This work reports the parameter identification of a servo system through a novel Robust Least Squares algorithm with Variable Forgetting Factor (ROLSVFF), which is aimed to the identification of time-varying parameters in perturbed dynamic systems. Real-time experiments allow the evaluation of the proposed identification algorithm, and three excitation signals are tested including a chaotic signals generated by a Duffing system. The ROLSVFF algorithm is compared against the classic Gradient algorithm, and the integral of the absolute value of the Parameter Estimate Derivative (IAPED) allows evaluating the smoothness of the parameter estimates. The experimental outcomes show that the value of the IAPED index remains in the range 70-100 for the ROLSVFF algoritm whereas in the case of the Gradient algorithm the corresponding range is 50-18500, and the value of the IAPED index strongly depends on the excitation signal.

This work proposes an adaptive controller applied to servo systems. Its salient features are the use of the acceleration and the velocity of the desired trajectory for building the regressor vector, which is employed in a update law endowed with the classic e-modification, and the proposal of a new smooth bounding technique for limiting the values of the parameter estimates produced by the update law. The performance of the proposed adaptive controller is evaluated through experiments on a low-cost laboratory prototype. In addition, the experiments allow concluding that the proposed parameter bounding technique combined with the use of noise-free signals in the update law produce good closed-loop performance.

This article presents the optimal tuning of an Active Disturbance Rejection Controller (ADRC) applied to the tracking control of a servo system. The ADRC consists of a Luenberger Observer coupled with a Disturbance Observer. Its purpose is to reject the disturbances affecting the servo system and to impose a desired closed-loop dynamics. Previous results on this controller focus only on its stability analysis. Moreover, finding the controller parameters that provide optimal performance is difficult. For the foregoing reasons, this work proposes using the Particle Swarm Optimization (PSO) algorithm to tune the parameters of the ADRC. The restrictions imposed on the particles are obtained from the stability analysis of the ADRC. This allows discarding those solutions leading to closed-loop instability. Therefore, the algorithm delivers solutions where a fitness function is minimized, and the closed-loop system is stable. Finally, realtime experiments on a laboratory prototype show the performance of the proposed tuning method.

This work presents an experimental study on the performance of the Particle Swarm Optimization (PSO) algorithm to solve the parameter estimation problem for a DC servomechanism. The study considers the case when the measurements exhibit high levels of noise. The parameters estimates obtained with the PSO algorithm are compared with those obtained using a standard Least Square (LS) algorithm. Two different data sets are applied to the PSO algorithm, the first set considers position measurements from the DC servomechanism without further processing. The second set corresponds to filtered position measurements using a first order filter with several cut-off frequencies. All the experimental results are obtained in a laboratory test platform where the position measurement are obtained through a potentiometer. This sensor produces measurements with high levels of noise, which is evaluated using a Signal-to-Noise Ratio (SNR) index. The experiments show that positive values of the SNR index translate into parameter estimates obtained with the PSO algorithm closer to those produced by the Least Squares algorithm. On the other hand, negative values of the SNR index correspond to significant discrepancies between these estimates.

The goal of this work is to describe a new delay-based control law called the Cascade Proportional Integral Retarded (CPIR) controller and its application to the position control of DC servodrives. The proposed controller has an inner loop-outer loop structure. The inner loop corresponds to an integral retarded (IR) controller that regulates the servodrive angular velocity. A proportional (P) controller closes the outer loop whose goal is to regulate the servodrive angular position. A tuning methodology for the CPIR controller is proposed and experiments using a laboratory prototype allow assessing its performance.

In this work we study a new Improved Adaptive Incremental Conductance (IAIC) algorithm proposed originally by A. Morales-Acevedo for maximum power point tracking (MPPT). The maximum power point is searched by means of an adaptive correction of the DC-DC converter duty cycle which is determined by the sum of the incremental and the instantaneous conductance at a given time. The operating principle is first presented and then the performance evaluation is made using a MATLAB/SEVIULINK model of a photovoltaic system to be connected to the grid. This new algorithm helps in reducing possible power over-impulses during solar radiation transient periods. In addition, it has a good behavior under steady state conditions because oscillations around the maximum power point are reduced. The IAIC algorithm is also better than the conventional adaptive incremental conductance (AIC) algorithm since the MPPT tracking efficiency is higher for the former than for the latter. The IAIC algorithm is highly efficient (above 99.6%), it is simpler than the AIC algorithm and it can be implemented easily using a low cost micro-controller.

This paper adresses the effect that a controller has on the parameter estimates for a closed-loop identification methodology with a DC servomechanism. Closed-loop identification is performed with a direct method, where a PD controller, which stabilizes the system without knowledge about its parameters, closes the loop. It is shown that when the perturbation signal is absent, exponential convergence can be claimed, making the identification algorithm robust. However, when there exists a perturbation signal it can be established a region where the parameter estimates belong to, and it is shown how this region is affected by the PD controller gains.

This paper presents a task-space robot controller composed of two nested loops. The inner loop corresponds to a proportional-derivative joint-position controller and the outer loop consists of a proportional-integral controller fed by task space measurements. The Lyapunov method allows concluding closed loop stability and a visual servoing application permits assessing the performance of the proposed controller.

In this paper, we propose a stable neurovisual servoing algorithm for set-point control of planar robot manipulators in a fixed-camera configuration an show that all the closed-loop signals are uniformly ultimately bounded (UUB) and converge exponentially to a small compact set. We assume that the gravity term and Jacobian matrix are unknown. Radial basis function neural networks (RBFNNs) with online real-time learning are proposed for compensating both gravitational forces and errors in the robot Jacobian matrix. The learning rule for updating the neural network weights, similar to a back propagation algorithm, is obtained from a Lyapunov stability analysis. Experimental results on a two degrees of freedom manipulator are presented to evaluate the proposed controller.

A direct adaptive neural control scheme with single and double integral-plus-state (IPS) actions is proposed. The control scheme contains two recurrent trainable neural network (RTNN) models, which are a plant parameter identifier and state estimator, an IPS feedback/feedforward controller, and one or two I-terms. The good performance of the adaptive IPS control scheme is confirmed by closed-loop systems analysis and by simulation results obtained with a MIMO plant, corrupted by noise. © 2005 Wiley Periodicals, Inc. Int J Int Syst 20: 213–224, 2005.

The paper proposed a new fuzzy-neural recurrent multi-model for systems identification and states estimation of complex nonlinear mechanical plants with friction. The parameters and states of the local recurrent neural network models are used for a local direct and indirect adaptive control systems design. The designed local control laws are coordinated by a fuzzy rule based control system. The applicability of the proposed intelligent control system is confirmed by simulation and experimental results, where a good convergence of all recurrent neural networks, is obtained.

A parametric recurrent neural network model and an improved dynamic backpropagation method of its learning, are applied for nonlinear plants identification and state estimation. The obtained parameters of the RNN model are used for design of an indirect adaptive control system. The paper suggests three main types of state-space control with RNN state estimation: a proportional; a proportional plus integral and a trajectory-tracking control. The applicability of the proposed neural indirect adaptive control schemes is confirmed by simulation results.

An improved universal parallel recurrent neural network canonical architecture, named a recurrent trainable neural network (RTNN), suited for state-space systems identification, and an improved dynamic backpropagation method of its learning, are proposed. The proposed RTNN is studied with various representative examples and the results of its learning are compared with other results given in the literature. For a complex nonlinear plants identification, a fuzzy-rule-based system and a fuzzy-neural multimodel, are used. The fuzzy-neural multimodel is applied to a mechanical system with friction identification.