., 2015).
Robust Passive Fault Tolerant Control for Air Fuel Ratio Control of Internal Combustion Gasoline Engine for Sensor and Actuator Faults
Published in IETE Journal of Research, 2023
Arslan Ahmed Amin, Khalid Mahmood-ul-Hasan
Robust control is a control system design approach in which systems can handle disturbances and uncertainties without losing stability provided the uncertainties and disturbances are within specified bounds. Robust control systems are static rather than dynamic and do not change their states as in case of adaptive control systems. For example, a high gain feedback system is a robust control system as due to this high gain, effect of variation in any other parameter would be negligible. However, the system must maintain stability in case of high gain. Another example of robust control is SMC which is a variable structure control and causes sliding of the system states around the desired trajectory by applying discontinuous control signal [25–27].