Intelligent control

Intelligent control is a computational procedure for directing a complex system with incomplete and inadequate representation and under incomplete specifications of how to do so in an uncertain environment toward a certain goal. Intelligent control often combines planning with on-lineonline error compensation and requires the learning of a system and environment to be part of the control process. Overall, intelligent control systems seek to emulate important characteristics of human intelligence. These characteristics inlcudeinclude adaptation and learning, planning under largegreat uncertainty, and coping with large amounts of data. And as intelligent control has developed, it has begun to compassencompass everything that is not characterized as conventional control. As an interdisciplinary approach, intelligent control combines and extends theories from areas such as control, computer science, operations research, and mathematics, and takes inspiration from biological systems.

Classical control systems require an agent in the process, and a designer to construct a mathematical model of the system, and the dynamics of aplanta plant that afectsaffects controlling it. In this systemssystem, the controller of the robotic system is the intelligence in the system and is required to control the robotic system.

Whereas, an intelligent control system requires a designer to input system behavior and the intelligent control system abstractly models the system. This is also known as the Lazymans Approach, since the design does not need to know the internal dynamics to be controlled. Further, in an intelligent control system, the intelligence is shifted toward the software controlling the system, but. theThe designer must have some knowledge of the system, but should not need to develop an accurate model of it for the intelligent control system to work.

The complexity of a controlled object that an intelligently controlled system may deal with includeincludes model uncertainty, high nonlinearity, distributed sensors/actuators, dynamic mutations, multiple time scales, complex information patterns, big data processprocesses, and strict characteristic indicators. In order toTo emulate human intelligence to solve these problems, various researches and researchers continue to suggest intelligent control can include expert control, fuzzy control, neural network control, hierarchical intelligent control, anthropomorphic intelligent control, integrated intelligent control, combined intelligent control, chaos control, and wavelet theory, amongstamong others. Further, intelligent control systems can try to acheiveachieve adaptation and learning, planning under uncertainty, and decision making.

The basic formmodel of the decision makingdecision-making process of fuzzy control isincludes fuzzy concept, fuzzy judgment, and fuzzy reasoning. These are also known as the three basic forms of fuzzy concept, fuzzy judgment, and fuzzy reasoning, also known as thethree basic forms of human fuzzy thinking. In a fuzzy controller, the fuzzy concept is a fuzzy linquisticlinguistic variable represented by a fuzzy set. For example, the exact amount of error is converted to the fuzzy quantity on the discrete domain, also known as fuzzy quantization processing. A fuzzy control systemssystem can be summarized into several fuzzy control rules in language, which can be described by a fuzzy relation matrix, which is a general principle of the operating process, and also known as the language model of the controlled object.

The adatpvieadaptive fuzzy controller works to make a control strategy described in language by observing and evaluating the performance of the controller. There are two types of adpativeadaptive control: direct adaptive control and indirect adaptive control. Direct adaptive control works to addadds an adaptive mechanism to the basic feedback control, which allows the fuzzy control to allowenable it to adaptively modify the controller parameters to make the control. Indirect adaptive control works to useuses online identification to identify the parameter of an object, and use identified parameters to adjust the control parameters to continuously improve the parameter corrector and improve the control performance. The adaptive fuzzy controller is built on a similar structure as the basic fuzzy controller mechanism, with an adaptive mechanism added.

Artificial neural networks are circuits, computer algorithms, orand mathematical representations inspired by massively connected setsets of neurons that imitate biological neural networks. This offers an alternative computing network that has proven useful in pattern recognition, signal processing, estimate, and control problems. The neural network model offers distributed storage of information, information processing and reasoning with parallelism, information processing with the characteristic of self-organization and self-learning, and a strong non-linear mapping capability from input to ouputoutput.

In the control system, the non-linear mapping capability can be used to model complex non-linear objects that are difficult to accurately describe, to act as controllers, to optimize calculations, to perform inference, for fault diagnosis, and to adapt to certain functions. Neural network control can be combined with fuzzy logic control, where fuzzy systems can directly express logic and knowledge, and neural networks are better at learnignlearning to express knowledge implicitly through data. They are complementary and related systemsystems, with fuzzy systems suitable for top-down expression, and neural network systems suitable for bottom-up learning processprocesses.

An expert control system uses an expert, or someone defined as an expert, based on their deep theoretical knowledge or practical experience in a given field, and follows the expertsexpert's decision-making actions to solve difficult problems or achieve important results through the accumulation of the theoretical knowledge and practical experience. The system then uses some kind of knowledge acquistiionacquisition method to store the knowledge and experience and code those decisions into actions an intelligent control system can take. This means an expert system generally consists of a knowledge base, a database, an inference engine, and an interpretation part and knowledge acquisition system. These systems are also expected to have high reliability and long-term continuous operation, real-time nature online control, excellent control performance and anti-interference, and made flexibleflexibility, and easy to maintainmaintenance.

Human-like intelligent control works to introduce some non-linear controlmethodscontrol methods, offering a more dynamic process and transient process, according to the needs of the sysetmssystem's characteristics, behavior, and control performance. This requires expert control experience and intuitive judgment and reasoning rules. These control systems are conducive to solving the contradiction between fastness, stability, and accuracy in the control system, while also enhancing the system's adaptibilityadaptability and robustness of the system in regardsregard to uncertain factors. In part, to achieve this, human-like intelligent control basically works to imiateimitate human intelligent behavior for control and decision-making.

Often this provides a machine with the necessary operational training, and the artificially implemented control method can be close to optimal. The structure of the human-like intelligent control is similar to an expert controller, including four parts: acquisition and processing of characteristic information, feature pattern set, pattern recognition, and control rule set. The working process of the human-like intelligent controller can be summarized into three steps: the system judges the characteristic mode of the dynamic process;, the inference mechanism searches for a matching control rule;, and the controller executes the control rules to keep the object controlled.

Large systems often have the characteristics of high-level order of the system, a large number of subsystems and interrelationships, a large number of system evaluation goals, and conflicts between goals. In order to process these systems, people tend to break them down into hierarchical levels. For large scalelarge-scale complex control systems, these systems aim to form a pyramid-like hierarchical control structure. The hierarchical intelligent control structure mimics the human central nervous system, which is organized according to a multilayer structure.

This structure is further divided into three basic processing methods and exchange methods: decentralized control, distributed control, and hierarchical control. The main structure of the system includes multiple descriptions and multi-level descriptions. Depending on the decision makingdecision-making objectives, the system can be divided into single-stage single-objective systems, single-stage multi-objective systems, and multi-stage multi-objective systems. In this control structure, the configured controller receives information from an upper-level controller and is used to control the controller or subsystem at a lower level. To ensure possible conflicts between controllers are avoided, the system also uses coordinator systems, of which there are many methods but tend to be based on two basic principles of association prediction coordination and association balance coordination.

Hierarchical intelligent control systems are a branch of intelligent control whichthat werewas first applied in industrial practice and played an important role in the formation of intelligent control systems; where. theThe intelligent control structure, according to the intelligence level, is divided into three levels: organization level, coordination level, and control level.

Bayesian probabilitsticprobabilistic control should measure the confidence of an individual for an uncertain proposition and use this property to control it, making the system subjective in this sense. Using the probability theory proposed by Bayes, the sensitivity of the decision makingdecision-making can be examined. Bayes further proposed the concept of prior and posterior probability, where the prior probability can be modified for new information to obtain the posterior probability; or, put another way, Bayesian probability control can be used to incorporate new information into the analysis. The model offers stable classification efficiency, can work well on small-scale data, can handle multi-class tasks, and is suitable for incremental training. Further, the model is not as sensitive to missing data as other models are, with a fairly simple algorithm.

The genetic algorithm control is a computational model offering a global, parallel, search and optimization methods developed on Darwinian principles. This model works with potential solutions to a problem, with each potential solution represented as a particular solution to the problem, generally expressed in some form of genetic code. The idea of these algorithms is based on the ideas from natural laws of biological genetics and is a search algorithm with an iterative process of "survival + detection.". The genetic algorithm uses randomization techniques to guide an efficient search of an encoded parameter space. Among them, selection, crossover, andmutationand mutation constitute the genetic operation of the algorithm; the five elements of parameter coding, initial population setting, fitness function design, genetic operation design, and control parameter setting constitute the core of the genetic algorithm. The genetic algorithm control often produces interpretable results, results that are easy to apply, with a range of data types that can be processed, can be used for optimization, and offers easyintegrated integrationeasily with the neural network.

Intelligent control is a computational procedure for directing a complex system with incomplete and inadequate representation and under incomplete specifications of how to do so in an uncertain environment toward a certain goal. Intelligent control often combines planning with on-line error compensation and requires learning of a system and environment to be part of the control process. Overall, intelligent control systems seek to emulate important characteristics of human intelligence. These characteristics inlcude adaptation and learning, planning under large uncertainty, and coping with large amounts of data. And as intelligent control has developed, it has begun to compass everything that is not characterized as conventional control. As an interdisciplinary approach, intelligent control combines and extends theories from areas such as control, computer science, operations research, mathematics, and takes inspiration from biological systems.

Classical control systems require an agent in the process, and a designer to construct a mathematical model of the system, and the dynamics of aplant that afects controlling it. In this systems, the controller of the robotic system is the intelligence in the system and is required to control the robotic system.

Whereas, an intelligent control system requires a designer to input system behavior and the intelligent control system abstractly models the system. This is also known as the Lazymans Approach, since the design does not need to know the internal dynamics to be controlled. Further, in an intelligent control system, the intelligence is shifted toward the software controlling the system, but the designer must have some knowledge of the system, but should not need to develop an accurate model of it for the intelligent control system to work.

The complexity of a controlled object that an intelligently controlled system may deal with include model uncertainty, high nonlinearity, distributed sensors/actuators, dynamic mutations, multiple time scales, complex information patterns, big data process, and strict characteristic indicators. In order to emulate human intelligence to solve these problems, various researches and researchers continue to suggest intelligent control can include expert control, fuzzy control, neural network control, hierarchical intelligent control, anthropomorphic intelligent control, integrated intelligent control, combined intelligent control, chaos control, and wavelet theory, amongst others. Further, intelligent control systems can try to acheive adaptation and learning, planning under uncertainty, and decision making.

The basic form of the decision making process of fuzzy control is the three basic forms of fuzzy concept, fuzzy judgment, and fuzzy reasoning, also known as thethree basic forms of human fuzzy thinking. In a fuzzy controller, the fuzzy concept is a fuzzy linquistic variable represented by a fuzzy set. For example, the exact amount of error is converted to the fuzzy quantity on the discrete domain, also known as fuzzy quantization processing. A fuzzy control systems can be summarized into several fuzzy control rules in language, which can be described by a fuzzy relation matrix, which is a general principle of the operating process, and also known as the language model of the controlled object.

System principle of fuzzy control.

The adatpvie fuzzy controller works to make a control strategy described in language by observing and evaluating the performance of the controller. There are two types of adpative control: direct adaptive control and indirect adaptive control. Direct adaptive control works to add an adaptive mechanism to the basic feedback control, which allows the fuzzy control to allow it to adaptively modify the controller parameters to make the control. Indirect adaptive control works to use online identification to identify the parameter of an object, and use identified parameters to adjust the control parameters to continuously improve the parameter corrector and improve the control performance. The adaptive fuzzy controller is built on a similar structure as the basic fuzzy controller mechanism, with an adaptive mechanism added.

Structure of the adaptive fuzzy controller.

Artificial neural networks are circuits, computer algorithms, or mathematical representations inspired by massively connected set of neurons that imitate biological neural networks. This offers an alternative computing network that has proven useful in pattern recognition, signal processing, estimate, and control problems. The neural network model offers distributed storage of information, information processing and reasoning with parallelism, information processing with the characteristic of self-organization and self-learning, and a strong non-linear mapping capability from input to ouput.

In the control system, the non-linear mapping capability can be used to model complex non-linear objects difficult to accurately describe, to act as controllers, to optimize calculations, to perform inference, for fault diagnosis, and to adapt to certain functions. Neural network control can be combined with fuzzy logic control, where fuzzy systems can directly express logic and knowledge, and neural networks are better at learnign to express knowledge implicitly through data. They are complementary and related system, with fuzzy systems suitable for top-down expression, and neural network systems suitable for bottom-up learning process.

An expert control system uses an expert, or someone defined as an expert based on their deep theoretical knowledge or practical experience in a given field, and follows the experts decision-making actions to solve difficult problems or achieve important results through the accumulation of the theoretical knowledge and practical experience. The system then uses some kind of knowledge acquistiion method to store the knowledge and experience and code those decisions into actions an intelligent control system can take. This means an expert system generally consists of a knowledge base, a database, an inference engine, and an interpretation part and knowledge acquisition system. These systems are also expected to have high reliability and long-term continuous operation, real-time nature online control, excellent control performance and anti-interference, and made flexible and easy to maintain.

Human-like intelligent control works to introduce some non-linear controlmethods, offering a more dynamic process and transient process, according to the needs of the sysetms characteristics, behavior, and control performance. This requires expert control experience and intuitive judgment and reasoning rules. These control systems are conducive to solving the contradiction between fastness, stability, and accuracy in the control system, while also enhancing the system's adaptibility and robustness of the system in regards to uncertain factors. In part, to achieve this, human-like intelligent control basically works to imiate human intelligent behavior for control and decision-making.

Often this provides a machine the necessary operational training, the artificially implemented control method can be close to optimal. The structure of the human-like intelligent control is similar to an expert controller, including four parts: acquisition and processing of characteristic information, feature pattern set, pattern recognition, and control rule set. The working process of the human-like intelligent controller can be summarized into three steps: the system judges the characteristic mode of the dynamic process; the inference mechanism searches for a matching control rule; and the controller executes the control rules to keep the object controlled.

Large systems often have the characteristics of high-level order of the system, a large number of subsystems and interrelationships, a large number of system evaluation goals, and conflicts between goals. In order to process these systems, people tend to break them down into hierarchical levels. For large scale complex control systems, these systems aim to form a pyramid-like hierarchical control structure. The hierarchical intelligent control structure mimics the human central nervous system which is organized according to a multilayer structure.

This structure is further divided into three basic processing methods and exchange methods: decentralized control, distributed control, and hierarchical control. The main structure of the system includes multiple descriptions and multi-level descriptions. Depending on the decision making objectives, the system can be divided into single-stage single-objective systems, single-stage multi-objective systems, and multi-stage multi-objective systems. In this control structure, the configured controller receives information from an upper-level controller and is used to control the controller or subsystem at a lower level. To ensure possible conflicts between controllers are avoided, the system also uses coordinator systems, of which there are many methods but tend to be based on two basic principles of association prediction coordination and association balance coordination.

Hierarchical intelligent control systems are a branch of intelligent control which were first applied in industrial practice and played an important role in the formation of intelligent control systems; where the intelligent control structure, according to the intelligence level, is divided into three levels: organization level, coordination level, and control level.

Bayesian probabilitstic control should measure the confidence of an individual for an uncertain proposition and use this property to control it, making the system subjective in this sense. Using the probability theory proposed by Bayes, the sensitivity of the decision making can be examined. Bayes further proposed the concept of prior and posterior probability, where the prior probability can be modified for new information to obtain the posterior probability; or, put another way, Bayesian probability control can be used to incorporate new information into the analysis. The model offers stable classification efficiency, can work well on small-scale data, can handle multi-class tasks, and is suitable for incremental training. Further, the model is not as sensitive to missing data as other models are, with a fairly simple algorithm.

The genetic algorithm control is a computational model offering a global, parallel, search and optimization methods developed on Darwinian principles. This model works with potential solutions to a problem, with each potential solution represented as a particular solution the problem, generally expressed in some form of genetic code. The idea of these algorithms is based on the ideas from natural laws of biological genetics and is a search algorithm with an iterative process of "survival + detection". The genetic algorithm uses randomization techniques to guide an efficient search of an encoded parameter space. Among them, selection, crossover, andmutation constitute the genetic operation of the algorithm; the five elements of parameter coding, initial population setting, fitness function design, genetic operation design, and control parameter setting constitute the core of the genetic algorithm. The genetic algorithm control often produces interpretable results, results that are easy to apply, with a range of data types that can be processed, can be used for optimization, and offers easy integration with neural network.