Model predictive control has become a first choice control strategy in industry because it is intuitive and can explicitly handle MIMO linear and nonlinear systems with the presence of variable constraints and interactions. In this work a nonlinear state-space model has been developed and used as the internal model in predictive control for the ALSTOM gasifier. A linear model of the plant at 0%...

Future space systems will require high robustness of the control system as well as high performance because of their two major features: lightly damped structural dynamics; and nonlinear inertia properties induced by the large motion of articulating sensor heads and antennas. Effective robust control design techniques are needed to satisfy the performance requirements of future space systems in...

The design of stabilizing controllers for multiple-input–multiple-output (MIMO) nonlinear plants with unknown nonlinearities is a challenging problem. The high dimensionality coupled with the inability to identify the nonlinearities online or offline accurately motivates the design of stabilizing controllers based on approximations or on approximate estimates of the plant nonlinearities that ar...

this article presents a fuzzy robust mixed - sensitivity gain - scheduled h controller based on the loop -shaping methodology for a class of mimo uncertain nonlinear time - varying systems. in order to design this controller, the nonlinear parameter - dependent plant is first modeled as a set of linear subsystems by takagi and sugeno’s (t - s) fuzzy approach. both loop - shaping methodology and...

one of the most important challenges in nonlinear, multi-input multi-output (mimo) and time variant systems (e.g., robot manipulator) is designing a controller with acceptable performance. this paper focused on design a new artificial non linear controller with on line tunable gain applied in the robot manipulator. the sliding mode fuzzy controller (smfc) was designed as 7 rules mamdani’s infer...

In this paper, a novel adaptive fuzzy robust controller with a state observer approach based on the hybrid particle swarm optimization-simulated annealing (PSO-SA) technique for a class of multi-input multi-output (MIMO) nonlinear systems with disturbances is proposed. Firstly, particle swarm optimization-simulated annealing (PSO-SA) is used to adjust the fuzzy membership functions, while adapt...

In this paper, the fault diagnosis problem for a class of nonlinear multipleinput multiple-output (MIMO) systems with uncertainty is investigated. Under some geometric conditions, the system is transformed into two different subsystems. One is in the generalized observer canonical form and is not affected by actuator faults, so a nonlinear sliding mode observer for this subsystem is constructed...

Linear model based predictive control (MBPC) has many advantages but also drawbacks over nonlinear MBPC. In this paper a possibility of using Linear MBPC to control nonlinear systems is investigated. Takagi-Sugeno fuzzy models are chosen as the model structure. Local linear models can be derived from the linear rule consequents in a straightforward way. Each sample time a local linear model is ...

In this paper, robust adaptive neural network control is investigated for a class of multi-input-multi-output (MIMO) pure-feedback nonlinear system with unknown nonlinearities. The unknown nonlinearities could be come from unmodeled dynamics, modeling errors, or nonlinear time-varying uncertainties. Based on the backstepping design technique and the universal approximation property of the neura...

The aim of this paper is to propose a model-based feedback control strategy for indoor temperature regulation in buildings equipped with underfloor air distribution (UFAD). Supposing distributed sensing and actuation capabilities, a 0−D model of the ventilation process is first derived, based on the thermodynamics properties of the flow. A state-space description of the process is then inferred...