The wastewater treatment process (WWTP) operates under diverse conditions, and precise control of dissolved oxygen (DO) and nitrate nitrogen (NO3-N) concentrations is crucial for satisfying water quality standards. This paper proposes an adaptive fuzzy neural network (FNN) constrained control method based on an event-triggered mechanism. Firstly, the unknown nonlinear dynamic functions encountered in WWTP are effectively approximated using the FNN’s strong adaptive capability. Secondly, an event-triggered control (ETC) strategy is introduced to reduce the communication burden, with trigger conditions designed based on the control signal error. Subsequently, a time-varying asymmetric barrier Lyapunov function (BLF) is used to construct controllers for DO and NO3-N concentrations, ensuring variables remain within time-varying constraint ranges. Meanwhile, a fault-tolerant control (FTC) approach is introduced to cope with potential actuator failures during the WWTP. Finally, simulations using Benchmark Simulation Model 1 (BSM1) are performed to verify the effectiveness of the proposed method.

This paper presents an adaptive fuzzy tracking control protocol for a category of uncertain nonlinear multi-agent systems (MASs) subject to deception attacks and multiple constraints (time-varying asymmetry constraints on tracking error and system states). Fuzzy logic systems (FLSs) are employed to approximate the unknown nonlinear dynamics in MASs. Since deception attacks over the sensor network make the actual states of the MASs unavailable, this paper employs the compromised states for feedback control. Based on the relationship between the compromised states and actual states, the problem of satisfying the original state constraints boils down to the new constraints on compromised states. The use of a nonlinear mapping together with the dynamic surface control (DSC) strategy can ensure that the multiple constraints are solved with the constraint boundaries of system output being freely selected by the user, the feasibility requirement on the virtual controller is removed, and the computational explosion is mitigated. Under this control protocol, the tracking performance of MASs subject to deception attacks is achieved, while all constraints are not violated. Ultimately, simulation results verify the effectiveness and advantage of the developed control method.

To enhance the control effectiveness and operational efficiency of the wastewater treatment process (WWTP), this article proposes a multivariable optimal control scheme based on an identifier–critic–actor reinforcement learning (RL) framework with an event-triggered mechanism (ETM) for dissolved oxygen (DO) and nitrate nitrogen (NO) concentrations. First, the first fuzzy neural network (FNN) is used to estimate the unknown dynamics in WWTP, and the second FNN is implemented within the critic–actor optimization framework. Second, the RL algorithm is applied to design optimal controllers for DO and NO concentrations by constructing a tangent barrier Lyapunov function. Moreover, a dynamic ETM based on the adaptive threshold strategy is proposed to balance the control performance and energy consumption in the wastewater system. Finally, stability analysis and the benchmark simulation model no. 1 are conducted to verify that the control scheme proposed in this article demonstrates effectiveness and enhanced performance.

In a wastewater treatment process (WWTP), which covers a variety of physical, chemical, and biological treatment steps, it is a challenging control task to maintain proper dissolved oxygen (DO) and nitrate nitrogen (NO) concentrations to meet effluent standards. This paper proposes an event-triggered adaptive control method using a self-organizing fuzzy neural network (ETSOFNN) to regulate DO and NO concentrations. Moreover, a self-organizing fuzzy neural network (SOFNN) based on the maximum correlation entropy-induced criterion identifies and approximates the nonlinear function, which further enables the dynamic adjustment of the controller structure, including the addition or deletion of parameters. In addition, a dynamic event-triggered mechanism with a relative threshold strategy is introduced into the controller, and the trigger conditions are designed according to the tracking error. Utilizing Lyapunov stability theory, the stability of the control system is demonstrated. Finally, simulations based on the benchmark simulation model 1 (BSM1) platform are conducted to verify the effectiveness of the ETSOFNN method. Note to Practitioners—In this paper, the aim is to develop an online tracking and control method for controlling dissolved oxygen (DO) concentration and nitrate nitrogen (NO) concentration in wastewater treatment process (WWTP) and to make it applicable to wastewater treatment plants under a wide range of operating conditions. Due to the dynamic and complex nature of WWTP, which involves multiple treatment units and control variables, as well as characteristics such as strong coupling, non-Gaussian characteristics, and significant nonlinearity, there are considerable challenges in accurately establishing mathematical models. Therefore, this paper investigates a dynamic event-triggered mechanism and utilizes a self-organizing fuzzy neural network (SOFNN) applied to the control method of WWTP. Firstly, a mathematical model of WWTP is established. Secondly, using the SOFNN based on correlation entropy compensation, the structure of the controller is automatically constructed, which enables more accurate adjustment depending on the actual working conditions to improve the robustness and adaptability of the controller. Next, a dynamic event-triggered strategy is applied in the controller to reduce the number of controller updates and thus reduce the communication cost. Finally, the results of simulation experiments show that this control method performs well, and the strategy will be extended to actual wastewater treatment plants in the future to optimize the operation results.

This paper introduces an event-triggered saturation-tolerant prescribed control (STPC) framework for rigid spacecraft subject to actuator faults and actuator saturation via a fixed-time disturbance observer (FTDO). An FTDO with time-varying observer gains is initially developed to reconstruct the lumped perturbations caused by external disturbances, parameter uncertainties, and actuator faults. A fixed-time auxiliary system is employed to counter the adverse effects of actuator saturation. Additionally, asymmetric prescribed performance and shift functions are skillfully incorporated to handle arbitrary bounded initial conditions. Subsequently, a novel FTDO-based event-triggered STPC strategy is formulated, ensuring that attitude-tracking errors converge to predefined performance bounds within a predetermined time while minimizing unnecessary control signal updates. The practical fixed-time stability of all closed-loop signals is validated, with the strict avoidance of Zeno behavior. Finally, simulation studies are conducted to verify the accuracy and effectiveness of the proposed approach.

To obtain the effective purification performance in wastewater treatment process (WWTP), the optimal control is an important method to guarantee the effluent quality reaching the standard and improve the treatment efficiency. The concentrations of dissolved oxygen (DO) and nitrate nitrogen (NO$_{\text{3}}$-N) are primary metrics that impact effluent quality, which is needed to be stably tracking controlled for achieving optimal performance in WWTP. Therefore, the double-layer fuzzy neural network (FNN)-based optimal control method with multivariable is proposed. First, considering the dynamic characteristic of WWTP, the FNN-based actor network is exploited to approximate the unknown dynamic information. Subsequently, the FNN-based critic network is integrated to minimize the cost function of DO and NO$_{\text{3}}$-N concentrations, which is composed of the control error and the control variable. Then, to guarantee the stability of the optimal controller, the Lyapunov function is constructed through backstepping method to analyze the control system performance. Finally, the optimality and effectiveness of the control system with multivariable are verified via the simulation experiments in benchmark simulation model 1.

To address the challenges posed by flight envelope limitation, external disturbances, model uncertainties and actuator failures in quadrotor unmanned aerial vehicles (UAVs), we propose an adaptive neural attitude control method that incorporates a Nussbaum function and nonlinear disturbance observer (NDO). By designing the Nussbaum function, we effectively address potential actuator failures while leveraging the NDO enables us to employ feedforward compensation strategy to mitigate perturbation effects. To handle the flight envelope limitation and model uncertainties, we introduce a nonlinear state-dependent function (NSDF) and neural networks (NNs), respectively. The NSDF is utilized to directly constrain the attitude, while the NNs are constructed to estimate the unknown components. Simulation results demonstrate that this approach successfully addresses the flight envelope limitation and maintains robust tracking performance even in the presence of external disturbances, model uncertainties and actuator failures in the controlled system.

Recently, the wastewater treatment process has become an effective tool to improve the ecological environment. Due to the inevitable actuator faults, the load capacity of the equipment and the purification efficiency of the wastewater are reduced. How to improve the purification and economic efficiency is a huge challenge. Therefore, a prescribed performance-based fault-tolerant optimal control method with multivariable is designed in this article. First, the error transformation function-based prescribed performance is introduced to achieve stable tracking control. Second, the fault approximation based adaptive control method is used to solve the problem of unavoidable actuator faults. Subsequently, the multivariable optimal controller is structured to guarantee the control accuracy and equipment energy consumption simultaneously. Finally, the steady state and transient performance are analyzed and the optimality is verified by some experiments.

The key focus in this paper is to develop an adaptive fuzzy function constraint control method for nonlinear systems with actuator and sensor faults. The effectiveness indicators of actuator and sensor are considered as the unknown functions with the bounded time-varying bias fault existing in actuator. Because system states are not applied to design controller, how to achieve constraints on the system states is a significant challenge. In order to cope with this challenge, the constraint target transformation is introduced to convert the constraints on real states to measurement variables. Then, the nonlinear statedependence mapping is used to ensure tracking error and system states remain within the given function constrain ranges. In contrast to the existing time-dependence constraint methods, this paper consider that the constraint boundaries are related not only time but also state variables, which extends the application fields for practical systems. Moreover, the deferred transformation function is employed to remove the restrictive condition that the initial values of system states must be within the constraint regions. Finally, the effectiveness of the developed method is confirmed by two simulation examples including the numerical example and wastewater treatment process (WWTP).

Wastewater treatment process (WWTP) is one of the most means to achieve the water resource protection and sustainable utilization. Dissolved oxygen and nitrate are main factors limiting effluent quality, which are involved with carbon consumption, nitrification and denitrification. To achieve efficacious control of dissolved oxygen and nitrate concentrations under actuator faults and saturation, a nonlinear mapping-based adaptive neural fault-tolerant control method is developed in this article for WWTP. The coordinate transformation is employed to boil down the constrained denitrification and aeration processes to unconstrained issues. To obtain the appointed steady-state tracking performance, nonlinear mapping is incorporated into the adaptive fault-tolerant controller, because of many categories of physical, chemical, and biological phenomena existing in associated and treatment zones, radial basis function neural networks are used to approximate the uncertain dynamics in WWTPs. The proposed control scheme is verified via the benchmark simulation model 1.

Various constraints commonly exist in most physical systems; however, traditional constraint control methods consider the constraint boundaries only relying on constant or time variable, which greatly restricts applying constraint control to practical systems. To avoid such conservatism, this study develops a new adaptive neural controller for the nonlinear strict-feedback systems subject to state-dependent constraint boundaries. The nonlinear state-dependent mapping is employed in each step of backstepping procedure, and the prescribed transient performance on tracking error and the constraints on system states are ensured without repeatedly verifying the feasibility conditions on virtual controllers. The radial basis function neural network (NN) with less parameters approach is introduced as an identifier to estimate the unknown system dynamics and reduce computation burden. For removing the effect of unknown control direction, the Nussbaum gain technique is integrated into controller design. Based on the Lyapunov analysis, the developed control strategy can ensure that all the closed-loop signals are bounded, and the constraints on full system states and tracking error are achieved. The simulation examples are used to illustrate the effectiveness of the developed control strategy.

Wastewater treatment process (WWTP), consisting of a class of physical, chemical, and biological phenomena, is an important means to reduce environmental pollution and improve recycling efficiency of water resources. Considering characteristics of the complexities, uncertainties, nonlinearities, and multitime delays in WWTPs, an adaptive neural controller is presented to achieve the satisfying control performance for WWTPs. With the advantages of radial basis function neural networks (RBF NNs), the unknown dynamics in WWTPs are identified. Based on the mechanistic analysis, the time-varying delayed models of the denitrification and aeration processes are established. Based on the established delayed models, the Lyapunov-Krasovskii functional (LKF) is used to compensate for the time-varying delays caused by the push-flow and recycle flow phenomenon. The barrier Lyapunov function (BLF) is used to ensure that the dissolved oxygen (DO) and nitrate concentrations are always kept within the specified ranges though the time-varying delays and disturbances exist. Using Lyapunov theorem, the stability of the closed-loop system is proven. Finally, the proposed control method is carried out on the benchmark simulation model 1 (BSM1) to verify the effectiveness and practicability.

For a category of nonlinear systems subject to time-varying constraints on tracking error and full states and unknown control directions, this article develops an adaptive neural control strategy using nonlinear mapping and double-layer constraint structure. Radial basis function neural network is applied to identify the unknown system dynamics. The dynamic surface control with less learning parameters is employed to eliminate “explosion of complexity” and reduce online computation burden. The Nussbaum gain technique is employed to deal with the unknown control direction. The nonlinear mapping is applied to ensure the satisfaction of the multiple constraints on state variables and remove feasibility conditions on virtual control signals. Double-layer constraint boundaries are incorporated into controller design process, the inside boundaries are utilized to cope with the multiple state constraints, and the outside boundaries are used into controller and adaptive law design. Hence, the singularity problem caused by the system state approaching the bound boundary is completely solved. The numerical example is used to deduce the availability of developed control approach.

In this article, an adaptive neural learning method is introduced for a category of nonlinear strict-feedback systems with time-varying full-state constraints. The two challenging problems of state constraints and learning capability are investigated and solved in a unified framework. To obtain the learning of unknown functions and satisfy full-state constraints, three main steps are considered. First, an adaptive dynamic surface controller (DSC) based on barrier Lyapunov functions (BLFs) is structured to implement that the closed-loop systems signals are bounded and full-state variables remain within the prescribed time-varying intervals. Moreover, the radial basis function neural networks (RBF NNs) are used to identify unknown functions. The output of the first-order filter, instead of virtual control derivatives, is used to simplify the complexity of the RBF NN input variables. Second, the state transformation is used to obtain a class of linear time-varying subsystems with small perturbations such that the recurrence of the RBF NN input variables and the partial persistent excitation condition are actualized. Therefore, the unknown functions can be accurately approximated, and the learned knowledge is kept as constant NN weights. Third, the obtained constant weights are borrowed into an adaptive learning scheme to achieve the batter control performance. Finally, simulation studies illustrate the advantage of the reported adaptive learning method on higher tracking accuracy, faster convergence rate, and lower computational expense by reusing learned knowledge.

In this article, the problem of tracking control for a class of nonlinear time-varying full state constrained systems is investigated. By constructing the time-varying asymmetric barrier Lyapunov function (BLF) and combining it with the backstepping algorithm, the intelligent controller and adaptive law are developed. Neural networks (NNs) are utilized to approximate the uncertain function. It is well known that in the past research of nonlinear systems with state constraints, the state constraint boundary is either a constant or a time-varying function. In this article, the constraint boundaries both related to state and time are investigated, which makes the design of control algorithm more complex and difficult. Furthermore, by employing the Lyapunov stability analysis, it is proven that all signals in the closed-loop system are bounded and the time-varying full state constraints are not violated. In the end, the effectiveness of the control algorithm is verified by numerical simulation.

This article presents the adaptive tracking control scheme of nonlinear multiagent systems under a directed graph and state constraints. In this article, the integral barrier Lyapunov functionals (iBLFs) are introduced to overcome the conservative limitation of the barrier Lyapunov function with error variables, relax the feasibility conditions, and simultaneously solve state constrained and coupling terms of the communication errors between agents. An adaptive distributed controller was designed based on iBLF and backstepping method, and iBLF was differentiated by means of the integral mean value theorem. At the same time, the properties of neural network are used to approximate the unknown terms, and the stability of the systems is proven by the Lyapunov stability theory. This scheme can not only ensure that the output of all the followers meets the output trajectory of the leader but also make the state variables not violate the constraint bounds, and all the closed-loop signals are bounded. Finally, the efficiency of the proposed controller is revealed.

For nonlinear full-state-constrained systems with unmeasured states, an adaptive output feedback control strategy is developed. The main challenge of this article is how to avoid that the unmeasured states exceed the constrained spaces. To achieve a good tracking performance for the considered systems, a stable state observer is structured to estimate unmeasured states which are not available in the control design. In addition, the constraints existing in most practical engineering are the source of reducing control performance and causing the system instability. The main limitation of current barrier Lyapunov functions is the feasibility conditions for intermediate controllers. The nonlinear mappings are used to achieve the satisfaction of full-state constraints directly and avoid feasibility conditions for intermediate controllers. By the Lyapunov theorem, the closed-loop system stability is proven. Simulation results are given to confirm the validity of the developed strategy.

In this paper, an adaptive neural network (NN) control scheme is developed for a class of stochastic nonlinear systems with time-varying full state constraints. In the controller design, RBF NNs are employed to approximate the unknown terms, and the backtracking technique is introduced to overcome the restriction of matching conditions. At the same time, tangent type time-varying barrier Lyapunov functions (tan-TVBLFs) are constructed to ensure the full state constraints are never violated, where tan-TVBLFs are beneficial to integrate constraint analysis into a common method. Furthermore, the Lyapunov stability theory is used to prove that all closed-loop signals are semiglobal uniformly ultimately bounded in probability and error signals remain in the compact set do not violate the time-varying constraints. A simulation example will be used to exhibit the effectiveness of the proposed control scheme.

In this article, the adaptive fault-tolerant control (FTC) problem is solved for a switched resistance-inductance-capacitance (RLC) circuit system. Due to the existence of faults which may lead to instability of subsystems, the innovation of this article is that the unstable subsystems are taken into account in the frame of output constraint and unmeasurable states. Obviously, there are not any unstable subsystems in unswitched systems. The unstable subsystems will involve many serious consequences and difficulties. Since the system states are unavailable, a switched state observer is designed. In addition, the fuzzy-logic systems (FLSs) are employed to approximate unknown internal dynamics in the controller design procedure. Then, the barrier Lyapunov function (BLF) is exploited to guarantee that the system output satisfy its constrained interval. Moreover, by using the average dwell-time method, all signals in the resulting systems are proofed to be bounded even when faults occur. Finally, the proposed strategy is carried out on the switched RLC circuit system to show the effectiveness and practicability.

In this paper, a novel adaptive fuzzy tracking control strategy is developed for nonlinear time-varying delayed systems with full state constraints. State constraints and time delays are normally found in various real-life plants, which are two important factors for degrading system performance significantly. In the framework of adaptive control, the effects of state constraints and time-varying delays are removed simultaneously. The integral Barrier Lyapunov functionals (IBLFs) are applied to achieve full-state-constraint satisfactions and remove the need of the transformed error constraints in previous BLFs. The unknown time-varying delays are completely compensated by introducing the separation technique and Lyapunov–Krasovskii functionals (LKFs). The unknown functions existing in systems are approximated by employing fuzzy logic systems (FLSs). With the help of less-adjustable parameters, only one parameter is needed to be adjusted online in each step of control design. The novel strategy can guarantee that a satisfactory tracking performance is achieved and the signals existing in the closed-loop system are bounded. Finally, by presenting simulation results, the efficiency of the proposed approach is revealed.

In the novel, an adaptive neural network (NN) controller is developed for a category of nonlinear stochastic systems with full state constraints and unknown time delays. The control quality and system stability suffer from the problems of state time delays and constraints which frequently arises in most real plants. The considered systems are transformed into new constrained free systems based on nonlinear mappings, such that full state constraints are never violated and the feasibility conditions on virtual controllers (the values of virtual controllers and its derivative are assumed to be known) are removed. To compensate for unknown time delayed uncertainties, the exponential type Lyapunov-Krasovskii functionals (LKFs) are employed. NNs are utilized to approximate unknown nonlinear functions appearing in the design procedure. In addition, by employing dynamic surface control (DSC) technique and less adjustable parameters, the online computation burden is lightened. The control method presented can achieve the semiglobal uniform ultimate boundedness of all the closed-loop system signals and the satisfactions of full state constraints by rigorous proof. Finally, by presenting simulation examples, the efficiency of the presented approach is revealed.

This paper addresses the problem of adaptive tracking control for a class of strict-feedback nonlinear state constrained systems with input delay. To alleviate the major challenges caused by the appearances of full state constraints and input delay, an appropriate barrier Lyapunov function and an opportune backstepping design are used to avoid the constraint violation, and the Pade approximation and an intermediate variable are employed to eliminate the effect of the input delay. Neural networks are employed to estimate unknown functions in the design procedure. It is proven that the closed-loop signals are semiglobal uniformly ultimately bounded, and the tracking error converges to a compact set of the origin, as well as the states remain within a bounded interval. The simulation studies are given to illustrate the effectiveness of the proposed control strategy in this paper.

This paper proposes an adaptive neural control method for a class of nonlinear time-varying delayed systems with time-varying full-state constraints. To address the problems of the time-varying full-state constraints and time-varying delays in a unified framework, an adaptive neural control method is investigated for the first time. The problems of time delay and constraint are the main factors of limiting the system performance severely and even cause system instability. The effect of unknown time-varying delays is eliminated by using appropriate Lyapunov-Krasovskii functionals. In addition, the constant constraint is the only special case of time-varying constraint which leads to more complex and difficult tasks. To guarantee the full state always within the time-varying constrained interval, the time-varying asymmetric barrier Lyapunov function is employed. Finally, two simulation examples are given to confirm the effectiveness of the presented control scheme.

In this paper, we design an adaptive neural network (NN) controller of uncertain n-joint robotic systems with time-varying state constraints. By proposing a nonlinear mapping, the robotic systems are transformed into the multiple-input, multiple-output systems. Compared with constant constraints, the time-varying state constraints are more general in the real systems. To overcome the design challenge, the time-varying barrier Lyapunov function is introduced to ensure that the states of the robotic systems are bounded within the predetermined time-varying range. The NN approximations are employed to approximate the uncertain parametric and unknown functions in the robotic systems. Based on the Lyapunov analysis, it can be proved that all signals of robotic systems are bounded; the tracking errors of system output converge on a small neighborhood of zero and the time-varying state constraints are never violated. Finally, a simulation example is performed to demonstrate the feasibility of the proposed approach.

In this paper, an adaptive output feedback control is framed for uncertain nonlinear discrete-time systems. The considered systems are a class of multi-input multioutput nonaffine nonlinear systems, and they are in the nested lower triangular form. Furthermore, the unknown dead-zone inputs are nonlinearly embedded into the systems. These properties of the systems will make it very difficult and challenging to construct a stable controller. By introducing a new diffeomorphism coordinate transformation, the controlled system is first transformed into a state-output model. By introducing a group of new variables, an input-output model is finally obtained. Based on the transformed model, the implicit function theorem is used to determine the existence of the ideal controllers and the approximators are employed to approximate the ideal controllers. By using the mean value theorem, the nonaffine functions of systems can become an affine structure but nonaffine terms still exist. The adaptation auxiliary terms are skillfully designed to cancel the effect of the dead-zone input. Based on the Lyapunov difference theorem, the boundedness of all the signals in the closed-loop system can be ensured and the tracking errors are kept in a bounded compact set. The effectiveness of the proposed technique is checked by a simulation study.

An adaptive control approach based on the fuzzy systems for a class of uncertain nonlinear multi-input multi-output (MIMO) systems is presented in this paper. This class of systems is in the nested multiple coupling structure and their states are constrained in the corresponding compact sets. The properties of the system structure are inevitable to bring about a complicated design and a difficult task. The fuzzy logic systems are employed to approximate the unknown functions of systems, and the decoupling backstepping way is proposed to design the stability controller and adaptation laws. Barrier Lyapunov functions (BLFs) are constructed in the backstepping design to guarantee that the constraint bounds are not violated. Based on Lyapunov analysis in barrier form, we can prove the stability of the closed-loop system. Two simulation examples are viewed to verify the feasibility of the approach.

This paper presents an adaptive neural control strategy for an n-link rigid robotic manipulator with both state constraints and unknown time-varying delayed states. The design difficulties cause by the state constraints and unknown network-induced time-varying delays which appear in the n-link rigid robot simultaneously. In order to overcome these difficulties, the novel Barrier Lyapunov functions and an iterative backstepping technique are employed to guarantee constraint satisfaction of the position of the robot, the opportune Lyapunov-Krasovskii functionals and separation techniques are utilized to eliminate the effect of unknown functions with time-varying delayed states in communication channels. As the universal approximator, the neural networks are used to estimate the unknown functions of systems. By using the Lyapunov analysis, we can achieve that all the closed-loop signals are semiglobal uniformly ultimately bound, the tracking errors converge to a small set about zero and the good tracking performances of the system output. The feasibility of the proposed control algorithm can be demonstrated by providing simulation results.

This paper deals with the tracking control problem for a class of nonlinear multiple input multiple output unknown time-varying delay systems with full state constraints. To overcome the challenges which cause by the appearances of the unknown time-varying delays and full-state constraints simultaneously in the systems, an adaptive control method is presented for such systems for the first time. The appropriate Lyapunov-Krasovskii functions and a separation technique are employed to eliminate the effect of unknown time-varying delays. The barrier Lyapunov functions are employed to prevent the violation of the full state constraints. The singular problems are dealt with by introducing the signal function. Finally, it is proven that the proposed method can both guarantee the good tracking performance of the systems output, all states are remained in the constrained interval and all the closed-loop signals are bounded in the design process based on choosing appropriate design parameters. The practicability of the proposed control technique is demonstrated by a simulation study in this paper.

In this paper, an adaptive neural tracking control strategy is presented to stabilize a class of uncertain nonlinear strict-feedback systems with the full state constraints and time-delays. Because the full state constraints and time-delays appear simultaneously in the systems, they lead to the difficulties in the controller design. The opportune barrier Lyapunov functions (BLFs) are designed to ensure that the states constraints are not violated. The novel backstepping procedures with BLFs are utilized to eliminate the effect of the nonlinear system which caused by the time-delays. Finally, it is proved that all the signals in the closed-loop system are semiglobal uniformly ultimately bounded and the tracking errors converge to a small interval based on proposed Lyapunov and backstepping design method. The effectiveness of the proposed scheme is demonstrated by a simulation in this paper.