Shop Textbooks. Add to Wishlist. USD Sign in to Purchase Instantly.
Temporarily Out of Stock Online Please check back later for updated availability. Overview This unique book presents an analytical uniform design methodology of continuous-time or discrete-time nonlinear control system design which guarantees desired transient performances in the presence of plant parameter variations and unknown external disturbances.
Numerical examples with well known Ziegel-Nichols tuning rules  or its simulation results are presented as well. Control problem statement tuning rules, identification and adaptation schemes Consider a nonlinear system with time delay in has been developed .
Hence, from 5 , the system is satisfied for all x, w e Ox x Ow. By setting J. Moreover, the output transients of x t should Then the inverse replacement have the desired performance indices. The main qualitative property of the singularly Since fJ. We assume that the fast and slow modes in the closed-loop system [ x, w , g x, w are unknown continuous bounded and exponential convergence of FMS transients to equilibrium are provided, then after the damping of functions of x and w on the bounded set n x x nM'. Thus, the output holds for all x, w e Ox x 0"..
Two-time-scale motions analysis closed-loop system.
The parameter d -ambiguity resolution can be done 1. The lower bound for p, and the parameter d :. Output step response in the closed-loop system 18 , Control variable response in the closed-loop system 18 18 , In accordance with 10 , the struc- ture of the PIDcontroller is given by where J.
Plant model and universal controller the system Two-time-scale motions analysis quirements imposed on the desired output transient performance indices of x t in the system The system 24 may. Then by means of the pre..
The results show that the proposed KKT-based predictive controller is effective from different aspects. This paper is devoted to the direct and indirect methods in system identification and using system identification in Position Control Robotic Benchmark via open loop and closed loops system identification. Other aspects which are also studied are controllability and observability. Control System 1. Other classes of disturbances need different types of sub-systems to be included. Unable to display preview. From a geometrical point of view, looking at the states of each variable of the system to be controlled, every "bad" state of these variables must be controllable and observable to ensure a good behavior in the closed-loop system.
Example displayed in Figs. Let us consider the following nonlinear system: 2, In accordance with 21 , consider 1. The controller 35 corresponds to the reference model in the fonn of type 1 system, that is Output step response in the closed-loop system 34 , The controller 36 can be rewritten as the sys- tem of state space differential equations for a pur- V. Hence, from 28 - propriate cost-function selection.
Kristianson and B. Theory Applicat. The knowledge of the bounds for leans, Louisiana, , Vol. Moscow, , VoJ..
Tikhonov, "Systems of differential equations The main restriction is due to the fact that the containing a small parameter multiplying the deriva- requirement of stability of FMS transients comes tive," In: Mathematical Sb. Moscow, " Vol. Gerashchcnko and S. The trade-off set of the control- linear systems, Moscow: Na. To obtain the controller parameters in frequency loop-shaping framework, the optimization problem is solved with primal-dual path following interior point method.
To demonstrate the effectiveness of the proposed controllers, simulation comparisons with some recently developed methods are included. Moreover, the proposed method is experimentally validated on a temperature control process. Ghousiya Begum, A. Seshagiri Rao, T. To possess H2 optimal behavior, the derived IMC controller minimizes the integral square error ISE for step input disturbancesby defining the Blaschke product of unstable poles of the specific input and the model.
Then it is converted into a single feedback loop controller as either PID or PID with first order filter on the basis of proposed underdamped IMC filter to improve the integral action and thereby providing fast response which is not feasible with critically damped filter. Maclaurin series approximation is used to design PID controller and Pades approximation is used to design PID with first order lead-lag filter.
Various first order plus dead time FOPDT examples are taken and simulation is executed on diverse unstable processes and compared with some of the developed methods in recent time in the literature. The two proposed controllers provide significant improvement with respect to both nominal and perturbed conditions. The robustness studies have also been carried out for uncertainties in the plant dynamics and it is apparent that the proposed tuning method is highly robust.
Keywords : unstable process; IMC control; H2 minimization; lead lag filter. However, to make it applicable in real time, major components needed are availability of proper sensor augmented pumps, glucose monitoring systems, and control techniques. Recent times many researchers suggest robust control techniques for designing a robust controller for computing the required insulin dose for a highly nonlinear human metabolism system.
In this control strategy, the basic linear quadratic regulator LQR is re-formulated with a state estimator based on the backstepping control approach to enhance the control performance. The justification of enhanced control performance of BLQGC is demonstrated by comparative result analysis with pre-published control techniques. By the introduction of sinusoidal pulse width modulation PWM control strategy it is expected that the nature of armature current would be nearly sinusoidal and generated torque ripples will be lesser.
In this proposed structure of a PMSM drive the speed reference has been incorporated with a speed controller to fortify that the exact speed of the proposed motor match with the base speed with null speed error. The overall structure of the PMSM drive is separated into two loop control structure, inner current loop and outer speed loop.
Moreover the performance of a fuzzy logic speed controlled PMSM drive as compared to all classical controllers provides better dynamic as well as steady state performance with reduced torque ripples. Therefore the entire performance of the proposed simplified PMSM drive in closed loop control strategy is executed and efficacy of controllers is resolved under various operating conditions.
Hence the superiority of intelligent speed controller fuzzy logic controller for this proposed PMSM drive model over all classical controllers is validated and optimized for high performance applications. Finally an auto-tuning control strategy for the fuzzy intelligent speed controller is also proposed for optimal operation of the drive system Keywords : Fuzzy logic controller; Lead compensator; Lead-Lag compensator; Permanent Magnet Synchronous Motor; Voltage Source Inverter.
A faulty sensor may lead to degraded system performance, system instability, or even a fatal accident. This paper proposes a fault detection identification algorithm to identify online sensor performance degradation and failure, where the sensor faults are characterized by variations of the sensor measurement noise covariance matrix.
To be specific, the proposed algorithm has two key features: online estimating the slowly-varying sensor measurement noise covariance and detecting the sudden fast change of the sensor measurement noise covariance. The covariance-matching technique, along with the adaptive Kalman filter, is utilized based on the information about the quality of the weighted innovation sequence to estimate the slowly-varying sensor measurement noise covariance.
The covariance-matching of the weighted innovation sequence improves the prediction accuracy and reduces the computational load, making it suitable for real-time applications. A memory-based technique, calculating the Euclidean distance of estimated covariance matrices between two sliding estimation windows, is used to detect the abrupt or intermittent change of sensor noise covariance matrix.
The memory-based technique is adopted due to its simplicity and online applicability. The proposed algorithm originally is designed for discrete linear time-varying DLTV systems and applied to discrete linear parameter-varying DLPV systems. Simulation results show that the proposed algorithm is capable of estimating the slowly-varying sensor measurement noise covariance and detecting the abrupt or intermittent change of sensor measurement noise covariance for multiple-input and multiple-output discrete linear parameter-varying systems, where the scheduling parameters lie within a compact set.
Furthermore, the proposed estimation algorithm shows a reasonable rate of convergence.
The computational complexity of the neural network is avoided by the use of Chebyshev polynomials as the basis function. The online weight update of the Chebyshev Neural Network CNN is designed for the closed loop system based on the Lyapunov stability analysis to obtain the asymptotically stable system. However, the parameter tuning of the approach is an extremely challenging mission. In this paper, the bacteria foraging optimization BFO algorithm, and the particle swarm optimization PSO algorithm are proposed to optimize the performance of the system driven by the LADRC approach in light of the identified model of the servo motor.
Extensive simulation results and experimental tests are given to demonstrate the proposed approaches are effective and efficient for the performance optimization of the LADRC approach. Keywords : algebraic parameter identification; bacteria foraging optimization; discrete time; linear active disturbance rejection control; particle swarm optimization; performance optimization.
We start by constructing two polytopic sub-systems using transformed coordinate system design. Hence, the second one is potentially faulty sensor and it is free from actuator faults. Anyway, in practise, the so-called observer matching condition is usually hard to satisfy due to the complexity and unpredictability of the system faults.