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62 Result(s)
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Prof. Dr.-Ing. Rolf Isermann
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Book
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Chapter
Control of Variable Processes with Constant Controllers
The preceding dontroller design methods assumed that the process model is exactly known. However, this is never the case in practice. Both in theoretical modelling and in experimental identification one must a...
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Chapter
Stochastic Control Systems
The controllers treated in the preceding chapters were designed for deterministic disturbances, that means for signals which are exactly known a priori and can be described analytically. Real disturbances, how...
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Chapter
Controllers for Finite Settling Time (Deadbeat)
The ripples between the sampling points that can appear with the cancellation controllers treated in chapter 6 can be avoided if a finite settling time is required for both the controlled variable and the mani...
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Chapter
Minimum Variance Controllers for Stochastic Disturbances
In the design of minimum variance controllers the variance of the controlled variable $$ \operatorname{var} [{\text{y}}({\text{k}})] = {\text{...
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Chapter
Controllers for Processes with Large Deadtime
The controller designs of the preceding chapters automatically took the process dead time into account. This was straightforward because dead time can be simply included in process models using discrete-time s...
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Chapter
Cascade Control Systems
The design of an optimal state controller involves the feedback of all the state variables of the process. If only some state variables can be measured, but for example only one state variable between the proc...
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Chapter
Multivariable Matrix Polynomial Control Systems
Based on the matrix polynomial representation of multivariable processes described in section 18.1.5 the design principles of some single input/single output controllers can be transferred to the multivariable...
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Chapter
Structures of Multivariable Processes
Part E considers some design methods for linear discrete-time multivariable processes. As shown in Figure 18.0.1 the inputs ui and outputs yj of multivariable processes influence each other, resulting in mutual J...
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Chapter
Adaptive Control Systems — A Short Review
There are many more ways in which adaptive controllers or adaptive control algorithms can be realized with digital computers, i.e. process computers and microcomputers, than with analog techniques. The great p...
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Chapter
Identification in Closed Loop
If the design of self-optimizing adaptive control systems is based on identified process models, process identification has to be performed in closed loop. There are also other applications in dynamic processe...
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Chapter
The Influence of Amplitude Quantization of Digital Control
In the previous chapters the treatment of digital control systems was based on sampled, i.e. discrete-time signals only. Any amplitude quantization was assumed to be so fine that the amplitudes could be consid...
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Chapter
Case Studies of Identification and Digital Control
In this last chapter the application of various methods of identification and digital control to industrial processes is discussed. The preceding chapters have shown that there are two main ways of combining m...
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Chapter
Combining Control Algorithms and Actuators
This section deals with the connection of control algorithms with various types of actuator. Therefore the way to control the actuators and the dynamic response of the actuators are considered initially.
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Chapter
Control with Digital Computers (Process Computers, Microprocessors)
In data processing with process computers signals are sampled and digitized, resulting in discrete (discontinuous) signals,which are quantized in amplitude and time, as shown in Fig. 2.1.
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Chapter
Deterministic Control Systems
Deterministic control systems are control systems that are designed for external deterministic disturbances or deterministic initial values. Deterministic disturbances or initial values are variabl...
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Chapter
Cancellation Controllers
The problem in tracking control system design is to make the controlled variable y follow the command input w as closely as possible. If the model GP of a stable process is known exactly and if there is no other ...
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Chapter
Parameter-optimized Controllers for Stochastic Disturbances
The parameter-optimized control algorithms given in chapter 5 can be modified to include stochastic disturbance signals n(k) by using the quadratic performance criterion
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Chapter
State Controllers for Stochastic Disturbances
The process model assumed in chapter 8 for the derivation of the state controller for deterministic initial values is now excited by a vector stochastic noise signal v(k)
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Chapter
Feedforward Control
If an external disturbance v of a process can be measured before it acts on the output variable y then the control performance with respect to this disturbance can often be improved by feedforward control, as ...
