This work is concerned with the tuning of parameters Model Predictive Control (MPC) algorithms when used for industrial tasks, i.e., compensation disturbances that affect process (process uncontrolled inputs and measurement noises). The discussed simulation optimisation procedure quite computationally simple since consecutive are optimised separately, it requires only a very limited number simu...

The grid-forming converter is an important unit in the future power system with more inverter-interfaced generators. However, improving its performance still a key challenge. This paper proposes generalized architecture of from view multivariable feedback control. As result, many existing popular control strategies, i.e., droop control, synchronization virtual synchronous generator matching dis...

This paper addresses the problem of performance assessment for MIMO PID controllers. An efficient comparative analysis is established using two Iterative Linear Matrix Inequality (ILMI) approaches. To evaluate the robustness and the performances of each ILMI approach, four tests are carried out on the closed-loop system including White Gaussian noise, set point changes, parametric perturbation,...

This paper discusses the development of robust versions of multivariable Non-Minimal State Space (NMSS) design procedures, for Proportional-Integral-Plus (PIP) control systems. The formulation allows for the exploitation of any state variable feedback control procedure, including modern robust designs based on H2 and H∞ methods, as well as risk sensitive optimisation. The control designs are ev...

This paper presents a unit vector based output-feedback model-reference adaptive control (UV-MRAC) for uncertain multi-input-multi-output (MIMO) linear systems. Some features of this new algorithm are: (i) Less restrictive a priori knowledge about the high frequency gain matrix of the plant is required. (ii) It applies to plants of any uniform relative degree. (iii) The closed loop system is ex...

Time-domain limitations due to right half-plane zeros and poles in linear multivariable control systems are studied. Lower bounds on the interaction are derived. They show not only how the location of zeros and poles are critical in multivariable systems, but also how the zero and pole directions in4uence the performance. The results are illustrated on the quadruple-tank process, which is a new...

This paper deals with an iterative learning control law for multivariable systems. The desired inputs are supposed to be known and periodic. The principle of the control is to make outputs as close as possible to desired inputs at each new period. After the design of multivariable repetitive controller, we give the stability condition of the algorithm and some simulation results.