Faouzi M’sahli

Model-based Predictive and Backstepping controllers for a state coupled four-tank system with bounded control inputs: A comparative study

Abstract This paper investigates the problem of global tracking control design for a state coupled four-tank liquid level system with bounded control inputs. For this MIMO system׳s dynamics, motivated by a desire to provide precise liquid level control, two radically different control approaches are presented and compared: the nonlinear generalized predictive control (NGPC) and the Backstepping control. First, an analytical solution of the NGPC is developed based on the nominal model. Then, a nonlinear Backstepping controller is designed in order to ensure globally asymptotical stabilization for this nonlinear system. To ensure a suitable basis for their comparison, the two different control methods are designed and verified with the same test setup under the control input saturation imposed by the system’s actuators. To highlight the efficiency and applicability of the proposed control schemes, simulation as well as experimental results are provided and discussed.

Extracting TSK-type Neuro-Fuzzy model using the Hunting search algorithm

This paper proposes a Takagi-Sugeno-Kang (TSK) type Neuro-Fuzzy model tuned by a novel metaheuristic optimization algorithm called Hunting Search (HuS). The HuS algorithm is derived based on a model of group hunting of animals such as lions, wolves, and dolphins when looking for a prey. In this study, the structure and parameters of the fuzzy model are encoded into a particle. Thus, the optimal structure and parameters are achieved simultaneously. The proposed method was demonstrated through modeling and control problems, and the results have been compared with other optimization techniques. The comparisons indicate that the proposed method represents a powerful search approach and an effective optimization technique as it can extract the accurate TSK fuzzy model with an appropriate number of rules.

Robust exponential stabilisation of a class of nonlinear systems using a novel technique of higher order sliding mode control

In this work, we propose a novel technique of higher order sliding mode control in order to solve the problem of chattering phenomenon which appears, generally, with standard sliding mode controller. The proposed approach provides an exponential stability on the sliding surface and guarantees the robustness of the closed loop system against uncertainties and external matched disturbances. The resulting controller has been applied to control an induction motor. Numerical simulations are developed to show the effectiveness of the proposed approach.

Generalized predictive control for a coupled four tank MIMO system using a continuous-discrete time observer

This paper deals with the problem of the observer based control design for a coupled four-tank liquid level system. For this MIMO systems dynamics, motivated by a desire to provide precise and sensorless liquid level control, a nonlinear predictive controller based on a continuous-discrete observer is presented. First, an analytical solution from the model predictive control (MPC) technique is developed for a particular class of nonlinear MIMO systems and its corresponding exponential stability is proven. Then, a high gain observer that runs in continuous-time with an output error correction time that is updated in a mixed continuous-discrete fashion is designed in order to estimate the liquid levels in the two upper tanks. The effectiveness of the designed control schemes are validated by two tests; The first one is maintaining a constant level in the first bottom tank while making the level in the second bottom tank to follow a sinusoidal reference signal. The second test is more difficult and it is made using two trapezoidal reference signals in order to see the decoupling performance of the systems outputs. Simulation and experimental results validate the objective of the paper.

Unknown inputs observers design for a class of nonlinear switched systems

The main topic of this paper is the problem of constructing unknown inputs observers for MIMO nonlinear switched systems. The idea is to switch between different observers to estimate the unknown states and the non-measured inputs for every transient’s periods with the choice of various values of the gain to reduce the error estimation. Theoretical stability analysis is derived to ensure the convergence of the designed observer. Instead, simulation results on a quadruple tank process are given in order to assess the efficiency of the proposed hybrid observers.

Parameter Identification of an Activated Sludge Wastewater Treatment Process Based on Particle Swarm Optimization Method

The current paper is entirely devoted to show the applicability of Particle Swarm Optimization (PSO) algorithm as a parameter identification method for a representative model of an Activated Sludge Wastewater Treatment Process (ASWWTP) with alternating phases. The model of identification is composed of two linear submodels: one for the aerobic phase and the other for the anoxic phase. In order to prove the efficiency of the proposed method, its performance is compared with another classical method called Simplex Search Algorithm (SSA) as well as with the experimental data.

Cuckoo Search Approach for Parameter Identification of an Activated Sludge Process

A parameter identification problem for a hybrid model is presented. The latter describes the operation of an activated sludge process used for waste water treatment. Parameter identification problem can be considered as an optimization one by minimizing the error between simulation and experimental data. One of the new and promising metaheuristic methods for solving similar mathematical problem is Cuckoo Search Algorithm. It is inspired by the parasitic brood behavior of cuckoo species. To confirm the effectiveness and the efficiency of the proposed algorithm, simulation results will be compared with other algorithms, firstly, with a classical method which is the Nelder-Mead algorithm and, secondly, with intelligent methods such as Genetic Algorithm and Particle Swarm Optimization approaches.

Unknown Inputs Nonlinear Observer for an Activated Sludge Process

This paper deals with the jointly estimation problem of unknown inputs and nonmeasured states of one altering aerated activated sludge process (ASP). In order to provide accurate and economic concentration measures during aerobic and anoxic phases, a cascade high gain observer (HGO) approach is developed. Only two concentrations are available; the other process’s states are assumed unavailable. The observer converges asymptotically and it leads to a good estimation of the unavailable states which are the ammonia and substrate concentration, as well as a quite reconstruction of the unknown inputs, which are the influent ammonia and the influent substrate concentrations. To highlight the efficiency of the proposed HGO with this MIMO system’s dynamics, simulation results are validated with experimental data.

Robust High-Gain Observers Based Liquid Levels and Leakage Flow Rate Estimation

The paper aims to solve the problem of liquid level and leakage flow rate estimations for a state coupled four-tank process, that is why an UIO is developed to simultaneously estimate the unmeasured state variables and the perturbations considered as unknown inputs. We have proposed a state repartition that allows putting the model of the quadruple tank system to the canonical form for which the design of the observer is more easier. The observation scheme that uses a combination of high-gain observers and sliding mode observers allows improving robustness in the state estimation quality and a perfect reconstruction of the disturbance waveforms.

Output Feedback Robust Exponential Higher Order Sliding Mode Control

The higher order sliding mode controller (HOSMC) is a robust control scheme used to overcome the chattering phenomenon which appears, generally, with standard sliding mode controller (SMC). In this chapter, a novel technique of HOSMC for uncertain nonlinear systems is presented. The proposed controller allows obtaining an exponential stability as well a finite time convergence to the sliding surface and guarantees robustness against uncertainties and external matched disturbances. Furthermore, the synthesis of the control law depends explicitly on the states of the system. But, in practice, most of systems admit one or more unknown states. Such problem represents a serious drawback when implementing the controller in real time. To solve this problem we incorporate a High Gain Observer (HGO) into the controller to estimate the missing states. These techniques of control and observation are applied to an induction motor system. Numerical simulations are developed to show the effectiveness of the resulting controller.