Researchers have done a lot of work [14-19] to compensate the nonlinearity and parameter uncertainty of **mechanical** **arm** dynamics using appropriate methods, so as to realize the accurate trajectory tracking of the **mechanical** **arm**. Reference [14] proposes a parameter recognition method for LuGre model on the basis of stable-state error analysis. **Friction** torque gets derived by stable-state error, and dynamic and static parameters are identified by a genetic algorithm. Experiment outcomes verify the efficiency of the identification approach. Reference [15] uses the improved genetic algorithm so as to recognize the dynamic parameters of the LuGre model, takes the **control** force as the target approximation value, and directly uses the displacement (or acceleration) and **control** torque output by the **friction** parameter identification system, which has the advantages of high speed, high identification accuracy and good robustness. Reference [16] proposes a new fast identification method for LuGre **friction** parameters. Solving LuGre's discrete recursion equation according to the speed input and **friction** output can identify dynamic and static parameters at one time with high identification accuracy. Because the bristle deformation in the LuGre **friction** model is difficult to be measured accurately, it is important to create a corresponding viewer for the sake of excluding it online, and then identify the parameters on this basis. In order to simplify the controller design, references [17-19] adopt **neural** **network** **control**, **fuzzy** **control** and other methods to compensate the **friction** link, and achieve a good **control** effect. However, there is little research on the LuGre **friction** in the field of **mechanical** **arm** **control**. Therefore, the introduction of LuGre model into a **mechanical** **arm** system can more truly simulate the **friction** link of the system, which is of great significance for a high precision **control** of the **mechanical** **arm**.

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In this paper, the dual **adaptive** controller is designed for the modeling error and **friction** interference of the manipulator. **Adaptive** **fuzzy** **control** is used to compensate the influence of **friction** on the system; the **adaptive** **control** learning algorithm **based** on the regression matrix to overcome the modeling error caused by the variation of robot model parameters. We provide the detailed **control** algorithm. Finally, the simulation results show the effectiveness of the proposed method

Abstract— The potential applications of dynamically substruc- tured systems (DSSs) with both numerical and physical substruc- tures can be found in diverse dynamics testing fields. In this paper, an **adaptive** feedforward controller **based** on a **neural** **network** (NN) is proposed to improve the DSS testing perfor- mance. To facilitate the NN **compensation** design, a modified DSS framework is developed so that the DSS **control** can be considered as a regulation problem with disturbance rejection. Then an **adaptive** NN feedforward **compensation** technique is proposed to cope with uncertainties and nonlinearities in the DSS physical substructure. The proposed NN technique generalizes the existing results in the literature, and it does not require any information of the plant model and disturbance model, which significantly simplifies its application on DSS. In particular, we propose a novel **adaptive** law for the NN online learning, where appropriate NN weight error information is derived and used to achieve improved performance. Real-time experimental results on a **mechanical** test rig demonstrate the improved performance by using the NN **compensation** strategy and the new adaptation law.

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The mutation and crossover rates are two important evolutionary parameters and are typically statically set through trial-and-error in classical evolutionary algorithms [7]. However, a parameter that is optimal during the initial stages of a search may not be effective in later stages of the evolutionary process [8]. Hence, adaptively tuning the parameters during a search process would enhance the convergence properties of the evolutionary algorithm and therefore should improve **control** performance. Pedrycz [9] states that the mutation rate and the crossover rate can be experimentally adjusted from results from a series of observations of past simulation and provides a method using **Fuzzy** meta-rules.

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A pair of ordinary differential equations represents the mathematical modeliig of the chemical reactor used in this simulation 14] ' By manipulating the coolant jacket temperature T,, tr[r]

There are many types of dc servo motors used in the industries in which rotor inertia is can be very small, and in this result, motors with very high torque – to – inertia ratios are commercially available. Servo systems are generally controlled by conventional Proportional – Integral – Derivative (PID) controllers, since they designed easily, have low cost, inexpensive maintenance and effectiveness. It is necessary to know system’s mathematical model or to make some experiments for tuning PID parameters. However, it has been known that conventional PID controllers generally do not work well for non-linear systems, and particularly complex and vague systems that have no precise mathematical models. To overcome these difficulties, various types of modified conventional PID controllers such as auto-tuning and **adaptive** PID controllers were developed lately. Also **Fuzzy** Logic Controller (FLC) can be used for this kind of problems. When compared to the conventional controller, the main advantage of **fuzzy** logic is that no mathematical modeling is required.

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Image compression technology refers to the method using as less as possible bits to represent the image signal from the signal source and possibly reducing such resource consumption occupied by the image data as the frequency bandwidth, storage space and transmission time etc., so as to transmit and store the image signals [1]. The main purpose of image compression is to eliminate the redundant information of images, including encoding redundancy, redundancy between pixels and psychological visual redundancy information [2]. In past decades, studies on the image compression have obtained the rapid development, and many effective algorithms have occurred,and compression standards such as JPEG, JPEG2000 and MPEG have been formed. In order to further compress images, we can start from two aspects: one is the use of vision system feature with the human eye as "final consumer" of the image information. The image compression **based** on human visual characteristics increasingly becomes the feature for people to study, and because of the complexity of the visual system, at present, there still exist a lot of unknown areas to be explored in this field. The second is the development of new compression tools and more intelligent algorithms. Owing to the excellent performance, there still exist a lot of room for the artificial **neural** **network** to be applied in the field of image compression [3].

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An **adaptive** **neural** **network** controller is brought forward by the paper to solve trajectory tracking problems of robotic manipulators with uncertainties. The first scheme consists of a PD feedback and a dynamic compensator which is composed by **neural** **network** controller and variable structure controller. Neutral **network** controller is designed to **adaptive** learn and compensate the unknown uncertainties, variable structure controller is designed to eliminate approach errors of neutral **network**. The **adaptive** weight learning algorithm of **neural** **network** is designed to ensure online real-time adjustment, offline learning phase is not need; Global asymptotic stability (GAS) of system base on Lyapunov theory is analysised to ensure the convergence of the algorithm. The simulation results show that the kind of the **control** scheme is effective and has good robustness.

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In order to analyze and study the **fuzzy** **control** model of smart city optimization **control**, this paper designs a fully networked **fuzzy** controller **based** on BP **neural** **network** arithmetic, so that the realization process of **fuzzy** reasoning is networked and clear. The evaluation mode of wisdom town is constructed, and the index system is simplified according to the evaluation index system of the develop possible of wisdom town. The analog outcome shows that the above arithmetic can availably optimize the parameters and structure of the neuro vagueness **control**, and the designed neuro vagueness **control** has good performance. The build of wisdom cities is advantageous to further promoting the deep integration of industrialization, informationization, urbanization, and agricultural modernization, which is of great significance for solving urban development problems and promoting continuable town develop. To improve the development capacity of intelligent city, we should highlight the charac- teristics of smart cities; advocate the concept of sustainable development and low-carbon development, market-oriented, overall planning, top-level design, and micro-regulation; and take the road of development with local characteristics.

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Regarding skidding and slipping as disturbances, Kang et al. [24] proposed a robust tracking approach using a generalized extended state observer at kinematic and dynamic level. Later in their work, Kang et al. [25] pro- poses a robust tracking controller **based** on the **fuzzy** disturbance observer for a WMR with unknown skid- ding and slipping. Subject to Wheel Slip, Tian et al. [26] developed a time-invariant discontinuous feedback law is to asymptotically stabilize the robot system. In order to perform trajectory tracking under the longitudinal slip condition, Gao et al. [27] proposed an improved **adaptive** controller together with a **neural** **network** learning proce- dure. When existing the skidding, slipping and input dis- turbance, Chen [28] proposed a robust tracking **control** scheme **based** on the disturbance observer for wheeled mobile robots. In Ref. [29], a reinforcement learning- **based** **adaptive** **neural** tracking algorithm is proposed for the nonlinear discrete-time dynamic system of the WMR with skidding and slipping. From all the research above, we can find that most of the work focus on the **control** scheme design, which is only validated by simulation, lacking of experimental validation.

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Underwater vehicles are being emphasized as highly integrated and intelligent devices for a significant number of oceanic operations. However, their precise operation is usually hindered by disturbances from a tether or manipulator because their propellers are unable to realize a stable suspension. A dynamic multi‑body model‑**based** **adaptive** con‑ troller was designed to allow the controller of the vehicle to observe and compensate for disturbances from a tether or manipulator. Disturbances, including those from a tether or manipulator, are deduced for the observation of the controller. An analysis of a tether disturbance covers the conditions of the surface, the underwater area, and the vehi‑ cle end point. Interactions between the vehicle and manipulator are mainly composed of coupling forces and restor‑ ing moments. To verify the robustness of the controller, path‑following experiments on a streamlined autonomous underwater vehicle experiencing various disturbances were conducted in Song Hua Lake in China. Furthermore, path‑following experiments for a tethered open frame remote operated vehicle were verified for accurate cruising with a controller and an observer, and vehicle and manipulator coordinate motion **control** during the simulation and experiments verified the effectiveness of the controller and observer for underwater operation. This study provides instructions for the **control** of an underwater vehicle experiencing disturbances from a tether or manipulator. Keywords: Underwater vehicle, **Adaptive** **control**, Disturbance observer, Multi‑body dynamic model

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The **control** scheme consists of the FWNN plant model, FWNN controller and the optimization block. Where r (t ) is desired output and y (t ) is the output of **control** system. In the proposed **control** strategy, **neural** **control** system synthesis is performed in the closed-loop **control** system and e (t ) is used for tuning **network** weighs to provide appropriate **control** input. By minimizing a quadratic measure of the error between desired system output and the output of **control** object, i.e. e (t ) , the design problem can be characterized by the GA formulation. On the other hand, the genetic algorithm is used to correct the **network** parameters for adjusting of FWNN controller and identifier in real time operation.

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In related research work, back-propagation (BP) NN was used for vibration **control** of a 9-DOF redundant modular manipulator [ 1 ]. A multi-layer NN controller, which did not need off-line learning, was designed to **control** rigid robotic arms [ 2 ]. A **fuzzy**- Gaussian NN controller was proposed for trajectory tracking **control** of mobile robots [ 3 ]. A dual NN was presented for the bi-criteria kinematic **control** of redundant manip- ulators [ 4 ]. A sliding mode **adaptive** NN controller was devised for **control** of mobile modular manipulators [ 5 ].

The deep deterministic policy gradient algorithm successfully solved position and orientation **control** of the robot using two different approaches. The three actors approach proved to be easier to tune, faster to converge, and better in performance versus the single actor case. Therefore, where possible, dividing a system by its orthogonal controls improves training convergence and hyper-parameter tuning and also allows partial retraining, a major advantage when obtaining experiences is costly. The author hypothesizes that these benefits are a function of system complexity where reduced complexity is desirable. Future work includes experiments to further demon- strate the efficacy of **control** division in other environments. Nevertheless, designers should consider the single actor method when processing speed is the limiting factor. In the context of Roborodentia, the trained **network** provides the **control** loop by which the robot gets to specific locations in the field. The programmer need only supply a series of coordinates defining the robot’s path and can expect the robot’s DDPG **network** to determine the requisite motor voltages and wheel speeds to follow it closely. Although the robot’s travel path to key Roborodentia field features are hard-coded, reinforcement learning could feasibly solve the pathfinding problem as well.

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All the data extracted from figure 4-13 is displayed in the table 4 above. By looking at table 4 and comparing them with the results of PD **control** technique we can observe that the settling time is improved from 12sec to 4sec while using **fuzzy** logic but we have to pay the cost of rise in amplitude which is around 1.12. There is also an overshoot of around 20% present in the roll angle. Settling time is also improved by 8 sec in the case of pitch angle but still there is an overshoot of 18% present and the settling amplitude is also same as roll which is 1.12. There is no overshoot present in the Yaw angle and settling time is also improved which is now 2.8sec as compared to 12 sec in PD **control** and Yaw angle is showing amplitude of 1.12. There is a very major difference in height response as compared to the PD **control** there are no notable undershoot present but there is an overshoot of 14% present which also diminish very quickly and improved settling time of 3.5 sec is observed in altitude response. Altitude response is showing amplitude of 1.17 which is better than 1.23 as compared to PD **control**.

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l Data-driven way to create fuzzy signatures l Self-determined fuzzy signatures number l Improve HE’s fuzzy signature neural. network[r]

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ABSTRACT: This research work focuses on implementation of novel Concurrency **Control** methods using Artificial **Neural** Networks (ANN) and **Fuzzy** Logic with applications to Computer Aided Design / Computer Aided Manufacturing (CAD/CAM) database that involves more and unspecified duration of time to complete a transaction. Most of the transactions in the CAD/CAM data base include drawing entities to form a complete manufacturing drawing, with processing and inventory details. In this research work, Artificial **Neural** **Network** method using Functional Update Back Propagation Algorithm (FUBPA) and Locally Weighted Projection Regression (LWPR) and **Fuzzy** Logic have been used for implementing Concurrency **Control** while developing product Bearing using Autodesk inventor 9. This work ensures that associated parts cannot be accessed by more than one person due to locking. The ANN and **Fuzzy** Logic learn the objects and the type of transactions to be done. During testing performance, metrices are analyzed.

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In the forward kinematics problem the end-effecter’s location in the Cartesian space (or work space), that is its position and orientation, is determined **based** on the joint variables. The joint variables are the angles between the links, in the case of rotational joints, or the link extension, in the case of prismatic joints. Conversely, given a desired end-effecter position and orientation, the inverse kinematics problem refers to finding the values of the joint variables that allow the manipulator to reach the given location.

In this paper, the repetitive tracking **control** problem of traffic flow systems of a single lane freeway with random bounded off-ramp traffic volumes is studied. We consider a more general case in the sense that the system nonlinearities and system parameters are allowed to be unknown. An **adaptive** **fuzzy** **neural** **network** (FNN) controller and an **adaptive** robust controller are applied to compensate for the unknown system nonlinearity and input gain respectively. On the other hand, to deal with the disturbance from random bounded off-ramp traffic volumes, a dead zone like auxiliary error with the time-varying boundary layer is introduced as a bounding parameter. This proposed auxiliary error is also utilized for the construction of **adaptive** laws without using the bound of the input gain for all the adaptation parameters. The traffic density tracking error is shown to converge along the axis of learning iteration to a residual set whose level of magnitude depends on the width of boundary layer.

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Motivated by the aforementioned analysis, the PSO algorithm is utilized to determine the connection weights of the CNNs, thus a novel CNN algorithm **based** on the PSO is proposed. In this case, we use the proposed algorithm to design an **adaptive** controller for the chaotic system. Since the convergence interval of Chebyshev basis function is in [-1, 1], an S-type function is introduced to extend its input range [ −∞ +∞ , ] , which