Publisher's Synopsis
Control systems nowadays are becoming more and more complex, involving extremely large number of control loops, distributed networked control systems, coordinating a large number of autonomous agents. Control, computation, communication appear together to solve large scale control problems. The theory of Robust Control Systems has grown remarkably over the past ten years. Its popularity is now spreading over the industrial environment where it is an invaluable tool for analysis and design of servo systems. This rapid penetration is due to two major advantages: its applied nature and its relevance to practical problems of automation engineer. The main advantage of robust control techniques is to generate control laws that satisfy the two requirements mentioned above. More specifically, given a specification of desired behavior and frequency estimates of the magnitude of uncertainty, the theory evaluates the feasibility, produces a suitable control law, and provides a guaranty on the range of validity of this control law. This combined approach is systematic and very general. The control theory is concerned with influencing systems to realize that certain output quantities take a desired course. These can be technical systems, like heating a room with output temperature, a boat with the output quantities heading and speed, or a power plant with the output electrical power. These systems may well be social, chemical or biological, as, for example, the system of national economy with the output rate of inflation. The nature of the system does not matter. Only the dynamic behavior is of great importance to the control engineer. We can describe this behavior by differential equations, difference equations or other functional equations. In classical control theory, which focuses on technical systems, the system that will be influenced is called the (controlled) plant. The main objective of this book, Latest Trends in Robust Control, is to present a broad range of well worked out, recent theoretical and application studies in the field of robust control system analysis and design. Robust stability and robust control belong to fundamental problems in control theory and practice; various approaches have been proposed to cope with uncertainties that always appear in real plants as a result of identification /modelling errors, e.g. due to linearization and approximation, etc. A control system is robust if it is insensitive to differences between the actual plant and its model used to design the controller. To deal with an uncertain plant a suitable uncertainty model is to be selected and instead of a single model, behavior of a whole class of models is to be considered. Robust control theory provides analysis and design approaches based upon an incomplete description of the controlled process applicable in the areas of non-linear and time-varying processes, including multi input - multi output (MIMO) dynamic systems. MIMO systems usually arise as interconnection of a finite number of subsystems, and in general, multivariable centralized controllers are used to control them. However, practical reasons often make restrictions on controller structure necessary or reasonable. In an extreme case, the controller is split into several local feedbacks and becomes a decentralized controller. Compared to centralized full-controller systems such a control structure brings about certain performance deterioration; however, this drawback is weighted against important benefits, e.g. hardware, operation and design simplicity, and reliability improvement. Robust approach is one of useful ways to address the decentralized control problem. Control systems nowadays are becoming more and more complex, involving extremely large number of control loops, distributed networked control systems, coordinating a large number of autonomous agents. Control, computation, communication appear together to solve large scale control problems. The theory of Robust Control Systems has grown remarkably over the past ten years. Its popularity is now spreading over the industrial environment where it is an invaluable tool for analysis and design of servo systems. This rapid penetration is due to two major advantages: its applied nature and its relevance to practical problems of automation engineer. The main advantage of robust control techniques is to generate control laws that satisfy the two requirements mentioned above. More specifically, given a specification of desired behavior and frequency estimates of the magnitude of uncertainty, the theory evaluates the feasibility, produces a suitable control law, and provides a guaranty on the range of validity of this control law. This combined approach is systematic and very general. The control theory is concerned with influencing systems to realize that certain output quantities take a desired course. These can be technical systems, like heating a room with output temperature, a boat with the output quantities heading and speed, or a power plant with the output electrical power. These systems may well be social, chemical or biological, as, for example, the system of national economy with the output rate of inflation. The nature of the system does not matter. Only the dynamic behavior is of great importance to the control engineer. We can describe this behavior by differential equations, difference equations or other functional equations. In classical control theory, which focuses on technical systems, the system that will be influenced is called the (controlled) plant. The main objective of this book, Latest Trends in Robust Control, is to present a broad range of well worked out, recent theoretical and application studies in the field of robust control system analysis and design. Robust stability and robust control belong to fundamental problems in control theory and practice; various approaches have been proposed to cope with uncertainties that always appear in real plants as a result of identification /modelling errors, e.g. due to linearization and approximation, etc. A control system is robust if it is insensitive to differences between the actual plant and its model used to design the controller. To deal with an uncertain plant a suitable uncertainty model is to be selected and instead of a single model, behavior of a whole class of models is to be considered. Robust control theory provides analysis and design approaches based upon an incomplete description of the controlled process applicable in the areas of non-linear and time-varying processes, including multi input - multi output (MIMO) dynamic systems. MIMO systems usually arise as interconnection of a finite number of subsystems, and in general, multivariable centralized controllers are used to control them. However, practical reasons often make restrictions on controller structure necessary or reasonable. In an extreme case, the controller is split into several local feedbacks and becomes a decentralized controller. Compared to centralized full-controller systems such a control structure brings about certain performance deterioration; however, this drawback is weighted against important benefits, e.g. hardware, operation and design simplicity, and reliability improvement. Robust approach is one of useful ways to address the decentralized control problem. Control systems nowadays are becoming more and more complex, involving extremely large number of control loops, distributed networked control systems, coordinating a large number of autonomous agents. Control, computation, communication appear together to solve large scale control problems. The theory of Robust Control Systems has grown remarkably over the past ten years. Its popularity is now spreading over the industrial environment where it is an invaluable tool for analysis and design of servo systems. This rapid penetration is due to two major advantages: its applied nature and its relevance to practical problems of automation engineer. The main advantage of robust control techniques is to generate control laws that satisfy the two requirements mentioned above. More specifically, given a specification of desired behavior and frequency estimates of the magnitude of uncertainty, the theory evaluates the feasibility, produces a suitable control law, and provides a guaranty on the range of validity of this control law. This combined approach is systematic and very general. The control theory is concerned with influencing systems to realize that certain output quantities take a desired course. These can be technical systems, like heating a room with output temperature, a boat with the output quantities heading and speed, or a power plant with the output electrical power. These systems may well be social, chemical or biological, as, for example, the system of national economy with the output rate of inflation. The nature of the system does not matter. Only the dynamic behavior is of great importance to the control engineer. We can describe this behavior by differential equations, difference equations or other functional equations. In classical control theory, which focuses on technical systems, the system that will be influenced is called the (controlled) plant. The main objective of this book, Latest Trends in Robust Control, is to present a broad range of well worked out, recent theoretical and application studies in the field of robust control system analysis and design. Robust stability and robust control belong to fundamental problems in control theory and practice; various approaches have been proposed to cope with uncertainties that always appear in real plants as a result of identification /modelling errors, e.g. due to linearization and approximation, etc. A control system is robust if it is insensitive to differences between the actual plant and its model used to design the controller. To deal with an uncertain plant a suitable uncertainty model is to be selected and instead of a single m
