In practical **controller** **design**, stability and performance objec- tives should be considered to reach optimal control performance that can meet requirements of real world control applications. Essentially, optimal tuning problems of controllers can be reduced to an attempt to determining the best **controller** coefficients in a set of stabilizing **controller** options. For this **design** strategy, the stabilization objective turns into a principal component of optimal **controller** **design** tasks. Optimal **controller** **design** efforts have widely intensified in two **design** domains: (i) frequency **domain** methods, for instance, phase margin objective of loop shaping techniques is used to ensure system stability [11–13] and (ii) time **domain** methods, for instance, sum of square of control error is used to obtain a stable system response [36,37] . **Design** opportuni- ties of these two domains are largely exploited in numerous con- troller **design** works, and a fresh **design** **domain** can open up new **design** options. In this manner, the current study presents a **design** framework for exploitation of the first Riemann sheet of conformal mapping s ¼ **v** m , which is called **v** -**domain**, for optimal fractional

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In terms of technologies, the new principles of **disturbance** **rejection**, as applied to address critical needs in every industry sectors, will likely lead to brand new control technologies. The new motion control technology based ADRC is but one example [28]. Finally, in terms of applications, the **domain** experts, once brought up to speed with the new **design** concepts, will see the fundamental change in not just how control is designed, but also how systems and solutions are conceived. Shinskey [1] was right in that this advanced concept of **controller** – rejector needs to be communicated to those at the front line of production; the hundred-fold improvement he observed in the past and the over 50% energy saving obtained recently are testaments of the power of such ideas, as do the applications summarized in [26]. To this end, the centrality of **disturbance** **rejection** and the objective of **disturbance**-free control should be ﬁ rmly established in all future application researches. To answer the earlier question, these are the ideas and ideals that should be “ applied ” in future research in automatic control.

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For the multi-input and multi-output, strong-coupling nonlinear features of coordinated system for thermal power unit, it is difficult for traditional PID coordinated control scheme to meet the power grid demand which often participates in peak regulation and frequency modulation. In this paper, inverse Nyquist array is employed to carry out frequency **domain** analysis of the plant model. Then Pseudo- diagonalization is used to **design** the static decoupling compensation matrix of the system. Above on these, the linear active **disturbance** **rejection** **controller** ofevery channel in coordinated system can be designed repectively. Dynamic coupling and system unknown parts are observed by extended state observerof ADRC and is compensated to thesystem in time. The simulation tests show that the **disturbance** **rejection** results of the load and the main steam pressure for the coordinated control system under LADRC is better than that of PID control.

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For small UAVs, however, wind gusts present a considerably different challenge. The significant reduction in size leads to lower inertia, making small UAVs more sensitive to **disturbance** [5]. Moreover, a reduction in aircraft size is generally accompa- nied by a reduction in operating airspeed. This has reached a critical point for small UAVs where their operating airspeeds are of the same magnitude as the gust disturbances they are subjected to [6]. As a consequence, in gust alleviation for small UAVs, structural loads are less critical condition than flight performance. This presents a different problem of **disturbance** **rejection**, with small UAVs having very different considerations. Flight control can be gener- alised into two categories; outer loop trajectory control and inner loop attitude control. UAV trajectory track- ing in wind has been studied in literature, with a range of methods applied. Vector field guidance has demonstrated robustness to wind **disturbance** by util- ising ground speed and course for navigation [7]. It was shown that path planning with a known constant wind can improve mission accuracy and efficiency [8]. By using pre-computed information of aircraft turn- ing performance in wind, it has also been shown that path following in wind can be improved [9]. These methods demonstrate that using robust methods is feasible for trajectory tracking in wind. Using infor- mation of the wind improves performance further. The limitation being that accurate prior wind knowledge is not feasible, especially for gust disturbances. As small UAVs are highly affected by gusts, it should be considered in their operation. It has been shown that online estimation of steady wind can be obtained and used in trajectory following, with some ability to track variance [10]. **Disturbance** Observer Based Control (DOBC) augmentation has shown good per- formance in simulation and flight testing in rejecting **disturbance** of an unknown wind in trajectory track- ing [11]. Inner loop control for UAVs is also widely studied in literature. However, work regarding distur- bance **rejection** in this area is more sparse, particularly regarding external disturbances. **Disturbance** **rejection** for parameter uncertainty has been addressed by var- ious approaches including robust methods [12], Neu- ral Networks (NNs) [13], Support Vector Regression (SVR) [14] and Active **Disturbance** **Rejection** Control (ADRC) [15]. While these works were able to account

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Abstract: This paper proposes an innovative approach for controlling pollutant release in a long-distance tunnel via longitudinal ventilation. Enhanced by an active **disturbance** **rejection** control (ADRC) method, a ventilation **controller** is developed to regulate the forced air ventilation in a road tunnel. As a result, the pollutants (particulate matter and carbon monoxide) are reduced by actively regulating the air flow rate through the tunnel. The key contribution of this study lies in the development of an extended state observer that can track the system **disturbance** and provide the system with compensation via a nonlinear state feedback **controller** equipped by the ADRC. The proposed method enhances the **disturbance** attenuation capability in the ventilation system and keeps the pollutant concentration within the legitimate limit in the tunnel. In addition to providing a safe and clean environment for passengers, the improved tunnel ventilation can also achieve better energy saving as the air flow rate is optimized.

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Tables 3-5 are for the case when the shoulder endpoint acceleration is negative, and Tables 7-9 are for the case when the shoulder endpoint acceleration is positive. The central portion of the rule base where makes use of the entire output universe of discourse. This is part of the rule base where the acceleration input from the shoulder link endpoint is zero or small. As we move away from the center of the rule base (to the region where the shoulder link endpoint acceleration is large), only a small portion of the output universe of discourse is used to keep the output of the **controller** small.

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In this paper, a mathematical analysis was done on an RC closed-loop system. Due to the delay loop in the RC structure, for simplicity, the Pade approximation was used. In the worst case, the plant was considered unity as a full pass filter. When the period is known, the effect of RC gain and the conditions of the output system response were studied analytically. The performance was very good. It was shown that there is no need to make limitation on the amplitude of periodic **disturbance**. In other words, if the amplitude varies, the steady-state response which depends on RC gain decreases and the **disturbance** is rejected. The steady-state response was proportional to 20 log k . It was shown that the output system response is almost between 0 . 1 which is about -20dB attenuation. A novelty of this paper was adaptive FFT algorithm. An adaptive FFT algorithm was proposed for periodic **disturbance** **rejection** in different known and unknown and time-varying scenarios, and the period estimation was done accordingly.

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In view of the strong nonlinear and coupling characteristics of the airdrop operations, a novel control method is proposed based on the active **disturbance** **rejection** control (ADRC) for decoupling control strategy of the pitch angle and airspeed. The unknown disturbances, including aerodynamic uncertainty and nonlinear coupling effect between the aircraft and cargo dynamics, are estimated and compensated with the extended state observer (ESO). Meanwhile, the nonlinear law state error feedback (NLSEF) is adopted to restrain the compensation residual. Simulation and flight quality evaluation shows the satisfactory capacity and strong robustness of the proposed control method in guaranteeing the airdrop task and flight safety.

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HVAC (heating, ventilation, and air conditioning, HVAC) is a typical complex system with non-linear, strong coupling and strong **disturbance** influence. The study on HVAC system is significant for the energy efficiency in buildings, and its control strategies are the major concern [1] [2]. Generally, PID control me- thod is adapted in the temperature control of the zone in traditional HVAC sys- tem. The overshooting of the temperature often appears. When the special sys- tem has high requirement for the thermal and humidity of the zone, the over- How to cite this paper: Huang, C.-E., Li,

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rithm, we apply the PSO optimizer to search for a winner point in each sequential EI iteration. For setting the PSO parameters, the maximum iteration number is fixed 200 and the swarm is initialized with 30 particles. Notably, as we use GP surrogate instead of the true (original) sim- ulation model as an objective function in PSO, thus we don’t worry about the computational cost due to running of a true simulation model. At the end of any sequential EI iteration, the program checks the stopping rule(s). Here, we stop the EI procedure when the EI criterion becomes smaller than 0.01, or the number of sequential runs reaches 15 iterations. Also, at the end of any sequential EI iteration, two terms of the program are updated, i) the set of training sample points by adding a winner point and relevant SNR output that is computed accordingly from the original simulation model, ii) the best sample point obtained so far with the smallest SNR among all the training points and updating points. Moreover, according to an updated set of training samples, a new GP surrogate is constructed after each sequential EI iteration. It is important to note that we avoid extrapolation of GP surrogate in each sequential iteration by setting two different rules, i) we consider a death penalty for any point that is investigated by PSO and is located out of bounds of training points, ii) to estimate GP prediction error using jackknife leave-one-out approach, we only remove input combination’s rows that don’t locate on the margin of **design** space (see Section 3.1.5). The obtained results from the pro- posed algorithm and relevant sensitivity analysis are discussed in the following sections.

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Remark 3.2 Recently, many scholars have studied diﬀerent kinds of control theories about MRNNS such as exponential synchronization control [12], ﬁnite-time synchronization control [13], exponential lag adaptive synchronization control [18], lag synchronization control [19], and so on. However, to the best of our knowledge, there has not been any paper to discuss the **disturbance** attenuating **controller** **design** for MRNNs. This paper is the ﬁrst one to investigate the **disturbance** attenuating **controller** for MRNNs, which is sure to strengthen the systematic research theory for MRNNs and must further enrich the basis of application for MRNNs.

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Recently, a high-order SMC technique has been employed in reentry attitude control. Sliding mode **disturbance** observer was used. Also, it provides a good robustness to external disturbances, model uncertainties, and different mission trajectories. But the drawback is that attitude tracking is achieved within an infinite time rather than finite time. Slow convergence rate and poor **disturbance** **rejection** performance. But, when NDI is used along with sliding mode control, advantages are increased [5]. Limited time convergence, robustness, easy implementation are some main advantages.

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The sufficient operation of inter connected power system requires matching of total load demand associated with the losses. The operating point of power - system must be stable for the efficient working of the power system. Otherwise Power system experiences deviations in normal system frequency and scheduled tie line power exchanges to the other areas, which may cause undesirable effects [1]. There are two variables of interest in power system, namely frequency and tie-line power exchange. The combination of the variations in both parameters is called area **controller** error. Automatic generation **controller** **design** strategies have become an emerged area of research. The main aim of automatic generation control (AGC) is to control the mismatches in the system parameters at abnormal conditions. So many investigations and efforts have been carried out to **design** an optimal automatic generation **controller** to enhance stability and security of the system. Dynamic performance of all the conventional classical Integer Order (IO) controllers [2] like Integral, Proportional plus Integral , Proportional plus Integral plus Derivative controllers etc. has been reported in the Areas of AGC [3]-

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Optimization also termed as augmentation is the procedure of creating the things more unblemished, potent and dynamic in order to acquiesce the best result. The various techniques of optimization are Nelder-Mead, Active-Set, Interior-Point, SQP (sequential quadratic programming) et cetera. Numerically it can be explained as the procedure of expanding and shrinking of the endeavor capacity relying upon various conclusion variables under a deal of restrictions. The optimization technique has been used so as to discover and attain the finest results so as to **design** the most accurate **FOPID** **controller** that yields the finest output and assists the plant to augment its performance.

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By selecting Computer Monitor (the default option), the system is enabled to monitor activity on Atten- dant Access, Telephone Interface, and Night Bell inputs. This feature requires that a PC computer be connected to the RS-232 port of the **Controller**. Whenever the input becomes active, ASCII characters will be sent out the RS-232 port (DB9 pin connector) to the computer. The ASCII characters will be inter- cepted by a special software package in the computer that logs the time, date, input zone, type of activ- ity, zone that was paged, and duration of the activity. All such input activity to the paging system can then be viewed (and recorded) on the computer.

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Based on a thing that it is difficult to choose the parameters of active **disturbance** **rejection** control for the non-linear ALSTOM gasifier, multi-objective optimization algorithm is applied in the choose of parameters. Simulation results show that performance tests in load change and coal quality change achieve better dynamic responses and larger scales of rejecting coal quality disturbances. The study provides an alternative to choose parameters for other control schemes of the ALSTOM gasifier.

In this chapter, we begin with a review of the background of industrial control technology and its evolution. Control plays a vital and independent role in the engineering and sciences. Early inventions of automatic control can be traced back as far as the “South Pointing Chariot” [1] of China in 900 B.C. , shows in Fig.1.1. It is an ingenious solution to the problem of angle preserva- tion, where the figure of the wooden figure on top of the chariot always points south, its starting direction, no matter how the chariot moves and turns. The **design** principle of this famous invention from ancient China escaped the grasp of human being for thousands of years and it is a vivid rebuke of the common notion that technology is invented from applications of theories. Similarly, control theory did not have anything to do with the invention of the flyball governor, which brought us the Industrial Revolution and modern life style. In fact, the further developments of control technology in the 19th and 20th centuries proceeded without much contribution of control theory, covering a wide spectrum of modern industry, from manufacturing to aerospace and aeronautics, and so on. A particular sector of industrial control is process control, with which this thesis is concerned.

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When the feedback systems were being formed then the outputs demonstrated that the system now consists the properties of less power consumption, less time to heat the substances along with less overshoot. Earlier the integer order model had the settling time (time taken to heat the substance), steady state error (power consumption) and overshoot (explosion) of 1500 seconds, 50% and 0% respectively. When the PID **controller** was designed for the same using Cohen-Coon tuning technique and forming a feedback system it had setting time of around 800 sec. and also the steady state error was brought to 0% but the overshoot went up to 35%. Therefore **FOPID** **controller** is being designed using the concocted technique that is the amalgamation of tuning technique and optimization techniques and forming and feedback system with FOM of heating furnace, the system yielded steady state error as 0%, where the settling time have been reduced to 300 seconds and overshoot between 7%-12%.

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Hoogendijk et al. [2010] to compute the frequency response of a **controller** that achieves a desired closed-loop pole location. In Keel and Bhattacharyya [2008], a complete set of PID controllers is computed that guarantee a gain margin, phase margin and H∞ performance speciﬁcation using frequency-**domain** data. This method is extended to **design** of ﬁxed-order linearly parameterized controllers in Parastvand and Khosrowjerdi [2014]. A data-driven synthesis methodology for ﬁxed structure **controller** **design** problem with H∞ performance is presented in Den Hamer et al. [2009]. This method uses the Q parameterization in the frequency **domain** and solves a non-convex optimiza- tion problem to ﬁnd a local optimum. Another frequency- **domain** approach is presented in Khadraoui et al. [2013] to **design** reduced order controllers with guaranteed bounded error on the diﬀerence between the desired and achieved magnitude of closed-loop sensitivity functions. This ap- proach also uses a non-convex optimization method. Convex optimization is used in Karimi and Galdos [2010] to compute robust H ∞ controllers for SISO systems rep- resented by their frequency response. The H∞ robust performance constraints are convexiﬁed for linearly pa- rameterized controllers with the help of a desired open loop transfer function. Based on this method, a public **domain** toolbox for Matlab is developed which is available in Karimi [2013]. This approach is extended to compute decoupling controllers for MIMO systems in Galdos et al. [2010].

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