Research Article | Open Access

Kenji Sawada, Seiichi Shin, "Numerical Optimization Design of Dynamic Quantizer via Matrix Uncertainty Approach", *Mathematical Problems in Engineering*, vol. 2013, Article ID 250683, 12 pages, 2013. https://doi.org/10.1155/2013/250683

# Numerical Optimization Design of Dynamic Quantizer via Matrix Uncertainty Approach

**Academic Editor:**Zidong Wang

#### Abstract

In networked control systems, continuous-valued signals are compressed to discrete-valued signals via quantizers and then transmitted/received through communication channels. Such quantization often degrades the control performance; a quantizer must be designed that minimizes the output difference between before and after the quantizer is inserted. In terms of the broadbandization and the robustness of the networked control systems, we consider the continuous-time quantizer design problem. In particular, this paper describes a numerical optimization method for a continuous-time dynamic quantizer considering the switching speed. Using a matrix uncertainty approach of sampled-data control, we clarify that both the temporal and spatial resolution constraints can be considered in analysis and synthesis, simultaneously. Finally, for the slow switching, we compare the proposed and the existing methods through numerical examples. From the examples, a new insight is presented for the two-step design of the existing continuous-time optimal quantizer.

#### 1. Introduction

With the rapid network technology development, the networked control systems (NCSs) have been widely studied [1–10]. One of the challenges in NCSs is quantized control. In NCSs, the continuous-valued signals are compressed and quantized to the discrete-valued signals via the quantizer of the communication channel, and such quantization often degrades the control performance. Hence, a desirable quantizer minimizes the performance error between before and after the quantizer insertion.

Motivated by this, researchers [11–14] have provided optimal dynamic quantizers for the following problem formulation in the discrete-time domain. For a given plant , synthesize a “dynamic” quantizer such that the system composed of and in Figure 1(a) “optimally” approximates the plant in Figure 1(b) in the sense of the input-output relation. The obtained quantizer allows us to design various controllers for the plant on the basis of the conventional control theories. Also, this framework is helpful in not only the NCS problem but also various control problems such as hybrid control, embedded system control, and on-off actuator control.

**(a) Discrete-valued input system**

**(b) Usual system**

When we consider controlling a mechanical system with an on-off actuator, first the controlled object and its uncertainties are usually modeled in the continuous-time domain. Second, the model and its uncertainties are discretized to apply the above dynamic quantizer. However, the discretization sometimes results in uncertainties more complicated than those in the original model and creates undesirable complexity in robust control. The continuous-time setting quantizer is more suitable for the robust control of the quantized system than discrete-time one. Thus, our previous works [15, 16] have considered the continuous-time setting, while a number of the discrete-time settings have been studied by others [11–14]. In these works, it is assumed that the switching process of discretizing the continuous-valued signal is sufficiently quick relative to the control frequency and only the spatial determination (quantized accuracy) is considered as the quantization effect. This is because the switching speed of the continuous-time delta-sigma modulator for wireless broadband network systems is from 1 MHz to 100 MHz [17, 18].

On the other hand, the above assumption is essentially weak in the case of the slow switching such as the mechanical systems with on-off actuators [19]. For the slow switching, we need to consider the quantization effect on both the switching speed and the spatial constraints in continuous time. For example, Ishikawa et al. proposed a two-step design of a feedback modulator [20]: (i) the control performance of the modulator is considered under only the spatial constraint, and (ii) the modulator is tuned in terms of the switching speed constraint. However, the structure of the modulator is more restricted than that of the dynamic quantizer and the obtained modulator is not always optimal. Therefore, the dynamic quantizer under temporal resolution (switching speed) and spatial resolution constraints has still to be optimally designed. The simultaneous consideration of the two constraints is the particular challenge we address in this paper.

We propose a numerical optimization method for the continuous-time dynamic quantizer under switching speed and quantized accuracy constraints. To achieve the method, this paper solves the design problem via sampled-data control framework that has so far provided various results for networked control problems [7–9]. We refer to the previous work on optimal dynamic quantizer design [11, 12] and consider the basic feedforward system in Figure 1(a). In addition to the invariant set analysis [21, 22] similarly to our previous works [13, 15, 16], this paper utilizes a matrix uncertainty approach [23, 24] that is proposed in a sampled-data control framework. Although the obtained results can be more conservative than those in the previous works on continuous-time dynamic quantizer [15, 16] from the viewpoint of the class of the exogenous input and the applicable plants, both temporal and spatial resolution constraints can be addressed in analysis and synthesis, simultaneously. For the fast switching case, the proposed conditions converge to the corresponding conditions of our previous works. Finally, for the slow switching, we compare the proposed and existing methods [15, 16] through numerical examples. In particular, a new insight is presented for the two-step design of the existing continuous-time optimal quantizer.

*Notation*. The set of (positive) real matrices is denoted by (). The set of (positive) integer matrices is denoted by (). We denote by the set of piecewise-continuous functions of -dimensional finite vectors such that -norm of its functions is finite. and (or for simplicity of notation, and ) denote the zero matrix and the identity matrix, respectively. For a matrix , , and denote its transpose, its spectrum radius, and its maximum singular value, respectively. For a vector , is the th entry of . For a symmetric matrix , means that is positive (semi) definite. For a vector and a sequence of vectors , and denote their -norms, respectively. Finally, we use the “packed” notation .

#### 2. Problem Formulation

Consider the discrete-valued input system in Figure 1(a), which consists of the linear time invariant (LTI) continuous-time plant and the quantizer . The system is given by where , , , and denote the state vector, the measured output, the exogenous input, and the quantizer output, respectively. The continuous-valued signal is quantized into the discrete-valued signal via the quantizer . We assume that the matrix is Hurwitz; that is, the usual system in Figure 1(b) is stable in the continuous-time domain. The initial state is given as .

For the system , consider the continuous-time dynamic quantizer with the state vector as shown in Figure 2. Its switching speed (or its temporal resolution) is determined by the operator , which converts the continuous-time signal into the low temporal resolution signal as follows: That is, . is the ideal sampler with the sampling period and is the zero-order hold operator. The spatial resolution of the quantizer is expressed by the static quantizer with the quantization interval , that is, and the continuous-time LTI filter is given by Note that is of the nearest-neighbor type toward such as the midtread quantizer in Figure 3 ( where and are the th row of and ) and the initial state is given by for the drift free of [11, 12].

*Remark 1. *In synthesis, our previous works [15, 16] ignored the operator . In implementation, however, the continuous-time quantizer needs the switching process discretizing the continuous-valued signal. Of course, the applicable interval of switching depends on controlled objects such as narrowband or broadband networked systems and mechanical systems with on-off actuators. Therefore, it is important to consider the operator in synthesis.

For the system in Figure 1(a) with the initial state and the exogenous input , denotes the output of at the time . Also, for the system in Figure 1(b) without , denotes its output at the time . Consider the following cost function: If the quantizer minimizes , the system “optimally” approximates the usual system in the sense of the input-output relation. In this case, we can use the existing continuous-time controller design methods for the system in Figure 1(b) without considering the quantization effect. When the controlled object and its uncertainties are modeled in the continuous-time domain, therefore, the continuous-time quantizer can introduce robust control of the continuous-time setting directly, while the discrete-time quantizer requires discretization of the whole control system. Our previous works [15, 16] proposed an optimal dynamic quantizer for the cost function for the fast switching case . That is, only the spatial deterioration has been considered.

On the other hand, the simultaneous consideration of the temporal and spatial resolution constraints is the problem we address in this paper. To consider the temporal resolution constraint caused by the operator , this paper modifies the cost function as follows: Fixing ignores the output error between before and after the quantizer is inserted over the th sampling interval and leads to the cost function setting that is utilized for the discrete-time optimal dynamic quantizers [11–14]. Therefore, the optimal quantizer for minimizes the output error between the systems in Figures 1(a) and 1(b) in terms of the input-output relation under the temporal and spatial resolution constraints.

Motivated by the above, our objective is to solve the following continuous-time dynamic quantizer synthesis problem **(E)**: *for the system ** composed of ** and ** with the initial state ** and the exogenous input **, suppose that the quantization interval **, the switching speed *, *and the performance level ** are given. Characterize a continuous-time dynamic quantizer * i.e.,* find parameters ** achieving *.

This paper proposes continuous-time quantizers in terms of solving the problem **(E)** on the basis of invariant set analysis and the sampled-data control technique, while other researchers [11, 12, 14] have proposed the discrete-time optimal ones.

*Remark 2. *The cost function setting of this paper is more complicated than the existing continuous-time and discrete-time cases [11–16], so this paper considers the basic feedforward system composed of and similar to the previous works on optimal dynamic quantizer design [11, 12].

*Remark 3. *The plant is restricted to be stable because of the feedforward structure, while the existing results can address unstable plants. To remove this restriction, we need to consider a feedback system structure similar to existing ones [13–16]. This is our future task.

#### 3. Main Result

##### 3.1. System Expression

In this subsection, we consider the system expression for the quantizer analysis. Define the quantization error as From the properties of the quantizer and the operator , holds where . Then, one obtains where and for , . In this case, by using the sampled-data control technique, the following lemma holds.

Lemma 4. *Denote by the state vector of the usual system in Figure 1(b) and define the signals as follows:
**
For the cost function , the difference between and for , is given by the following system:
**
where , ; the matrices , , , and are defined as follows:
*

*Proof. *See Appendix A.

We focus on of . For the operator and the signal ,
holds. This implies that we cannot ignore the temporal resolution constraint on the cost function even if . On the other hand, low-pass prefiltering rectifies this situation [25]. In fact, for the stable LTI system ,
holds. For the evaluation of the cost function , then this paper utilizes
as the exogenous input. Note that if stable is strictly proper and . For the signal (15), holds, so the terms of in (11) are eliminated. Then, is rewritten as
Also, this paper solves the following synthesis problem **(****)**: *for the system* composed of and *with the initial state* *and the exogenous input* *in* (15), *suppose that the quantization interval* , the *switching speed* , *and the performance level* *are given*. *Characterize a continuous-time dynamic quantizer* (i.e., *find parameters* ) *achieving* .

##### 3.2. Quantizer Analysis

The quantization error of (16) is bounded as mentioned earlier. The reachable set and the invariant set characterize such a system with bounded input. Consider the LTI discrete-time system given by where and denote the state vector and disturbance input, respectively. We define the reachable set and the invariant set.

*Definition 5. *Define the reachable set of the system (17) to be a set which satisfies

*Definition 6. *Define the invariant set of the system (17) to be a set which satisfies

The analysis condition can be expressed in terms of matrix inequalities as summarized in the following proposition [22].

Proposition 7. *Consider the system (17). For a matrix , the ellipsoid is an invariant set if and only if there exists a scalar satisfying
*

Note that the ellipsoidal set covers the reachable set from outside. Define the set and rewrite the system (16) as where , , , and . The left multiplication of with leads to . The relation clearly holds because and the set is an independent bounded disturbance without the relation (7). That is, the reachable set of with is no larger than that of with the disturbance .

Then, this paper utilizes the reachable set to estimate the influences of the quantization error and the invariant set to characterize the cost function by substituting and into (20). Move on to the matrix exponential of and in (21), which is rewritten as Along with this, of (21) is also rewritten as In addition, from the properties of and , holds. Similarly to our previous papers [13, 15, 16], by using the control technique in [21], we provide the sufficient conditions for computing and of (24) as follows:

*Remark 8. *For the inequalities (25) and any vectors and , we have
and . Then, we see that (24) holds if and .

The inequalities (25) are difficult to test since we need to find , , and satisfying (20) and (25) for infinitely many values of . Then, using the matrix uncertainty technique [23, 24], we consider their sufficient conditions, which are easy to compute. Considering in (22) as a matrix uncertainty, we introduce the following lemma regarding the matrix exponential [26, 27].

Lemma 9. *For the matrix in (22),
**
holds where
*

*Proof. *Since (see [26]),
holds.

By using Lemma 9 and the -procedure [23, 28, 29], the sufficient condition analyzing the cost function of the system can be expressed in terms of matrix inequality as summarized in the following theorem.

Theorem 10. *Consider the system composed of and with the initial state and the exogenous input in (15). For the quantization interval and the switching speed , the upper bound of the cost function is given by
**
if there exist , , and satisfying
**
where the matrices and are defined by
*

*Proof. *See Appendix A.

Denote by the system without operator . In this case, the system is given by where Regarding the definition of , see Lemma 4. An advantage the condition (31) over conditions (20) is that it can be used for a small without numerical difficulty. This idea comes from [23, 24]. In the limit of , and hold, so and hold. In the same limit, from , conditions (31) and (32) converge to the analysis conditions of the continuous-time dynamic quantizer for the system in [15, 16]. On the other hand, for a small , , , and hold, so and ( and ) are close to identity and zero matrices, respectively, and the left side of (20) is close to zero.

In numerical computation, it is appropriate to fix the structure of such that holds. For example, we can set , and this setting leads to the following optimization problem **(Aop)**:When scalar is fixed, the conditions in Theorem 10 are linear matrix inequalities (LMIs) in terms of the other variables. Using standard LMI software and the line search of , we can obtain an upper bound of .

##### 3.3. Quantizer Synthesis

The problem **(Aop)** suggests that the quantizer synthesis problem is reduced to the following nonconvex optimization problem **(OP)**:
That is, if **(OP)** is feasible, **(****)** is feasible.

From the matrix product such as and in (31), the synthesis condition is difficult to derive from Theorem 10 unlike the continuous-time case without the operator in [15, 16]. Thus, we fixed the parameters as follows: The structure (38) does not severely limit the synthesis because and of the continuous-time dynamic quantizer for the system in [15, 16] are also (38). See Appendix B. In other words, estimates the quantization influence on the system . Along with this, we fix of (31) as follows: The structure (39) also does not impose a severe limitation on the synthesis because an appropriate choice of the quantizer state coordinates allows us to assume that has the special structure for the full order case [30].

Under some circumstances (38) and (39), we obtain the following synthesis condition.

Theorem 11. *Consider the system composed of and with the initial state and the exogenous input in (15). Suppose that the quantization interval , the switching speed , and the performance level are given. For a scalar , there exist a continuous-time dynamic quantizer achieving (30) if one of the following equivalent statements holds.*(i)*There exist matrices , and a dynamic quantizer satisfying (31), (32), and (38).*(ii)*There exist matrices , , , satisfying
**where
**
In this case, such a quantizer parameter is given by
*

*Proof. *We fix as shown in (39) and introduce the change of variables . Hence, (31) and (32) result in (40) and (41). Also, designing yields because is determined by and .

In the limit of ; converges to and converges to ; then conditions (40) and (41) also converge to the synthesis condition of the continuous-time dynamic quantizer for the system in (34). Also, by setting for Theorem 11, the quantizer synthesis problem **(****)** is reduced to the following optimization problem **(Sop)**:If **(Sop)** is feasible, **(****)** is feasible. Therefore, a continuous-time dynamic quantizer considering both spatial and temporal resolution constraints is obtained from Theorem 11.

*Remark 12. *To consider numerical optimization analysis or synthesis of a quantizer as shown in **(Aop)** and **(Sop)**, we need the signal assumption (15) in Theorems 10 and 11. On the other hand, for the high speed switching such that is very small, the assumption (15) ensures that solutions to the problem **(****)** converge to our previous results [15, 16]. Therefore, the results of this paper partly include our previous results [15, 16] although each class of exogenous signals and plants is restricted.

#### 4. Discussion

For the slow switching, we compare the proposed method and existing continuous-time quantizer [15, 16]. Consider the system . The plant is the stable minimum phase LTI system:
In the case without the operator , an optimal form of the continuous-time quantizer [15, 16] is given by . See Appendix B. The continuous-time quantizer and its performance are parameterized by the free parameter . For the simulation, we consider a two-step design for ; we first set and second insert the operator in the obtained . Also, the achievable performance of is calculated by **(Aop)**.

For the comparison, we set the switching speed [s] and the quantization interval . First, we set and then obtain with . Also, for is obtained from **(Aop)**. Second, we solve the problem **(Sop)** and obtain and the matrix . In this case, both quantizers can approximate well. Figures 4 and 5 illustrate the simulation results of the time responses of with the proposed quantizer and the quantizers in . The initial state and the input are given. In Figures 4 and 5, the thin lines and the thick lines are for the conventional system in Figure 1(b) and the system in Figure 1(a), respectively. We see that the controlled outputs of the discrete-valued input systems with the dynamic quantizers approximate those of the usual systems even if the quantized outputs are applied. Also, the two controlled outputs approximated by both quantizers are exactly the same.

**(a)**

**(b)**

**(a)**

**(b)**

Next, we consider the case . In this case, the two controlled outputs approximated by the two quantizers differ. **(Aop)** for is infeasible. From **(Sop)**, on the other hand, we obtain and the matrix . Figures 6 and 7 illustrate the simulation results on the time responses of with and the proposed quantizer in the same fashion. We see that of the usual plant is approximated by of the system with the proposed quantizer, while of the system with diverges. From this example, we see that the proposed method can address the spatial resolution and the temporal resolution issues, simultaneously. Also, Theorem 10 verifies whether the quantizer is applicable to the given switching speed setting.

**(a)**

**(b)**

**(a)**

**(b)**

*Remark 13. *In the above numerical experiments, the proposed quantizer is designed and the quantizer is analyzed for , while the time responses of the quantizers are simulated for . That is, this is the conservativeness caused by the signal assumption (15). However, we see that the above results verify the effectiveness of the proposed method even if the signal conservativeness exists.

Here, we focus on the eigenvalues of for the system with . The eigenvalues for and are and then is unstable in the discrete-time domain. From Theorem 10, **(Aop)** is infeasible if is bigger than 1 (in other words, is unstable). are the eigenvalues of . That is, for is unstable. Then, we consider the case in which and () such that is stable. The corresponding eigenvalues are . In this case, for is obtained from **(Aop)**.

From the above results, the existing continuous-quantizer in [15, 16] may be suitable for a two-step design such that is stable via the parameter . In terms of the upper bound of cost function , first, let us consider the problem **(P-1)**: maximize for such that
where the parameters of are given by . This problem is LMI for the line search of . For , its solution is (). However, for is obtained from **(Aop)**. By using Theorem 10, next, let us consider the problem **(P-2)**:
where the parameters of and are given by . This problem is LMI for the plane search of and . For , its solution is () and then for is obtained. This performance is about the same as that of the proposed quantizer. Therefore, we see that Theorem 10 is also helpful for the two-step design of the existing continuous-time quantizer [15, 16] even if the tractable optimization method instead of the plane search remains an issue for future work. Such a method correlates the parameter with the switching speed , so its insight is expected not only to result in a new two-step design but also to clarify the relationship between the discrete-time and continuous-time dynamic quantizers. Of course, important future topics also include considering the quantized feedback control system with unstable plants and generalizing the exogenous signal for the evaluation of the cost function.

#### 5. Conclusion

Focusing on the broadbandization and the robustness of the networked control systems, this paper has dealt with the continuous-time quantized control. We have proposed numerical optimization methods analyzing and synthesizing the continuous-time dynamic quantizer on the basis of the invariant set analysis and the sampled-data control technique. The contributions of the proposed method can be summarized as follows.(i)Both the temporal and spatial resolution constraints can be simultaneously considered, whereas Ishikawa et al. [20] considered the two constraints step-by-step and we [15, 16] previously ignored the temporal constraint in synthesis. As a result, the proposed method is applicable to both the slow and fast switching cases. (ii)The maximum output difference for each sampling interval is proven to be evaluated numerically via the matrix uncertainty approach, while the existing results [11–14] evaluate that only for each sampling instance. (iii)The analysis and synthesis conditions are given in terms of BMIs. However, the quantizer analysis and synthesis problems are reduced to tractable optimization problems. (iv)The new insight is presented for the existing continuous-time quantizer design [15, 16]. Also, this paper has clarified the following areas for future work. (i)Because of the feedforward structure, the plant is restricted to be stable. To address unstable systems, we need to propose design methods for feedback control systems. (ii)The sensors and actuators are distributed in the networked control system [31], so it is necessary to design multiple (decentralized) quantizers rather than a centralized quantizer similar to the existing ones [14, 16]. (iii)The class of exogenous signals evaluating the cost function is restricted. To avoid this conservativeness, it is necessary to propose the equivalent discrete-time expression instead of (11) using adjoint operator similar to sampled-data control [25, 32]. (iv)For networked control applications, it is important to consider the time-varying sampling period, time delay, packet loss, and so on similar to [6–10]. For example, the works [6, 8] using the LMI technique address time-varying sampling period and time delay, so it is expected that our method using the LMI technique also extends to such problems.

#### Appendix

#### A. Proof

The proof of Lemma 4 is as follows.

*Proof. * for , (which is the behavior of over the th sampling interval) is given by the discretized system of :
where . is given by (9) and is given by the discretized system of :
where . This is because is given by
Then, for , is expressed by the discretized system of :
Also, for , without the quantizer is given by
where and . From and , we obtain the system in (11).

For the proof of Theorem 10, we use the -procedure [23, 28, 29].

Lemma A.1. *For the real matrices , , and of appropriate size, the inequality
**
holds for any matrix such that if and only if there exists a matrix such that
*

Then, the proof of Theorem 10 is as follows.

*Proof. *We use Lemmas 9 and A.1 with
In this case, for the inequalities (25), we obtain their sufficient conditions as follows:
Then, the upper bound of is given by (30). By substituting and into (20), we obtain
By Schur complement [29], (A.10) is equivalent to (31) where and . Also, (A.9) is equivalent to (32) where .

#### B. Continuous-Time Dynamic Quantizer [15, 16]

For the system in (34) without the operator , we consider the following non-convex optimization :The dynamic quantizer without the operator is obtained from and is achieved. Also, an optimal form of the continuous-time quantizer [15, 16] is given by where its achievable upperbound of is characterized by where . The continuous-time quantizer and its performance are parameterized by the free parameter . Note that the larger values of not only provide the better approximation performance, but also switch the outputs , more quickly. In other words, the quantizer from results in the switching that is too fast and is sometimes not applicable to the slow switching case.

#### Acknowledgments

The authors would like to thank the reviewers for their valuable comments. This work was partly supported by Grant-in-Aid for Young Scientists (B) no. 24760332 from the Ministry of Education, Culture, Sports, Science and Technology of Japan.

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Copyright © 2013 Kenji Sawada and Seiichi Shin. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.