Robusno adaptivno upravljanje istosmjernim servomotorom s nelinearnom širokom zračnosti

In this paper, the problem of driving angular position of a direct current servomotor system with unmodeled wide backlash nonlinearity is addressed. In order to tackle this problem, a control scheme based on an adaptive super twisting algorithm is proposed. In order to implement the proposed controller, information about angular velocity is estimated by means of a robust differentiator. Based on a simplified model of the system, the proposed scheme increases robustness against unmodeled dynamics as backlash, as not all the parameters of the system nor the bounds of the perturbations are required to be known. Experimental results considering a wide backlash angle near to 2*PI, illustrate the feasibility and performance of the proposed control methodology.U ovom radu bavi se problemom kutnog pozicioniranja istosmjernog sevomotora s nemodeliranom nelinearnošću široke zračnosti. Za rješenje tog problema predlaže se korištenje upravljačke sheme bazirane na algoritmu adaptivnog uvijanja. Kako bi se implementiralo predloženo upravljanje, kutna brzina estimira se korištenjem robusnog diferencijatora. Bazirana na pojednostavljenom modelu sustava, predložena shema povećava robustnost u odnosu na nemodeliranu dinamiku kao što je zračnost. Pritom nije potrebno poznavanje svih parametara sustava niti očekivane granice smetnji. Eksperimetalni rezultati, koji uzimaju u obzir široki kut zračnosti od skoro pi$, ilustriraju izvodljivost i učinkovitost predloženog algoritma upravljanja

A cascade dead-zone extended state observer for a class of systems with measurement noise

For high frequency noise, a new $2n$-th order cascade extended state observer with dynamic dead-zone structure is proposed in this paper. Dead zone dynamic consists of two parts. One is to "trim" the effect of noise by cutting off the part that falls in the dead zone. The other part pushes the dead zone amplitude to converge to 0 as soon as possible to ensure the convergence of the estimation error. Moreover, in the cascade structure, the high-gain parameter grows only to a second power, thus avoiding excessive amplification of the measurement noise and solving numerical implementation problems. The design procedure ensures that the extended state observer is input-to-state stable. Numerical simulations show the improvement in terms of total disturbance estimation and noise attenuation. The frequency-domain analysis of the proposed ESO using the describing function method investigates the effect of the dead zone nonlinear parameter on the performance of a closed-loop system

Integral Input-to-State Stability of Nonlinear Time-Delay Systems with Delay-Dependent Impulse Effects

This paper studies integral input-to-state stability (iISS) of nonlinear impulsive systems with time-delay in both the continuous dynamics and the impulses. Several iISS results are established by using the method of Lyapunov-Krasovskii functionals. For impulsive systems with iISS continuous dynamics and destabilizing impulses, we derive two iISS criteria that guarantee the uniform iISS of the whole system provided that the time period between two successive impulse moments is appropriately bounded from below. Then we provide an iISS result for systems with unstable continuous dynamics and stabilizing impulses. For this scenario, it is shown that the iISS properties are guaranteed if the impulses occur frequently enough. For impulsive systems with stabilizing impulses and stable continuous dynamics for zero input, we obtain an iISS result which shows that the entire system is uniformly iISS over arbitrary impulse time sequences. As applications, iISS properties of a class of bilinear systems are studied in details with simulations to demonstrate the presented results

Robust Adaptive Control via Neural Linearization and Compensation

We propose a new type of neural adaptive control via dynamic neural networks. For a class of unknown nonlinear systems, a neural identifier-based feedback linearization controller is first used. Dead-zone and projection techniques are applied to assure the stability of neural identification. Then four types of compensator are addressed. The stability of closed-loop system is also proven

Controlling a DC Motor through Lypaunov-like Functions and SAB Technique

In this paper, state adaptive backstepping and Lyapunov-like function methods are used to design a robust adaptive controller for a DC motor. The output to be controlled is the motor speed. It is assumed that the load torque and inertia moment exhibit unknown but bounded time-varying behavior, and that the measurement of the motor speed and motor current are corrupted by noise. The controller is implemented in a Rapid Control Prototyping system based on Digital Signal Processing for dSPACE platform and experimental results agree with theory

Advantages of Fuzzy Control While Dealing with Complex/ Unknown Model Dynamics: A Quadcopter Example

Commonly, complex and uncertain plants cannot be faced through well-known linear approaches. Most of the time, complex controllers are needed to attain expected stability and robustness; however, they usually lack a simple design methodology and their actual implementation is difficult (if not impossible). Fuzzy logic control is an intelligent technique which, on its basis, allows the translation from logic statements to a nonlinear mapping. Although it has been proven to effectively deal with complex plants, many recent studies have moved away from the basic premise of linguistic interpretability. In this work, a simple fuzzy controller is designed in a clear way, privileging design easiness and logical consistency of linguistic operators. It is simulated together to a nonlinear model of a quadcopter with added actuators variability, so the robust operation of the controller is also proven. Uneven gain, bandwidth, and time-delay variations are applied among quadcopter’s motors, so the simulations results enclose those characteristics which could be found in reality. As those variations can be related to actuators’ performance, an analysis can be driven in terms of the features which are not commonly included in mathematical models like power electronics drives or electric machinery. These considerations may shorten the gap between simulation and actual implementation of the fuzzy controller. Briefly, this chapter presents a simple fuzzy controller which deals with a quadcopter plant as a first approach to intelligent control

Supervision of Nonlinear Networked Control Systems Under Network Constraints

International audienceThe remote supervision for a class of nonlinear systems in the presence of additive disturbances and measurement noises is considered in this paper. The communication network may introduce time delays while exchanging data among sites connected to the network medium (i.e., the data acquisition site and the remote plant site). Two different approaches are presented in this paper. The first one uses a conventional estimator-based predictor when the uncertainties are supposed to be known. In the case of unknown but bounded uncertainties by known bounds, an interval estimation-based predictor evaluating the set of admissible values for the state is investigated. The state prediction techniques are used to compensate the effect of network-induced delays. Simulation results are introduced to illustrate the efficiency of the proposed techniques