Earticle

The learning control develops controllers that learn to improve their performance at executing a given task, based on experience performing this specific task. In a previous work, the authors presented an iterative precision of linear decentralized learning control based on p-integrated learning method for the vertical dynamic multiple systems. This paper develops an indirect decentralized learning control based on adaptive control method. The original motivation of the learning control field was learning in robots doing repetitive tasks such as an assembly line works. This paper starts with decentralized discrete time systems, and progresses to the robot application, modeling the robot as a time varying linear system in the neighborhood of the nominal trajectory, and using the usual robot controllers that are decentralized, treating each link as if it is independent of any coupling with other links. Some techniques will show up in the numerical simulation for vertical dynamic robot. The methods of learning system are shown for the iterative precision of each link.

The learning control develops controllers that learn to improve their performance at executing a given task, based on experience performing this specific task. In a previous work, the authors presented an iterative precision of linear decentralized learning control based on p-integrated learning method for the vertical dynamic multiple systems. This paper develops an indirect decentralized teaming control based on adaptive control method. The original motivation of the teaming control field was loaming in robots doing repetitive tasks such as on an assembly line. This paper starts with decentralized discrete time systems, and progresses to the robot application, modeling the robot as a time varying linear system in the neighborhood of the nominal trajectory, and using the usual robot controllers that are decentralized, treating each link as if it is independent of any coupling with other links. Some techniques will show up in the numerical simulation for vertical dynamic robot. The methods of learning system are shown up for the iterative precision of each link.

In order to improve the problem of premature convergence and computational efficiency of traditional differential evolution algorithm in solving high-dimensional problems, an improved differential evolution (HMSDE) algorithm based on combing elite synergy strategy, multi-population strategy and dynamic adaptive strategy is proposed in this paper. In the proposed HMSDE algorithm, the population is dynamically divided into multi-populations in order to keep the diversity of the population, elite synergy strategy is used to achieve information exchange among different sub-populations, and dynamic adaptive strategy is used to dynamically control the parameter values of scaling factor and crossover factor in order to improve the stability and robustness of the HMSDE algorithm. In order to test the performance of the HMSDE algorithm, a set of 10 benchmark functions are selected in here. The results show that the HMSDE algorithm takes on remarkable optimized ability, faster convergence speed and higher search accuracy. And the HMSDE algorithm can avoid the premature convergence and outperforms several state-of-the-art performances.

Neural networks are known as kinds of intelligent strategies since they have learning capability. There are various their applications from intelligent control fields, however, their applications have limits from the point that the stability of the intelligent control systems is not usually guaranteed. In this paper we propose an Adaptive Tracking Control of Nonlinear System the radial basis function network that is a kind of neural networks. The learning method involves structural adaptation and parameter adaptation. No prior knowledge of the plant is assumed, and the controller has to begin with exploration of the state space. The exploration versus exploitation dilemma of reinforcement learning is solved through smooth transitions between the two modes. The controller is capable of asymptotically approaching the desired reference trajectory, which is showed in simulation result.

To solve the slow convergence speed, low precision in later period and tedious parameter setting of differential evolution when applied to complex optimization functions, an improved differential evolution algorithm (dn-DADE) based on dynamic adaptive strategy is proposed. Firstly, the elite solutions of current population are utilized in the new mutation strategy (DE/current-to-dnbest/1) to guide the search direction, and then these optional elite solutions tend to the global optimal solution in the late stage of evolution to balance the diversity of population and convergence speed. Secondly, the adaptive update strategies of scaling factor and crossover factor are designed for control parameter values self-adapting at different search stages, thus improve the stability and robustness of the algorithm. A set of 14 benchmark functions is adopted to test the performance of the proposed algorithm. The results show that dn-DADE algorithm has the advantages of remarkable optimizing ability, higher search precision, faster convergence speed and outperforms several state-of-the-art improved differential evolution algorithms in terms of the main performance indexes.

The appropriate choice of crossover and mutation rates is critical to the success of genetic algorithms. Earlier researches focused on finding optimal non-dynamic crossover or mutation rates, which vary for different problems, and different stages of the genetic process in a problem. This paper proposed an ameliorate adaptive genetic algorithm where the mutation and crossover rates are adapted dynamically based on the evaluation results of the respective offspring in the next generation. Simultaneously, we combined this new algorithm with PD controllers to improve the stability of control systems. The experimental results are compared with other researchers’ approaches in this field. The PD-GA method can also significantly accelerate the system response and induce lower overshoot as well.

The study on nonlinear control system has received great interest from the international research field of automatic engineering. There are currently some alternative and complementary methods used to predict the behavior of nonlinear systems and design nonlinear control systems. Among them, characteristic modeling (CM) and fuzzy dynamic modeling are two effective methods. However, there are also some deficiencies in dealing with complex nonlinear system. In order to overcome the deficiencies, a novel intelligent modeling method is proposed by combining fuzzy dynamic modeling and characteristic modeling methods. Meanwhile, the proposed method also introduces the low-level learning power of neural network into the fuzzy logic system to implement parameters identification. This novel method is called neuro-fuzzy dynamic characteristic modeling (NFDCM). The neuro-fuzzy dynamic characteristic model based overall fuzzy control law is also discussed. Meanwhile the local adaptive controller is designed through the golden section adaptive control law and feedforward control law. In addition, the stability condition for the proposed closed-loop control system is briefly analyzed. The proposed approach has been shown to be effective via an example.

This paper proposes an adaptive dynamic surface control (DSC) approach for disturbance attenuation of uncertain nonlinear systems in the parametric strict-feedback form. In the proposed control system, a smooth projection algorithm is employed to train uncertain parameters. The proposed DSC system can overcome the complexity of an actual controller caused by the recursive differentiation of virtual controllers and parameter adaptation laws in the backstepping design procedure. From Lyapunov stability analysis, it is shown that the proposed controller has $H_{\infty}$ tracking performance to attenuate external disturbances.

This paper studies the robust adaptive dynamic surface control of a class of nonlinear systems with unmatched uncertainties. The unmatched uncertainties consist of not only the linearly parameterized terms but also the nonlinearly parameterized terms. The bound of each nonlinearly parameterized uncertainty term is supposed to be expressed by a known nonnegative function multiplied by a constant called bound parameter. According to whether the bound parameters are known or not, two different kinds of robust adaptive dynamic surface control algorithms are proposed. It is proved that in each case all the states of the closed-loop system are kept uniformly ultimately bounded, and the output is driven to track a feasible desired output trajectory with an arbitrarily small error. An example is also employed to indicate the effect of the proposed methods.

In this paper, an adaptive controller with structurally dynamic wavelet network is developed for a harmonic drive subject to parameter varying friction. The control architecture integrates a proportional controller, a feedback adaptive component and sliding component to adaptively compensate for the friction to achieve accurate position tracking. Global asymptotic stability of the algorithm is proved by using Lyapunov function. In parallel to the adaptive controller, a fuzzy reconfiguration scheme is devised to change the structure of the network along with weights updating to improve the system tracking performance and robustness. Experimental tests on a harmonic drive manipulator verify the effectiveness of the proposed control method.

In this paper, a novel estimation technique for a robust adaptive control scheme is presented for a class of uncertain nonlinear systems with a general set of uncertainty. For a class of introduced more extended semi-strict feedback forms which generalize the systems studied in recent years, a novel estimation technique is proposed to estimate the states of the fully nonlinear unmodeled dynamics without stringent conditions. With the introduction of powerful functions, the estimation error can be tuned to a desired small region around the origin via the estimator parameters. In addition, with some effective functions, a modified adaptive backstepping for dynamic uncertainties is presented to drive the output to an arbitrarily small region around the origin by an appropriate choice of the design parameters. With our proposed schemes, we can remove or relax the assumptions of the existing results.

This paper proposes an adaptive control method of partially known system and shows its application result to control for two-wheeled WMR. The controlled system is stable in the sense of Lyapunov stability. To design a tracking controller for welding path reference, an error configuration is defined and the controller is designed to drive the error to zero as fast as desired. Moments of inertia of system are considered to be unknown system parameters. Their values are estimated using update laws in adaptive control scheme. The effectiveness of the proposed controller is shown through simulation results.

This paper considers the problem of adaptive fault-tolerant guaranteed cost controller design via dynamic output feedback for a class of linear time-delay systems against actuator faults. A new variable gain controller is established, whose gains are tuned by the designed adaptive laws. More relaxed sufficient conditions are derived in terms of linear matrix inequalities (LMIs), compared with the corresponding fault-tolerant controller with fixed gains. A real application example about river pollution process is presented to show the effectiveness of the proposed method.

This paper addresses dynamic modeling and task-space trajectory following issues for nonholonomic mobile manipulators moving on a slope. An integrated dynamic modeling method is proposed considering nonholonomic constraints and interactive motions. An adaptive neural-fuzzy controller is presented for end-effector trajectory following, which does not rely on precise apriori knowledge of dynamic parameters and can suppress bounded external disturbances. Effectiveness of the proposed algorithm is verified through simulations.

This paper presents an adaptive neural network based controller and its application to Dynamic Positioning (DP) control system of ship. The proposed neural network based controller is developed for station-keeping and low-speed maneuvering control of ship. At first, the DP system configuration is described. And then, to validate the proposed DP system, computer simulations of station-keeping and low-speed maneuvering performance of a multi-purpose supply ship are presented under the influence of measurement noise, external disturbances such as sea current, wave, and wind. The simulations have shown the feasibility of the DP system in various maneuvering situations.

This paper proposes a novel fixed-time path-following control strategy for underactuated ships with input delay. Firstly, the fractional power is employed in the controller design to obtain the fast system convergence. Then, a fixed-time dynamic surface control (FTDSC) technique is presented to approximate the analytic derivatives of the virtual controller. Furthermore, to achieve timely system response, an input delay auxiliary system (IDAS) is introduced to effectively compensate the delayed signal between control commands and actuators. A concise adaptive law is developed to eliminate the negative impact induced by system uncertainty and ocean disturbance. Through the Lyapunov stability analysis, all tracking errors of the closed-loop system are converged to the neighborhood of origin within fixed-time setting. Finally, the effectiveness and superiority of the proposed controller is exemplified by numerical simulations.

This paper proposes an adaptive event-triggered anti-windup dynamic positioning (DP) control method for a turret-moored vessel subject to the uncertainties and nonconvex control input constraint. Firstly, by introducing a nonconvex constraint operator, the designed control input is mapped to the actual control input vector with the maximum amplitude constraint in the same direction, thereby ensuring that the actual control input remains within the nonconvex constraint set. Secondly, the uncertainties are ingeniously separated into an unavailable single parameter and available state-related items. The unavailable single parameter is estimated by an adaptive law online. Then, an adaptive nonconvex constraint anti-windup DP controller is proposed based on the single parameter adaptive law and the nonconvex constraint operator. Furthermore, we integrate an adaptive event-triggered mechanism into the DP controller to decrease its execution frequency. The adaptive event-triggered mechanism can effectively balance the control performance and control signal update frequency. Finally, the effectiveness of the proposed methods is validated through simulations.

This paper proposes an adaptive Dynamic Matrix Control (DMC) using Fuzzy Inference and its application to boiler-turbine system. Nine Step Response Models (SRM) at various operating points are represented as fuzzy inference rules. On-line fuzzy inference is performed at every sampling step to find the suitable SRM. Therefore, the proposed adaptive DMC can consider the nonlinearity of boiler-turbine system.

A common network such as internet does not provide a guaranteed network bandwidth to accommodate multimedia service in a reliable fashion. In this paper, we propose a new rate control mechanism for multimedia service on the internet which is adaptive to the dynamic QoS in real-time. Also we adapt an QoS monitoring module and real-time transmission control module to adapt dynamic network bandwidth. To do this, we used layer attribution of media data and also considered loss rate and buffer threshold in receiver side for measurement of dynamic QoS.

In this paper, a robust.adaptive control scheme is presented for precise trajectory tracking of nonholonomic mobile robots. In the controller, a set of desired trajectory is defined and used in constructing the control input which constitutes the main part of the proposed controller. The stable operating characteristics such as precise trajectory tracking, parameter estimation, disturbance suppression, tec., are shown through experiments as well as computer simulation.